id: https://api.badgr.io/public/badges/1904UOIdTHGECr5XfiuosQ
name: Six Sigma Green Belt (SSGB)
image:
  id: https://api.badgr.io/public/badges/1904UOIdTHGECr5XfiuosQ/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Six Sigma Green Belt certification program offered by Arizona State University. The recipient has the ability to use the Define, Measure, Analyze, Improve and Control (DMAIC) methodology; a customer-focused, fact and data-driven approach that guides continuous improvement and operational excellence. The holder of this digital credential is trained on a series of statistical and standard improvement tools to develop solutions to existing problems, processes, services, and products. In addition, a certified Six Sigma Green Belt has successfully completed an implementation project reviewed and supervised by a Master Black Belt. A Six Sigma Green Belt can improve processes within their specific work area and serve as a part-time member of a team contributing to a larger scale Black Belt or Master Black Belt project. A Six Sigma Green Belt is the stepping stone to a Black Belt certification.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: continuous improvement
tags: process improvement
tags: six sigma
tags: quality assurance
tags: quality improvement
tags: quality control
alignment:
  target name: Business Process Mapping
  target url: https://skills.emsidata.com/skills/KS1218X78G2S5H470TJX
  target framework: EMSI
  target code: KS1218X78G2S5H470TJX
alignment:
  target name: Data Collection
  target url: https://skills.emsidata.com/skills/KS122NR6NY1KB0TD40G4
  target framework: EMSI
  target code: KS122NR6NY1KB0TD40G4
alignment:
  target name: Define Measure Analyze Improve And Control (DMAIC)
  target url: https://skills.emsidata.com/skills/KS1232N75YHBKF78PT7S
  target framework: EMSI
  target code: KS1232N75YHBKF78PT7S
alignment:
  target name: Experimental Design
  target url: https://skills.emsidata.com/skills/KS122VN73RX0QR6GMDHV
  target framework: EMSI
  target code: KS122VN73RX0QR6GMDHV
alignment:
  target name: Problem Solving
  target url: https://skills.emsidata.com/skills/KS125F678LV2KB3Z5XW0
  target framework: EMSI
  target code: KS125F678LV2KB3Z5XW0
alignment:
  target name: Six Sigma Green Belt Certification
  target url: https://skills.emsidata.com/skills/KS124HM6ZWL986Y3QK0L
  target framework: EMSI
  target code: KS124HM6ZWL986Y3QK0L
alignment:
  target name: Statistical Process Controls
  target url: https://skills.emsidata.com/skills/KS440TB6786R7HHQ2Z7D
  target framework: EMSI
  target code: KS440TB6786R7HHQ2Z7D
alignment:
  target name: Value Stream Mapping
  target url: https://skills.emsidata.com/skills/KS441QN78S11TRN1F178
  target framework: EMSI
  target code: KS441QN78S11TRN1F178
alignment:
  target name: Business Process Mapping
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-54850
alignment:
  target name: Data collection
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-37121
alignment:
  target name: Define Measure Analyze Improve Control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-25111
alignment:
  target name: Experimental design
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-19508
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
alignment:
  target name: Six Sigma Green Belt
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-30593
alignment:
  target name: Statistical process control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-16661
alignment:
  target name: Value stream mapping
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31432
criteria:
  id: https://asuengineeringonline.com/professional-programs/six-sigma-green-belt-certification
  narrative: A participant needs to review and understand the associated Six Sigma Green Belt curriculum, in addition to successfully passing the topic quizzes and executing an applied project to be reviewed and approved by a Master Black Belt and program facilitator.
type: BadgeClass

id: https://api.badgr.io/public/badges/PxHMVX9OQYuF2hTdSgtogA
name: FSE Test Credential (Name)
image:
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description: An example credential. This is the description field when creating the badge.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: engineering
tags: computer science
alignment:
  target name: Artificial Intelligence
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  target framework: EMSI
  target code: KS120BV6SR75RBKQH0G3
alignment:
  target name: Machine Learning
  target url: https://skills.emsidata.com/skills/KS1261Z68KSKR1X31KS3
  target framework: EMSI
  target code: KS1261Z68KSKR1X31KS3
alignment:
  target name: TensorFlow
  target url: https://skills.emsidata.com/skills/KS2GHRCYA6TRT29F1HOO
  target framework: EMSI
  target code: KS2GHRCYA6TRT29F1HOO
alignment:
  target name: Artificial intelligence
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-19982
alignment:
  target name: Machine learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-30919
alignment:
  target name: Tensorflow
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-34624
criteria:
  id: http://asuengineeringonline.com/
  narrative: This is the description of the earning criteria section of the badge.
type: BadgeClass

id: https://api.badgr.io/public/badges/0yooWwx9RUyMF3oo56mKzQ
name: Lean Six Sigma Yellow Belt (LSSYB)
image:
  id: https://api.badgr.io/public/badges/0yooWwx9RUyMF3oo56mKzQ/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Lean Six Sigma Yellow Belt certification program offered by Arizona State University. The holder of this digital credential has the ability to use process maps to model relationships and optimize process flows; apply quality tools to understand processes and make data-driven decisions; use Lean concepts to remove waste from a process; utilize root cause analysis to identify the source of faults or problems; apply basic control methods to monitor stability and process performance. A Lean Six Sigma Yellow Belt, is skilled at the foundational concepts of Lean Six Sigma and equipped with tools to make improvements to the processes on which they work. These practitioners are often responsible for identifying and defining challenges to determine the cause, and for developing solutions to close performance gaps, and implement solutions. A Lean Six Sigma Yellow Belt is the stepping stone to a Lean Six Sigma Green Belt certification.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: lean
tags: six sigma
tags: lean six sigma
tags: process improvement
tags: continuous improvement
tags: lean thinking
alignment:
  target name: Brainstorming
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  target framework: EMSI
  target code: KS1215X736FGJZK6JKST
alignment:
  target name: Business Process Mapping
  target url: https://skills.emsidata.com/skills/KS1218X78G2S5H470TJX
  target framework: EMSI
  target code: KS1218X78G2S5H470TJX
alignment:
  target name: Define Measure Analyze Improve And Control (DMAIC)
  target url: https://skills.emsidata.com/skills/KS1232N75YHBKF78PT7S
  target framework: EMSI
  target code: KS1232N75YHBKF78PT7S
alignment:
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  target framework: EMSI
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alignment:
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  target url: https://skills.emsidata.com/skills/ES5BA78322FB1F916BF2
  target framework: EMSI
  target code: ES5BA78322FB1F916BF2
alignment:
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  target url: https://skills.emsidata.com/skills/KS127KL757Y8ZF12JSD1
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  target code: KS127KL757Y8ZF12JSD1
alignment:
  target name: PDCA Cycle
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  target framework: EMSI
  target code: KS127MX6S0K86B13J69C
alignment:
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  target url: https://skills.emsidata.com/skills/KS125F678LV2KB3Z5XW0
  target framework: EMSI
  target code: KS125F678LV2KB3Z5XW0
alignment:
  target name: Root Cause Analysis
  target url: https://skills.emsidata.com/skills/KS7G2RS68WTQ4B3H2KMQ
  target framework: EMSI
  target code: KS7G2RS68WTQ4B3H2KMQ
alignment:
  target name: Brainstorming
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31187
alignment:
  target name: Business Process Mapping
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-54850
alignment:
  target name: Define Measure Analyze Improve Control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-25111
alignment:
  target name: Lean Six Sigma
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-27031
alignment:
  target name: Lean Six Sigma Yellow Belt
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-54546
alignment:
  target name: Pareto Principle
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-49649
alignment:
  target name: PDCA Cycle
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-60076
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
alignment:
  target name: Root Cause Analysis
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-18808
criteria:
  id: https://asuengineeringonline.com/professional-programs/lean-six-sigma-yellow-certificate
  narrative: A participant needs to review and understand the associated Lean Six Sigma Yellow Belt lectures and curriculum, completing the active learning activities in addition to passing completion of knowledge checks and a final exam with a score of 70% or higher.
type: BadgeClass

id: https://api.badgr.io/public/badges/lKWD6fnIT8OdLkFUyvLgNA
name: Lean Six Sigma Green Belt (LSSGB)
image:
  id: https://api.badgr.io/public/badges/lKWD6fnIT8OdLkFUyvLgNA/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Lean Six Sigma Green Belt certification program offered by Arizona State University. The recipient has the ability to use the Define, Measure, Analyze, Improve and Control (DMAIC) methodology; a customer-focused, fact and data-driven approach that guides continuous improvement and operational excellence. The holder of this digital credential is trained on a series of statistical and standard improvement tools to develop solutions to existing problems, processes, services and products across multiple industries and domains. A Lean Six Sigma Green Belt is a trained continuous improvement professional taught to measure process factors and provide complete process analyses to implement sound improvement solutions. A Lean Six Sigma Green Belt is the stepping stone to a Black Belt certification.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: lean thinking
tags: continuous improvement
tags: process improvement
tags: lean management
tags: lean leadership
tags: lean
tags: six sigma
tags: lean six sigma
tags: supply chain management
alignment:
  target name: Define Measure Analyze Improve And Control (DMAIC)
  target url: https://lightcast.io/open-skills/skills/KS1232N75YHBKF78PT7S
  target framework: EMSI
  target code: KS1232N75YHBKF78PT7S
alignment:
  target name: Operational Excellence
  target url: https://lightcast.io/open-skills/skills/KS127CR6S5TQJVYMG3HB
  target framework: EMSI
  target code: KS127CR6S5TQJVYMG3HB
alignment:
  target name: Problem Solving
  target url: https://lightcast.io/open-skills/skills/KS125F678LV2KB3Z5XW0
  target framework: EMSI
  target code: KS125F678LV2KB3Z5XW0
alignment:
  target name: Complex Problem Solving
  target url: https://lightcast.io/open-skills/skills/ES80E93EF8629D5C793C
  target framework: EMSI
  target code: ES80E93EF8629D5C793C
alignment:
  target name: Creative Problem-Solving
  target url: https://lightcast.io/open-skills/skills/KS122HN63RD7JX5WNCJ8
  target framework: EMSI
  target code: KS122HN63RD7JX5WNCJ8
alignment:
  target name: Project Management
  target url: https://lightcast.io/open-skills/skills/KS1267F6MSPN366LX7ST
  target framework: EMSI
  target code: KS1267F6MSPN366LX7ST
alignment:
  target name: Statistical Process Controls
  target url: https://lightcast.io/open-skills/skills/KS440TB6786R7HHQ2Z7D
  target framework: EMSI
  target code: KS440TB6786R7HHQ2Z7D
alignment:
  target name: Value Stream Mapping
  target url: https://lightcast.io/open-skills/skills/KS441QN78S11TRN1F178
  target framework: EMSI
  target code: KS441QN78S11TRN1F178
alignment:
  target name: Define Measure Analyze Improve Control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-25111
alignment:
  target name: CBT Certified Operational Excellence
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-25705
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
alignment:
  target name: Project management
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26898
alignment:
  target name: Statistical process control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-16661
alignment:
  target name: Value stream mapping
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31432
criteria:
  id: https://asuengineeringonline.com/professional-programs/lean-six-sigma-green-belt-certification
  narrative: To earn this badge, the learner demonstrated the ability to define process problems within a real-world context, identify opportunities for improvement, and apply the DMAIC framework to guide their approach. They showed skill in mapping and analyzing workflows to uncover sources of inefficiency and variation, using data to diagnose root causes and inform decision-making. The learner applied relevant Lean and Six Sigma tools to develop, test, and implement solutions, and demonstrated their ability to sustain improvements through appropriate control strategies. Throughout the experience, they communicated findings and recommendations to stakeholders, showing the capacity to apply structured problem-solving methods in a professional environment.
type: BadgeClass

id: https://api.badgr.io/public/badges/wulgJIxjRm-6lRI5QOea9Q
name: Six Sigma Black Belt (SSBB)
image:
  id: https://api.badgr.io/public/badges/wulgJIxjRm-6lRI5QOea9Q/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Six Sigma Black Belt certification program offered by Arizona State University. The recipient has a deep understanding of the Six Sigma concepts and the “process improvement triad”. The holder of this digital credential can apply the DMAIC methodology, design for six sigma and lean product development concepts to product/process design projects. In addition, a certified Six Sigma Black Belt uses appropriate analytical, statistical, and lean methods, and a fundamental understanding on how to plan, design and conduct experiments efficiently and effectively. Six Sigma Black Belts analyze the resulting data to obtain objective conclusions. A Six Sigma Black Belt is a full-time, highly trained continuous improvement professional often responsible for leading problem-solving projects, conducting Six Sigma and other continuous improvement training, and coaching project teams. A Six Sigma Black Belt is the stepping stone to a Master Black Belt certification.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: project management
tags: quality improvement
tags: strategy
tags: management
tags: quality assurance
tags: advanced quality control
tags: six sigma
tags: process improvement
tags: continuous improvement
alignment:
  target name: Complex Problem Solving
  target url: https://skills.emsidata.com/skills/ES80E93EF8629D5C793C
  target framework: EMSI
  target code: ES80E93EF8629D5C793C
alignment:
  target name: Control Chart
  target url: https://skills.emsidata.com/skills/KS122CN61BGR14CT2VZF
  target framework: EMSI
  target code: KS122CN61BGR14CT2VZF
alignment:
  target name: Data Analysis
  target url: https://skills.emsidata.com/skills/KS120GV6C72JMSZKMTD7
  target framework: EMSI
  target code: KS120GV6C72JMSZKMTD7
alignment:
  target name: Data Modeling
  target url: https://skills.emsidata.com/skills/KS122PM76DCYL9WC89Y7
  target framework: EMSI
  target code: KS122PM76DCYL9WC89Y7
alignment:
  target name: Define Measure Analyze Improve And Control (DMAIC)
  target url: https://skills.emsidata.com/skills/KS1232N75YHBKF78PT7S
  target framework: EMSI
  target code: KS1232N75YHBKF78PT7S
alignment:
  target name: Design For Six Sigma
  target url: https://skills.emsidata.com/skills/KS122WV6LN1WQ1S1718R
  target framework: EMSI
  target code: KS122WV6LN1WQ1S1718R
alignment:
  target name: Experimental Design
  target url: https://skills.emsidata.com/skills/KS122VN73RX0QR6GMDHV
  target framework: EMSI
  target code: KS122VN73RX0QR6GMDHV
alignment:
  target name: Six Sigma Black Belt Certification
  target url: https://skills.emsidata.com/skills/KS122KN7458KR53RS94C
  target framework: EMSI
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alignment:
  target name: Six Sigma Methodology
  target url: https://skills.emsidata.com/skills/KS1201W79NJW2J5ZQDFV
  target framework: EMSI
  target code: KS1201W79NJW2J5ZQDFV
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
alignment:
  target name: Control Chart
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-60283
alignment:
  target name: Data analysis
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-17153
alignment:
  target name: Data modeling
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-18420
alignment:
  target name: Define Measure Analyze Improve Control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-25111
alignment:
  target name: Robust Design for Six Sigma
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-30529
alignment:
  target name: Experimental design
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-19508
alignment:
  target name: Six Sigma Black Belt
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26202
alignment:
  target name: Six Sigma
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26900
criteria:
  id: https://asuengineeringonline.com/professional-programs/six-sigma-black-belt-certification
  narrative: A participant needs to review and understand the associated Six Sigma Black Belt lectures and curriculum, in addition to passing completion of the topic quizzes and exams with a score of 70% or higher, and executing an applied project to be reviewed and critiqued by a Master Black Belt.
type: BadgeClass

id: https://api.badgr.io/public/badges/lNW8w3nMRNitNUbjk82bwg
name: GCSP Service Learning
image:
  id: https://api.badgr.io/public/badges/lNW8w3nMRNitNUbjk82bwg/image
description: This digital credential certifies that the recipient has completed substantial service learning experience (at least 80 hours), to enhance their education and provide service to benefit the community. Holders of this micro-credential possess the social consciousness and motivation to use their technical expertise to help solve societal problems and have achieved the GCSP Social Consciousness competency. The Fulton Grand Challenges Scholars Program (GCSP), is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Students who complete the program receive the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: gcsp
tags: fulton difference
tags: service learning
alignment:
  target name: Communications
  target url: https://skills.emsidata.com/skills/KS122556LMQ829GZCCRV
  target framework: EMSI
  target code: KS122556LMQ829GZCCRV
alignment:
  target name: Leadership
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  target framework: EMSI
  target code: KS124JB619VXG6RQ810C
alignment:
  target name: Teamwork
  target url: https://skills.emsidata.com/skills/KSKJX44T33B6D4UCC8CB
  target framework: EMSI
  target code: KSKJX44T33B6D4UCC8CB
alignment:
  target name: Problem Solving
  target url: https://skills.emsidata.com/skills/KS125F678LV2KB3Z5XW0
  target framework: EMSI
  target code: KS125F678LV2KB3Z5XW0
alignment:
  target name: Communication
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-44146
alignment:
  target name: Leadership
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31001
alignment:
  target name: Teamwork
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31251
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars must demonstrate sustained involvement in service learning project(s) or experience(s) related to their Grand Challenges theme area and/or engineering for at least 80 hours. This competency may be fulfilled through participation in Engineering Projects in Community Service (EPICS) courses for two semesters. Other service learning experiences eligible for approval may include: - Engineers Without Borders projects - Engineering outreach projects - Providing service as a leader through Fulton Ambassadors or mentorship. Students will also include their experiences in their GCSP digital portfolio.
type: BadgeClass

id: https://api.badgr.io/public/badges/RSsJoDz5R3eNAXv2WMCKqw
name: GCSP Research / Creative Project
image:
  id: https://api.badgr.io/public/badges/RSsJoDz5R3eNAXv2WMCKqw/image
description: This digital credential certifies that the recipient successfully completed two semesters of approved mentored research or creative project experience(s) related to their Grand Challenges theme area. The holder of this micro-credential has demonstrated the application of technical and research skills to a real-world problem to gain more in-depth knowledge of work in their chosen focus area (e.g. health, security, sustainability, joy of living). Holders of this credential have achieved the GCSP Talent competency. The Fulton Grand Challenges Scholars Program (GCSP) is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Students who complete the program receive the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: gcsp
tags: fulton difference
tags: research
alignment:
  target name: Problem Solving
  target url: https://skills.emsidata.com/skills/KS125F678LV2KB3Z5XW0
  target framework: EMSI
  target code: KS125F678LV2KB3Z5XW0
alignment:
  target name: Research Methodologies
  target url: https://skills.emsidata.com/skills/KS126FN71XVM1PW06M54
  target framework: EMSI
  target code: KS126FN71XVM1PW06M54
alignment:
  target name: Experimentation
  target url: https://skills.emsidata.com/skills/KS123RB65VYS69WB3D65
  target framework: EMSI
  target code: KS123RB65VYS69WB3D65
alignment:
  target name: Analytical Thinking
  target url: https://skills.emsidata.com/skills/ES86C12B1AA904C0F35F
  target framework: EMSI
  target code: ES86C12B1AA904C0F35F
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
alignment:
  target name: Research methods
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-36630
alignment:
  target name: Experimenting
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-44217
alignment:
  target name: Analytical thinking
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31148
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars will complete two semesters of mentored research or creative project experience related to their chosen Grand Challenges theme in order to gain hands-on experience and more in-depth knowledge of work in their chosen focus area. This may include participation in one or more of the following: - The GCSP Research Stipend program - The Fulton Undergraduate Research Initiative (FURI) program - Research Experience for Undergraduates - Barrett, the Honors College research and thesis - Independent research/creative project with faculty - Senior Capstone Design project - Other research programs Students will also include their experiences in their GCSP digital portfolio.
type: BadgeClass

id: https://api.badgr.io/public/badges/zprnjOG9Rf2OOmEEuMCieQ
name: GCSP Interdisciplinary Perspective
image:
  id: https://api.badgr.io/public/badges/zprnjOG9Rf2OOmEEuMCieQ/image
description: This digital credential certifies that the recipient successfully completed coursework and/or experiences in fields outside of engineering that complements their technical engineering work in order to prepare them to work at the overlap with public policy, business, law, ethics, human behavior, risk, the arts, and medicine. The holder of this micro-credential is familiar with the grand challenges that face human societies in the 21st century and applies an interdisciplinary perspective and approach when addressing those challenges in order to develop effective solution(s). Holders of this credential have achieved the GCSP Multidisciplinary competency. The Fulton Grand Challenges Scholars Program (GCSP) is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Students who complete the program receive the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: gcsp
tags: fulton difference
tags: interdisciplinary
alignment:
  target name: Systems Thinking
  target url: https://skills.emsidata.com/skills/KS4415S6Q3Q6JPVT2P33
  target framework: EMSI
  target code: KS4415S6Q3Q6JPVT2P33
alignment:
  target name: Interdisciplinary Collaboration
  target url: https://skills.emsidata.com/skills/ESECBB9821400A2E8C22
  target framework: EMSI
  target code: ESECBB9821400A2E8C22
alignment:
  target name: Systems thinking
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31174
alignment:
  target name: Interdisciplinary Collaboration
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-54308
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars develop an interdisciplinary systems perspective of engineering challenges and solutions through completing the FSE150: Perspectives on Grand Challenges for Engineering course and an additional course or experience exploring fields and topics outside of engineering. Students will also include their experiences in their GCSP digital portfolio.
type: BadgeClass

id: https://api.badgr.io/public/badges/8zoBOWPsQjC7Q47L8KHI1w
name: GCSP Global Competency
image:
  id: https://api.badgr.io/public/badges/8zoBOWPsQjC7Q47L8KHI1w/image
description: This digital credential certifies that the recipient has completed experiences and/or courses to develop an understanding of different cultures that is necessary to ensure cultural acceptance of proposed engineering solutions. The holder of this micro-credential possesses the multicultural awareness and global perspective necessary to address the global Grand Challenges and to become leaders of innovation in a global economy. Holders of this credential have achieved the GCSP Multicultural competency. The Fulton Grand Challenges Scholars Program (GCSP) is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Students who complete the program receive the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: gcsp
tags: fulton difference
tags: global
alignment:
  target name: Intercultural Competence
  target url: https://skills.emsidata.com/skills/KS1258479NCTHZ20PFDH
  target framework: EMSI
  target code: KS1258479NCTHZ20PFDH
alignment:
  target name: Global Scope
  target url: https://skills.emsidata.com/skills/KSCMAVZ8V1RK4NMUHDSU
  target framework: EMSI
  target code: KSCMAVZ8V1RK4NMUHDSU
alignment:
  target name: Cultural Diversity
  target url: https://skills.emsidata.com/skills/KS122L46TBBZJ9858RGR
  target framework: EMSI
  target code: KS122L46TBBZJ9858RGR
alignment:
  target name: Intercultural Competence
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-60573
alignment:
  target name: Cultural Diversity
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-59034
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars complete two multicultural courses OR experiences. An approved multicultural experience may include a study abroad program, a global internship, or a service learning project with a multicultural focus (e.g. through Engineers Without Borders (EWB) or Engineering Projects in Community Service (EPICS)). Students will also include their experiences in their GCSP digital portfolio.
type: BadgeClass

id: https://api.badgr.io/public/badges/EwKV_wPzQ-WtPXCBtwinsw
name: GCSP Entrepreneurial Mindset
image:
  id: https://api.badgr.io/public/badges/EwKV_wPzQ-WtPXCBtwinsw/image
description: This digital credential certifies that the recipient has completed an entrepreneurship course or experience to develop an understanding of the necessity of a viable business model for solution implementation. The holder of this micro-credential has experienced the practical issues typically encountered in starting up and operating a technology venture, can translate invention to innovation, and understands how to develop market ventures that scale to global solutions in the public interest. . Holders of this credential have achieved the GCSP Viable Business/Entrepreneurship competency. The Fulton Grand Challenges Scholars Program (GCSP) is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Students who complete the program receive the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
alignment:
  target name: Communications
  target url: https://skills.emsidata.com/skills/KS122556LMQ829GZCCRV
  target framework: EMSI
  target code: KS122556LMQ829GZCCRV
alignment:
  target name: Business Model
  target url: https://skills.emsidata.com/skills/KS1218Z5ZY0SPNMNSX4H
  target framework: EMSI
  target code: KS1218Z5ZY0SPNMNSX4H
alignment:
  target name: Needs Analysis
  target url: https://skills.emsidata.com/skills/KS126X26ZWSBLW188T04
  target framework: EMSI
  target code: KS126X26ZWSBLW188T04
alignment:
  target name: Entrepreneurship
  target url: https://skills.emsidata.com/skills/KS123KR6W35X39JP65MX
  target framework: EMSI
  target code: KS123KR6W35X39JP65MX
alignment:
  target name: Communication
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-44146
alignment:
  target name: Business model
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-39174
alignment:
  target name: Needs analysis
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-18220
alignment:
  target name: Entrepreneurial
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31241
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars will apply an entrepreneurial mindset to develop a viable business idea by completing the FSE 301: Entrepreneurship & Value Creation course or participating in a substantial entrepreneurship experience, such as ASU Venture Devils. Students will also include their experiences in their GCSP digital portfolio.
type: BadgeClass

id: https://api.badgr.io/public/badges/1gUIKQWDQzGmmh-K2K0UQg
name: Grand Challenges Scholar
image:
  id: https://api.badgr.io/public/badges/1gUIKQWDQzGmmh-K2K0UQg/image
description: The Fulton Grand Challenges Scholars Program (GCSP), endorsed by the National Academy of Engineering (NAE), is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Holders of this credential have successfully demonstrated the five competencies of the Grand Challenges Scholars Program: Talent, Multidisciplinary, Viable Business/Entrepreneurship, Multicultural, and Social Consciousness. Fulton Engineering students who are holders of this credential have achieved the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: gcsp
tags: fulton difference
tags: research
tags: global
tags: service learning
tags: entrepreneurship
tags: interdisciplinary
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars will have successfully achieved the five competencies of the NAE Grand Challenges Scholars Program: - Talent - Multidisciplinary - Viable Business/Entrepreneurship - Multicultural - Social Consciousness. Each of these competencies is certified by their individual credential. In addition, scholars must create a digital GCSP Portfolio to document and reflect on their experiences in the program. The digital portfolio will contain a representation of the work that the program participant has completed, and will demonstrate how their experiences are interconnected and aligned with their chosen Grand Challenges theme.
type: BadgeClass

id: https://api.badgr.io/public/badges/j7FRyOgyQEmYxk2jcOfrhA
name: GCSP Portfolio
image:
  id: https://api.badgr.io/public/badges/j7FRyOgyQEmYxk2jcOfrhA/image
description: This digital credential certifies that the recipient successfully created a digital GCSP Portfolio that includes descriptions, artifacts, and reflections on their experiences in the program. The digital portfolio contains a representation of the work that the program participant has completed for each of the five GCSP Competencies, and demonstrates how their experiences are interconnected and aligned with their chosen Grand Challenges theme. The Fulton Grand Challenges Scholars Program (GCSP) is a co-curricular program that combines innovative curriculum and cutting-edge research experiences into an intellectual fusion that spans academic disciplines and focuses on addressing global challenges. GCSP students engage in research, interdisciplinary, entrepreneurial, global, and service learning opportunities, and are uniquely prepared to collaborate and succeed in a transdisciplinary and global environment. Students who complete the program receive the distinction of Grand Challenges Scholar, recognized by both ASU and the National Academy of Engineering (NAE).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: gcsp
tags: fulton difference
tags: portfolio
alignment:
  target name: Communications
  target url: https://skills.emsidata.com/skills/KS122556LMQ829GZCCRV
  target framework: EMSI
  target code: KS122556LMQ829GZCCRV
alignment:
  target name: Critical Reflection
  target url: https://skills.emsidata.com/skills/ESE931B22DABCD5A9E32
  target framework: EMSI
  target code: ESE931B22DABCD5A9E32
alignment:
  target name: Communication
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-44146
alignment:
  target name: Critical Reflection
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-58682
criteria:
  id: https://gcsp.engineering.asu.edu/grand-challenge-scholars-program/program-requirements/
  narrative: To earn this credential, Fulton Grand Challenges Scholars will submit their final GCSP digital portfolio which includes summaries and reflections on their experiences in GCSP.
type: BadgeClass

id: https://api.badgr.io/public/badges/yFnXhvPkSR2Mirh-f80Xbg
name: Software Engineering Professional Certification
image:
  id: https://api.badgr.io/public/badges/yFnXhvPkSR2Mirh-f80Xbg/image
description: This digital credential certifies that the recipient successfully completed the required Computer Science graduate-level courses in the following areas: Software Verification, Validation, and Testing, Advanced Software Analysis and Design, Engineering Blockchain Applications, and Mobile Computing. The recipient also successfully completed several time-intensive projects modeled from real-world problems to test their comprehension and application of concepts and tools collected from the courses. Through a combination of lectures, live sessions, peer collaboration and applied projects, the recipient demonstrates understanding of how to apply software development concepts to blockchain, testing software to validate sustainability of smart mobile applications, predict software reliability using operational profiles, and practice software architecture and design principles.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: computer science
tags: software development
tags: software engineering
tags: professional certification
tags: mcs
alignment:
  target name: Software Design
  target url: https://skills.emsidata.com/skills/KS440QQ6NNV7S10DQST6
  target framework: EMSI
  target code: KS440QQ6NNV7S10DQST6
alignment:
  target name: Software Testing
  target url: https://skills.emsidata.com/skills/KS120JQ70DXY0GB5YQJ1
  target framework: EMSI
  target code: KS120JQ70DXY0GB5YQJ1
alignment:
  target name: Quality Assurance
  target url: https://skills.emsidata.com/skills/KS128866SHL94J005TTG
  target framework: EMSI
  target code: KS128866SHL94J005TTG
alignment:
  target name: Internet Protocol Security (IP SEC)
  target url: https://skills.emsidata.com/skills/KS12592696SWR9G8BFF2
  target framework: EMSI
  target code: KS12592696SWR9G8BFF2
alignment:
  target name: Machine Learning
  target url: https://skills.emsidata.com/skills/KS1261Z68KSKR1X31KS3
  target framework: EMSI
  target code: KS1261Z68KSKR1X31KS3
alignment:
  target name: Blockchain
  target url: https://skills.emsidata.com/skills/KS2DSDNSP4UY5VRHV2CM
  target framework: EMSI
  target code: KS2DSDNSP4UY5VRHV2CM
alignment:
  target name: Mobile Application Software
  target url: https://skills.emsidata.com/skills/KS125C56C2Y9MPSV9YQ3
  target framework: EMSI
  target code: KS125C56C2Y9MPSV9YQ3
alignment:
  target name: Software design
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-30952
alignment:
  target name: Software testing
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-20720
alignment:
  target name: Software quality assurance
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-39761
alignment:
  target name: IP Security
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-32823
alignment:
  target name: Machine learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-30919
alignment:
  target name: Blockchain
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-34202
alignment:
  target name: Mobile Application Software
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-58634
criteria:
  id: https://www.coursera.org/mastertrack/software-engineering-asu
  narrative: A participant needs to complete three Computer Science graduate-level courses (9 graduate credit hours) in software verification, validation, and testing, advanced software analysis and design, engineering blockchain applications, and mobile computing. In addition, participants must complete multiple hands-on applied projects demonstrating their knowledge and comprehension of the course material.
type: BadgeClass

id: https://api.badgr.io/public/badges/xxF14BVoSgS8rSc15rzjkA
name: Cybersecurity Professional Certification
image:
  id: https://api.badgr.io/public/badges/xxF14BVoSgS8rSc15rzjkA/image
description: This digital credential certifies that the recipient successfully completed the required Computer Science graduate-level courses in the following areas: Information Assurance and Security, Applied Cryptography, Software Security, Advanced Computer Network Security, Distributor & Multiprocessor OS, and Accelerated Applied Security. The recipient also successfully completed a variety of time-intensive projects modeled from real-world problems to test their comprehension and application of concepts and tools collected from the courses. Through a combination of lectures, live sessions, peer collaboration and applied projects, the recipient understands the vast field of cybersecurity through the lens of computer systems and algorithms. The recipient has a deeper understanding of how to protect and defend information by ensuring their availability, integrity, authentication, confidentiality, and non-repudiation through protection, detection, and reaction practices.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: mcs
tags: information assurance
tags: information security
tags: computer science
tags: cybersecurity
alignment:
  target name: Information Assurance
  target url: https://skills.emsidata.com/skills/KS1254B63QQJGQ9C7BC2
  target framework: EMSI
  target code: KS1254B63QQJGQ9C7BC2
alignment:
  target name: Information Systems Security
  target url: https://skills.emsidata.com/skills/KS120SS664Q5HF0WHTLY
  target framework: EMSI
  target code: KS120SS664Q5HF0WHTLY
alignment:
  target name: Cryptography
  target url: https://skills.emsidata.com/skills/KS122JY6L949K25JM9B7
  target framework: EMSI
  target code: KS122JY6L949K25JM9B7
alignment:
  target name: Encryption
  target url: https://skills.emsidata.com/skills/KS122ST5X31W4TVGPN0B
  target framework: EMSI
  target code: KS122ST5X31W4TVGPN0B
alignment:
  target name: Authentications
  target url: https://skills.emsidata.com/skills/KS120S165SXK6CKVK77P
  target framework: EMSI
  target code: KS120S165SXK6CKVK77P
alignment:
  target name: Network Protocols
  target url: https://skills.emsidata.com/skills/KS126ZZ7012DHZLZ9GC0
  target framework: EMSI
  target code: KS126ZZ7012DHZLZ9GC0
alignment:
  target name: Secure Network Communications
  target url: https://skills.emsidata.com/skills/KS440DL6XFPCDBD0H818
  target framework: EMSI
  target code: KS440DL6XFPCDBD0H818
alignment:
  target name: Distributed Computing
  target url: https://skills.emsidata.com/skills/KS12320732STS9N7XT64
  target framework: EMSI
  target code: KS12320732STS9N7XT64
alignment:
  target name: Multiprocessing
  target url: https://skills.emsidata.com/skills/KS126SH69Z48BC442BZ2
  target framework: EMSI
  target code: KS126SH69Z48BC442BZ2
alignment:
  target name: Information assurance
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26746
alignment:
  target name: IT security
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-39827
alignment:
  target name: Cryptography
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-34418
alignment:
  target name: Encryption
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-37496
alignment:
  target name: Authentication
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-37385
alignment:
  target name: Network protocols
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-37532
alignment:
  target name: Secure Network Communications
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-59304
alignment:
  target name: Distributed computing
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-34244
alignment:
  target name: Multiprocessing
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-27142
criteria:
  id: https://www.coursera.org/mastertrack/cybersecurity-asu
  narrative: A participant needs to complete three Computer Science graduate-level courses (9 graduate credit hours) in information assurance and security, applied cryptography, software security, advanced computer network security, distributor and multiprocessor OS, and accelerated applied security. In addition, participants must complete several hands-on applied projects demonstrating their knowledge and comprehension of the course material.
type: BadgeClass

id: https://api.badgr.io/public/badges/nquUe6USSFCQZYoxyQ-wWw
name: Big Data Professional Certification
image:
  id: https://api.badgr.io/public/badges/nquUe6USSFCQZYoxyQ-wWw/image
image alt text: Big Data Professional Certification badge image
description: This digital credential certifies that the recipient successfully completed the required Computer Science graduate-level courses in the following areas: Data Processing at Scale, Data Mining, Statistical Machine Learning, and Data Visualization The recipient also successfully completed a number of projects modeled from real-world problems to test their comprehension and application of concepts and tools collected from the courses. Through a combination of lectures, live sessions, peer collaboration and applied projects, the recipient is equipped to apply mathematical theory and decision making techniques to draw important insights from collected data to drive decisions.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: mcs
tags: computer science
tags: big data
tags: data mining
tags: data visualization
alignment:
  target name: Data Mining
  target url: https://skills.emsidata.com/skills/KS122PL70D99VRWMFM2T
  target framework: EMSI
  target code: KS122PL70D99VRWMFM2T
alignment:
  target name: Data Visualization
  target url: https://skills.emsidata.com/skills/KS122Q960QYMW3YH0YWF
  target framework: EMSI
  target code: KS122Q960QYMW3YH0YWF
alignment:
  target name: Big Data
  target url: https://skills.emsidata.com/skills/KS1210269P78D7GH8LWY
  target framework: EMSI
  target code: KS1210269P78D7GH8LWY
alignment:
  target name: Database Queries
  target url: https://skills.emsidata.com/skills/KS122QR67M3DQDNP937T
  target framework: EMSI
  target code: KS122QR67M3DQDNP937T
alignment:
  target name: Spatial Databases
  target url: https://skills.emsidata.com/skills/KS1249N615J2PKTR83N7
  target framework: EMSI
  target code: KS1249N615J2PKTR83N7
alignment:
  target name: NoSQL
  target url: https://skills.emsidata.com/skills/KS1272Y6RFNZKNGWDWMB
  target framework: EMSI
  target code: KS1272Y6RFNZKNGWDWMB
alignment:
  target name: Database Models
  target url: https://skills.emsidata.com/skills/KS122QQ6C07PY860HWBF
  target framework: EMSI
  target code: KS122QQ6C07PY860HWBF
alignment:
  target name: Data Modeling
  target url: https://skills.emsidata.com/skills/KS122PM76DCYL9WC89Y7
  target framework: EMSI
  target code: KS122PM76DCYL9WC89Y7
alignment:
  target name: Data Classification
  target url: https://skills.emsidata.com/skills/KS7G7PW66DSP5F1XZFCB
  target framework: EMSI
  target code: KS7G7PW66DSP5F1XZFCB
alignment:
  target name: Database Cluster
  target url: https://skills.emsidata.com/skills/KSEX0WRF8BL1FK9HZSC2
  target framework: EMSI
  target code: KSEX0WRF8BL1FK9HZSC2
alignment:
  target name: Deep Learning
  target url: https://skills.emsidata.com/skills/KSBZ9LW988KC56I219SP
  target framework: EMSI
  target code: KSBZ9LW988KC56I219SP
alignment:
  target name: Reinforcement Learning
  target url: https://skills.emsidata.com/skills/KS4LL1EBNO7YQ6BSVNIW
  target framework: EMSI
  target code: KS4LL1EBNO7YQ6BSVNIW
alignment:
  target name: Apache Spark
  target url: https://skills.emsidata.com/skills/KSAS8LK1JG2Y0OLN4C53
  target framework: EMSI
  target code: KSAS8LK1JG2Y0OLN4C53
alignment:
  target name: Data mining
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-18366
alignment:
  target name: Data Visualization
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-48486
alignment:
  target name: Big data
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-33718
alignment:
  target name: Database Queries
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-59191
alignment:
  target name: Spatial data
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-40110
alignment:
  target name: NoSQL
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26533
alignment:
  target name: Database modeling
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-35966
alignment:
  target name: Data modeling
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-18420
alignment:
  target name: Data Classification
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-55949
alignment:
  target name: Database Cluster
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-59190
alignment:
  target name: Deep learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-35909
alignment:
  target name: Reinforcement learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-35924
alignment:
  target name: Apache Spark
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-52901
criteria:
  id: https://www.coursera.org/mastertrack/big-data-asu
  narrative: A participant needs to complete three Computer Science graduate-level courses (9 graduate credit hours) in data processing, data mining, statistical machine learning, and data visualization, in addition to completing hands-on applied projects demonstrating their knowledge and comprehension of the course material.
type: BadgeClass

id: https://api.badgr.io/public/badges/e3Nr1eAtT2GHjEdMjx_28A
name: AI / ML Professional Certification
image:
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description: This digital credential certifies that the recipient successfully completed the required Computer Science graduate-level courses in the following areas: Statistical Machine Learning, Artificial Intelligence, Knowledge Representation and Reasoning, and Intro to Deep Learning in Visual Computing The recipient also successfully completed 5 time-intensive projects modeled from real-world problems to test their comprehension and application of concepts and tools collected from the courses. Through a combination of lectures, live sessions, peer collaboration and applied projects, the recipient understands how machines learn through learning paradigms, how to create autonomous agents, and how to train and optimize deep neural networks. The recipient has a deeper understanding in mathematics foundational skills, the AI techniques used by skilled practitioners, working effectively on machine learning projects, and how to identify opportunities for direct application of AI.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: mcs
tags: computer science
tags: professional certification
tags: machine learning
tags: artificial intelligence
tags: deep learning architectures
alignment:
  target name: Supervised Learning
  target url: https://skills.emsidata.com/skills/KS4412R60NGTMD1785Z8
  target framework: EMSI
  target code: KS4412R60NGTMD1785Z8
alignment:
  target name: Unsupervised Learning
  target url: https://skills.emsidata.com/skills/KSYMJOZ8SL2197CK9UAU
  target framework: EMSI
  target code: KSYMJOZ8SL2197CK9UAU
alignment:
  target name: Deep Learning
  target url: https://skills.emsidata.com/skills/KSBZ9LW988KC56I219SP
  target framework: EMSI
  target code: KSBZ9LW988KC56I219SP
alignment:
  target name: Deep Learning Methods
  target url: https://skills.emsidata.com/skills/ES444F7110B700C46A8C
  target framework: EMSI
  target code: ES444F7110B700C46A8C
alignment:
  target name: Machine Learning Methods
  target url: https://skills.emsidata.com/skills/ES2933FAEF72CE4E2840
  target framework: EMSI
  target code: ES2933FAEF72CE4E2840
alignment:
  target name: Machine Learning Algorithms
  target url: https://skills.emsidata.com/skills/KS125S466GJGQXS1W0JM
  target framework: EMSI
  target code: KS125S466GJGQXS1W0JM
alignment:
  target name: Contextual Image Classification
  target url: https://skills.emsidata.com/skills/KS1251W5YWWBQML40R1V
  target framework: EMSI
  target code: KS1251W5YWWBQML40R1V
alignment:
  target name: Supervised learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-35918
alignment:
  target name: Unsupervised learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-35919
alignment:
  target name: Deep learning
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-35909
alignment:
  target name: Machine learning methods
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-43712
alignment:
  target name: Machine learning algorithms
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-43704
alignment:
  target name: Contextual Image Classification
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-59046
criteria:
  id: https://www.coursera.org/mastertrack/ai-machine-learning-asu
  narrative: A participant needs to complete three Computer Science graduate-level courses (9 graduate credit hours) in statistical machine learning, artificial intelligence, knowledge representation and reasoning, and deep learning in visual computing. In addition, participants must complete hands-on applied projects demonstrating their knowledge and comprehension of the course material.
type: BadgeClass

id: https://api.badgr.io/public/badges/ONux3SDAQ6u0g_cnirnwpA
name: Lean Six Sigma Black Belt (LSSBB)
image:
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Lean Six Sigma Black Belt certification program offered by Arizona State University. The recipient is capable of managing and leading complex and transformative projects designed to increase efficiency and remove obstacles from an organization’s processes and workflows. The holder of this digital credential can apply the DMAIC methodology, design for six sigma and lean product development concepts to product/process design projects. In addition, a certified Lean Six Sigma Black Belt uses appropriate analytical, statistical, and lean methods, and a fundamental understanding on how to plan, design and conduct experiments efficiently and effectively. Lean Six Sigma Black Belts oversee tactical and strategic projects as well as various operational functions in their organizations. Other characteristics of a Lean Six Sigma Black Belt professional include demonstration of team leadership, understanding of all aspects of the so called Define-Measure- Analyze-Improve-Control (DMAIC) improvement process, and basic knowledge of lean enterprise concepts that allows identification of the non-value-added elements and activities.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: project management
tags: quality improvement
tags: strategy
tags: management
tags: quality assurance
tags: advance quality control
tags: six sigma
tags: lean six sigma
tags: process improvement
tags: continuous improvement
alignment:
  target name: Complex Problem Solving
  target url: https://skills.emsidata.com/skills/ES80E93EF8629D5C793C
  target framework: EMSI
  target code: ES80E93EF8629D5C793C
alignment:
  target name: Control Chart
  target url: https://skills.emsidata.com/skills/KS122CN61BGR14CT2VZF
  target framework: EMSI
  target code: KS122CN61BGR14CT2VZF
alignment:
  target name: Data Analysis
  target url: https://skills.emsidata.com/skills/KS120GV6C72JMSZKMTD7
  target framework: EMSI
  target code: KS120GV6C72JMSZKMTD7
alignment:
  target name: Data Modeling
  target url: https://skills.emsidata.com/skills/KS122PM76DCYL9WC89Y7
  target framework: EMSI
  target code: KS122PM76DCYL9WC89Y7
alignment:
  target name: Define Measure Analyze Improve And Control (DMAIC)
  target url: https://skills.emsidata.com/skills/KS1232N75YHBKF78PT7S
  target framework: EMSI
  target code: KS1232N75YHBKF78PT7S
alignment:
  target name: Design For Six Sigma
  target url: https://skills.emsidata.com/skills/KS122WV6LN1WQ1S1718R
  target framework: EMSI
  target code: KS122WV6LN1WQ1S1718R
alignment:
  target name: Experimental Design
  target url: https://skills.emsidata.com/skills/KS122VN73RX0QR6GMDHV
  target framework: EMSI
  target code: KS122VN73RX0QR6GMDHV
alignment:
  target name: Six Sigma Black Belt Certification
  target url: https://skills.emsidata.com/skills/KS122KN7458KR53RS94C
  target framework: EMSI
  target code: KS122KN7458KR53RS94C
alignment:
  target name: Lean Six Sigma
  target url: https://skills.emsidata.com/skills/KS125S36C8SFQ912VYBS
  target framework: EMSI
  target code: KS125S36C8SFQ912VYBS
alignment:
  target name: Lean Methodologies
  target url: https://skills.emsidata.com/skills/BGSA73E55B8CBD1E4213
  target framework: EMSI
  target code: BGSA73E55B8CBD1E4213
alignment:
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  target framework: EMSI
  target code: KS126085Y5H5R7G4467Q
alignment:
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  target url: https://skills.emsidata.com/skills/KS1201W79NJW2J5ZQDFV
  target framework: EMSI
  target code: KS1201W79NJW2J5ZQDFV
alignment:
  target name: Problem solving
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-31096
alignment:
  target name: Control Chart
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-60283
alignment:
  target name: Data analysis
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-17153
alignment:
  target name: Data modeling
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-18420
alignment:
  target name: Define Measure Analyze Improve Control
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-25111
alignment:
  target name: Robust Design for Six Sigma
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-30529
alignment:
  target name: Experimental design
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-19508
alignment:
  target name: Six Sigma Black Belt
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26202
alignment:
  target name: Lean Six Sigma
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-27031
alignment:
  target name: Lean methodology
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-43183
alignment:
  target name: Six Sigma Black Belt - LEAN
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26203
alignment:
  target name: Six Sigma
  target url: https://talentneuron.com/
  target framework: TALENT_NEURON
  target code: TN-26900
criteria:
  id: https://scai.engineering.asu.edu/graduate-certificate-lean-six-sigma-black-belt/
  narrative: The Lean Six Sigma Black Belt graduate certificate awards the Lean Six Sigma Black Belt certification for participants who successfully complete 5 graduate-level courses, including a LSS capstone course. The required IEE 585 Six Sigma Capstone course involves a real-life six sigma project, conducted under the supervision of the course instructor. The project involves all of the usual and necessary components of the DMAIC (Define, Measure, Analyze, Improve, Control) process of the Six Sigma approach, in addition to the demonstration of a deep understanding of the problem and solution approach by the participant.
type: BadgeClass

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name: Introduction to Analytics for Intelligent Manufacturing
image:
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image alt text: Introduction to Analytics for Intelligent Manufacturing badge image
description: This microcredential certifies that the recipient successfully completed ten hours of instruction, including lab-based assignments, covering the basis of simulation and data driven methods for analysis and improvement of manufacturing systems. The course features engaging hands on experiments to understand the use of data for process improvement and demonstrates the application of concepts learned in the courses in the workplace.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: iiot
tags: analytics
tags: manufacturing
tags: advanced manufacturing
criteria:
  narrative: This ten-hour course introduces the earner to simulation and data driven methods for analysis and improvement of manufacturing systems. The course modules offer a lecture and hands-on lab training in simulation and computer design of experiments, fundamental topics in quality control, and an overview to operations, modeling, and optimization in industry. The earner has successfully completed all required portions of this course.
type: BadgeClass

id: https://api.badgr.io/public/badges/b76rUJY0Tzm0ANT3RhtJ0w
name: IIoT: Raspberry PI with MQTT
image:
  id: https://api.badgr.io/public/badges/b76rUJY0Tzm0ANT3RhtJ0w/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the IIoT: Raspberry Pi with MQTT Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has the ability to develop a functional backend-middleware-frontend working solution with raspberry pi4 and showcase a solution with basic analysis with data contained within the database historian.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2. Level 2 micro-badges empower learners to apply their foundational knowledge through hands-on practice, allowing them to demonstrate their skills with tangible evidence. To earn this badge, learners must demonstrate their ability to develop a functional backend-middleware-frontend working solution with raspberry pi4 and showcase a solution with basic analysis with data contained within the database historian.
type: BadgeClass

id: https://api.badgr.io/public/badges/JydCLYiiTguoHSK2DZ9jSg
name: IIoT: MQTT Protocol
image:
  id: https://api.badgr.io/public/badges/JydCLYiiTguoHSK2DZ9jSg/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the IIoT: MQTT Protocol Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has the demonstrated proficiency in implementing MQTT communication and developing dashboards using Node-red.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2. Level 2 micro-badges empower learners to apply their foundational knowledge through hands-on practice, allowing them to demonstrate their skills with tangible evidence. To earn this micro-badge, individuals must simulate publishing and subscribing to machine data via MQTT in Node-red as well as create a node-red dashboard for simulated machine monitoring.
type: BadgeClass

id: https://api.badgr.io/public/badges/gitX44mQQ-qhDvY8IhdNLw
name: IIoT: Industrial Data Flow
image:
  id: https://api.badgr.io/public/badges/gitX44mQQ-qhDvY8IhdNLw/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the IIoT: Industrial Data Flow Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has the demonstrated an understanding of the foundational principles of smart manufacturing and how data flow contributes to smart manufacturing initiatives.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2. Level 2 micro-badges empower learners to apply their foundational knowledge through hands-on practice, allowing them to demonstrate their skills with tangible evidence. To earn this micro-badge, individuals must give contemporary examples of the applications of Smart Manufacturng, and perform a case study for a small manufacturer profile in the context of industrial data flow.
type: BadgeClass

id: https://api.badgr.io/public/badges/v_2yqd2sSe23pohX0QcJRQ
name: IIoT: Factory Dashboard with MQTT
image:
  id: https://api.badgr.io/public/badges/v_2yqd2sSe23pohX0QcJRQ/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the IIoT: Factory Dashboard with MQTT Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has the demonstrated an their ability to analyze simulated machine-generated data via MQTT and create a Grafana Canvas UI dashboard to visualize data on a web framework.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2. Level 2 micro-badges empower learners to apply their foundational knowledge through hands-on practice, allowing them to demonstrate their skills with tangible evidence.
type: BadgeClass

id: https://api.badgr.io/public/badges/HCbF5wcwSS-AK_bPXYhBzA
name: Industrial Internet of Things (IIoT) with MQTT Protocol Badge
image:
  id: https://api.badgr.io/public/badges/HCbF5wcwSS-AK_bPXYhBzA/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Industrial Internet of Things Badge offered by Arizona State University. The holder of this digital credential has completed all four of the related micro-badge: Industrial Data Flow, MQTT Protocol, Factory Dashboard with MQTT, and Raspberry Pi with MQTT. This recipient has demonstrated skills in IIoT technology stack management, MQTT protocol implementation, front-end UI development, middleware deployment, and smart manufacturing integration.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: Each micro badge part of this sequence is offered at Level 2. Level 2 micro-badges empower learners to apply their foundational knowledge through hands-on practice, allowing them to demonstrate their skills with tangible evidence. To earn this badge, individuals must complete all four micro-badges in the sequence: Industrial Data Flow, MQTT Protocol, Factory Dashboard with MQTT, Raspberry Pi with MQTT. Through this sequence, recipients must demonstrate their mastery of skills like describing how data flows from the machine asset to the factory information system, identifying, interpreting , and applying the publish-subscribe (Pub-Sub) based MQTT communication protocol, and deploying middleware services that connect the backend and frontent of an IIoT hardware-software stack.
type: BadgeClass

id: https://api.badgr.io/public/badges/vCeeD1ZBSmyyUtMx4_60eA
name: Engineering Project Management Certificate
image:
  id: https://api.badgr.io/public/badges/vCeeD1ZBSmyyUtMx4_60eA/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Engineering Project Management Certificate program offered by Arizona State University. The recipient holds a deep grasp of engineering project management principles, including project life cycle, key deliverables, and budget adherence. Certificate holders excel in defining project requirements, applying Work Breakdown Structure (WBS), and using Project Evaluation and Review Technique (PERT)/Critical Path Method (CPM) for effective scheduling. They are also proficient in Earned Value Management (EVM) and Agile methodologies, ensuring adaptability and efficiency. This certification validates the recipient's skill in enhancing project execution and delivering significant organizational value, making it a testament to their capability to manage and navigate complex engineering projects efficiently.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
tags: project management
tags: scope statement
tags: pert
tags: cpm
tags: work breakdown structure (wbs)
tags: agile
tags: scrum
criteria:
  id: https://goee.asu.edu/professional-programs/engineering-project-management
  narrative: The Engineering Project Management course consists of 15 hours of instruction, and an applied project. Participants will have 30 days after completing the 5-week training to submit complete their project consisting of a scope statement, Work Breakdown Structure and PERT/CPM diagram, a Monitor and Control Logbook, and a final report. During this time, the course instructors will provide coaching and advising to participants after specific phases of the project. For successful completion, participants must attend the 3-hour weekly sessions and successfully complete an Engineering Project Management applied project.
type: BadgeClass

id: https://api.badgr.io/public/badges/bT7O30trQpadgpCvOOC2iQ
name: Fundamentals Geometric Dimensioning and Tolerancing
image:
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description: Fundamentals of Geometric Dimensioning and Tolerancing (GD&T) This Level 2 micro-badge recognizes participants who have successfully completed a 10-hour introductory course on Geometric Dimensioning and Tolerancing (GD&T). The program equips learners with foundational knowledge of GD&T symbols, their meanings, and applications in interpreting prints and 3D models. Participants demonstrated their skills through hands-on practice, showcasing their ability to apply this international design standard for precise part feature control and tolerance analysis.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/contact-us
  narrative: To earn this micro-badge, participants must successfully complete the 10-hour GD&T course and pass the final exam with a score of 70% or higher.
type: BadgeClass

id: https://api.badgr.io/public/badges/MXnRyIUuSOuUNvikQp5rwA
name: Introduction to Semiconductor Packaging and Design
image:
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Introduction to Semiconductor Packaging and Design Micro-badge offered by Arizona State University.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: This micro-badge is offered at Level 1, focusing on foundational knowledge in semiconductor packaging and design. Level 1 Micro-badges cover essential concepts, key terms, and industry-specific examples that provide a comprehensive understanding of the subject matter. To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. The assessments will evaluate your understanding of semiconductor packaging types, the role of packaging in technological advancements, and the latest trends in the field.
type: BadgeClass

id: https://api.badgr.io/public/badges/EMaChAscRti4ZcLXTN0roQ
name: Introduction to Electrical Concepts in Semiconductor Packaging
image:
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Introduction to Electrical Concepts in Semiconductor Packaging Micro-badge offered by Arizona State University.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 1, focusing on foundational knowledge in electrical concepts related to semiconductor packaging. Level 1 Micro-badges cover essential theories, key terminology, and practical examples that are crucial for understanding the electrical aspects of semiconductor packaging. To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your grasp of power delivery network design, signal integrity principles, and the implementation of voltage regulation and noise reduction techniques in semiconductor packaging.
type: BadgeClass

id: https://api.badgr.io/public/badges/HSXQuyvsQjySaCT2y6zIJg
name: Introduction to Thermal Management and Mechanical Properties of Packages
image:
  id: https://api.badgr.io/public/badges/HSXQuyvsQjySaCT2y6zIJg/image
description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Introduction to Thermal Management and Mechanical Properties of Packages Micro-badge offered by Arizona State University.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: This micro-badge is offered at Level 1, focusing on foundational knowledge in thermal management and mechanical properties of semiconductor packages. Level 1 Micro-badges cover essential principles, key concepts, and practical applications that are crucial for understanding the thermal and mechanical integrity of electronic packages. To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your understanding of thermal resistance, heat transfer methods, and the mechanical behavior of materials in semiconductor packaging, ensuring you can apply these concepts to real-world scenarios.
type: BadgeClass

id: https://api.badgr.io/public/badges/iPldu3zdReOjfUBX6GrKFA
name: Introduction to Packaging Materials, Manufacturing, Test, and Reliability
image:
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of theIntroduction to Packaging Materials, Manufacturing, Test, and Reliability Micro-badge offered by Arizona State University.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: This micro-badge is offered at Level 1, focusing on foundational knowledge in packaging materials, manufacturing processes, testing, and reliability in the semiconductor industry. Level 1 Micro-badges provide a broad overview of essential concepts, key terminology, and practical examples in these areas. To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will evaluate your grasp of material selection, manufacturing techniques, and reliability testing in semiconductor packaging, along with process control systems and statistical analysis.
type: BadgeClass

id: https://api.badgr.io/public/badges/N_tOVEhjTwKC6UxhpPnsJQ
name: Semiconductor Packaging: Fundamental Concepts and Drivers
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Semiconductor Packaging: Fundamental Concepts and Drivers Badge offered by Arizona State University. The holder of this digital credential has completed all four of the related micro-badge: Introduction to Semiconductor Packaging and Design, Introduction to Electrical Concepts in Semiconductor Packaging, Introduction to Thermal Management and Mechanical Properties of Packages and Introduction to Packaging Materials, Manufacturing, Test, and Reliability.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this badge, individuals must complete all four micro-badges in the sequenceThis full badge is offered at Level 1, encompassing a comprehensive understanding of semiconductor packaging across four key areas: design, electrical concepts, thermal management, and materials/manufacturing processes. To earn the full badge, participants must complete all four micro-badges within the approved sequence, achieving a score of 70% or higher on each assessment. This ensures a solid grasp of semiconductor packaging principles, from design and electrical concepts to thermal management and reliability testing. Completing this full badge also qualifies participants for Continuing Education Units (CEU).
type: BadgeClass

id: https://api.badgr.io/public/badges/e27K4IURSzezKcbPsw0u-Q
name: Introduction to Microcontrollers and Electronics
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all of the requirements of the Introduction to Microcontrollers and Electronics Level 1 Micro-badge offered by Arizona State University. The holder of this digital credential has the demonstrated and ability to simulate basic electrical circuits , understand the usages of microcontrollers and integrate them into electrical circuits.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: Level 1 Micro-badges are associated with foundational knowledge, broader picture, key terms, explanations around a specific topic and examples of the use across the technology sector. This micro-badge is offered at Level 1, providing foundational knowledge on the topics of electricity, circuits, and microcontrollers. To earn this micro-badge, participants must successfully complete in-class exercises and activities that demonstrate knowledge and their ability to integrate a microcontroller in a circuit to complete a specific function.
type: BadgeClass

id: https://api.badgr.io/public/badges/hiQmbu0FTTuz3u3Wdfrk0w
name: Robotic Arm Programming and Control
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description: This digital credential certifies that the recipient successfully completed the "Robotic Arm Programming and Control" Level 1 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated foundational knowledge in robotic arm components, programming, and control (ie: integrating sensors, implementing control algorithms, and understanding the essential principles of kinematics and actuation.)
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 1, providing foundational knowledge on the components and control of robotic arms, as well as their applications in various industries. To earn this micro-badge, participants must successfully complete hands-on activities and in-class exercises that demonstrate their understanding of robotic arm assembly, integration of sensors, and the ability to implement a basic control algorithm for arm manipulation.
type: BadgeClass

id: https://api.badgr.io/public/badges/hSeCyrBiRw2z69ACQbWgKA
name: Introduction to Autonomous Robots
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description: This digital credential certifies that the recipient successfully completed the "Introduction to Autonomous Robots" Level 1 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated foundational knowledge in autonomous robotic systems, including design, sensing, and control, as well as an understanding of basic programming concepts and levels of autonomy in robotics.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 1, providing foundational knowledge on the components, control, and autonomy of robotic systems. To earn this micro-badge, participants must demonstrate their understanding of basic robotic components such as sensors, actuators, and controllers through in-class activities and an interactive case study, as well as present key control components necessary for developing a real-world autonomous system.
type: BadgeClass

id: https://api.badgr.io/public/badges/lgrbbzuESPePrZz44Jq5qQ
name: Import Pretrained Deep Learning in TensorFlow
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description: This digital credential certifies that the recipient successfully completed the "Import Pretrained Deep Learning Models in TensorFlow" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated intermediate knowledge of deep learning and TensorFlow, and can access, import, and work with pretrained deep learning models in a Python programming environment.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2, providing knowledge of deep learning concepts, TensorFlow fundamentals, and pretrained models. To earn this micro-badge, participants must demonstrate their understanding of deep learning and neural networks, as well as their ability to code, debug, and solve problems using TensorFlow. This includes successfully importing and working with pretrained models in a Python programming environment.
type: BadgeClass

id: https://api.badgr.io/public/badges/7tUP1psWRyKEtzF7AJAtCg
name: 2D Packaging & Assembly
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description: This micro-badge is offered at Level 2, focusing on advanced knowledge of 2D packaging and assembly within the semiconductor industry. Level 2 Micro-badges provide in-depth coverage of complex concepts, key terminology, and practical applications, enabling participants to gain a deeper understanding of the subject matter.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will evaluate your expertise in lead frame assembly, advanced wire bonding techniques, flip chip technology, and PCB assembly processes, including detailed inspection and quality control methodologies essential to 2D semiconductor packaging.
type: BadgeClass

id: https://api.badgr.io/public/badges/SuqG26idSlSZ0Pmuoudp4g
name: Materials Selection for Thermo-Mechanical and Electrical Performance
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description: This micro-badge is offered at Level 2, focusing on advanced knowledge of materials selection for optimizing thermo-mechanical and electrical performance in semiconductor packaging. Level 2 Micro-badges provide in-depth exploration of complex concepts, key terminology, and practical applications in this critical area.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your ability to evaluate material properties such as thermal conductivity, electrical conductivity, and mechanical strength, and apply these principles to solve thermo-mechanical and electrical challenges in semiconductor packaging.
type: BadgeClass

id: https://api.badgr.io/public/badges/vOCT5oMkQk6rWNItCyl5vw
name: Application of Electrical & Thermo-Mechanical Modeling
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description: This micro-badge is offered at Level 2, focusing on advanced modeling techniques for electrical and thermo-mechanical properties in semiconductor packaging. Level 2 Micro-badges provide comprehensive coverage of complex modeling concepts, essential terminology, and practical examples critical for mastering semiconductor package modeling.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will evaluate your expertise in electrical, thermal, and thermo-mechanical analysis, optimization techniques, and the application of simulation tools to solve real-world packaging challenges in the semiconductor industry.
type: BadgeClass

id: https://api.badgr.io/public/badges/Fpw3Y4S0Sv-x-2NcSmGuSQ
name: Test and Data Analysis for Quality and Reliability
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description: This micro-badge is offered at Level 2, focusing on testing methodologies and data analysis for ensuring quality and reliability in semiconductor packaging. Level 2 Micro-badges provide in-depth coverage of key concepts, analytical tools, and practical applications critical for evaluating package performance. To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your understanding of reliability testing techniques, data analysis methods, and statistical tools used to assess product quality and predict failure modes in semiconductor packaging.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: This micro-badge is offered at Level 2, focusing on advanced testing methodologies and data analysis for ensuring quality and reliability in semiconductor packaging. Level 2 Micro-badges provide in-depth coverage of key concepts, analytical tools, and practical applications critical for evaluating package performance. To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your understanding of reliability testing techniques, data analysis methods, and statistical tools used to assess product quality and predict failure modes in semiconductor packaging.
type: BadgeClass

id: https://api.badgr.io/public/badges/-IW-hq5gTOeZbXTnRhBgOg
name: Semiconductor Packaging, Assembly, and Test
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description: This digital credential certifies that the recipient successfully completed the learning components and has met all the requirements of the Semiconductor Packaging, Assembly, and Test Badge offered by Arizona State University. The holder of this digital credential has completed all four related micro-badges: 2D Packaging and Assembly, Materials Selection for Thermo-Mechanical and Electrical Performance, Application of Electrical and Thermo-Mechanical Modeling, and Test and Data Analysis for Quality and Reliability.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: This full badge is offered at Level 2 and requires the completion of all four micro-badges: 2D Packaging and Assembly, Materials Selection for Thermo-Mechanical and Electrical Performance, Application of Electrical and Thermo-Mechanical Modeling, and Test and Data Analysis for Quality and Reliability. Each micro-badge focuses on advanced concepts, essential terminology, and practical applications relevant to semiconductor packaging and manufacturing. To earn this full badge, participants must complete all course sessions for each micro-badge and achieve a score of 70% or higher on the assessments associated with each. These assessments will evaluate your comprehensive understanding of semiconductor packaging processes, material properties, modeling techniques, and testing methodologies, ensuring you are well-equipped to excel in the semiconductor industry.
type: BadgeClass

id: https://api.badgr.io/public/badges/4MT-iZVjRLKZHTK-cI2O9w
name: Utilize Pretrained DL Models in TensorFlow
image:
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description: This digital credential certifies that the recipient successfully completed the "Utilize Pretrained Deep Learning Models in TensorFlow" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated intermediate knowledge of feature extraction and decision-making using pretrained deep learning models in TensorFlow, along with hands-on experience in applying these techniques in a Python programming environment.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2, providing knowledge of deep learning-based feature extraction and decision-making using pretrained models. To earn this micro-badge, participants must demonstrate their ability to code, debug, and solve problems using TensorFlow, particularly in the context of feature extraction and decision-making with pretrained deep learning models. This includes completing activities involving the transfer and update of pretrained models.
type: BadgeClass

id: https://api.badgr.io/public/badges/1dOs5IeCTrirYT3P9EkWxw
name: Develop Convolutional Neural Networks with TensorFlow
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description: This digital credential certifies that the recipient successfully completed the "Develop Convolutional Neural Networks in TensorFlow" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated an understanding of CNN model architecture, coding CNNs in TensorFlow, and applying these models to process image data.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this Level 2 micro-badge, recipients demonstrated foundational skills in building and implementing Convolutional Neural Networks (CNNs) using TensorFlow. They completed hands-on activities, including coding CNNs from scratch, designing model architecture, and training models on image data. Additionally, they showed a thorough understanding of image convolution processes, the unique strengths and weaknesses of CNNs, and their application to different types of imaging data. Their work evidenced their ability to apply CNN knowledge to practical problem-solving in a Python programming environment.
type: BadgeClass

id: https://api.badgr.io/public/badges/EZnNAWjDSZehT2_ykSJTKw
name: Microcontroller Programming Basics
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description: The Microcontroller Programming Basics micro-badge covers essential skills for beginners in microcontroller programming. Students explore basic program structure, syntax, and key logic concepts, such as if-statements, for-loops, and functions. They also practice using flowcharts to plan code and apply debugging techniques with tools like breakpoints and the serial monitor. Ideal for those interested in engineering or tech, this course provides hands-on experience with microcontroller applications, building foundational skills to understand and troubleshoot code effectively.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this Level 1 micro-badge, recipients demonstrated a foundational understanding of microcontroller programming concepts, such as setup and loop functions, variables, and logic structures. They applied basic programming logic and structures, including if-statements, for-loops, and while-loops, to design and troubleshoot microcontroller programs. Additionally, recipients showcased their ability to plan and represent processes using flowcharts, and effectively used debugging tools, such as breakpoints and the serial monitor, to identify and resolve code issues.
type: BadgeClass

id: https://api.badgr.io/public/badges/8_kklrE5QFOn1eCFDZgfcQ
name: Develop Recurrent Neural Networks with TensorFlow
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description: This digital credential certifies that the recipient successfully completed the "Develop Recurrent Neural Networks in TensorFlow" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated an intermediate understanding of recurrent neural networks (RNNs) and their applications to time-series data. They have showcased the ability to design, code, and debug RNN models using TensorFlow in a Python programming environment. Additionally, the recipient has analyzed the strengths, weaknesses, and practical uses of RNNs compared to other deep learning models such as CNNs.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This Level 2 micro-badge builds on foundational deep learning knowledge. To earn it, participants must demonstrate an understanding of time-series data, RNN architecture, and training. They must compare RNNs to other models like CNNs, highlighting strengths and applications, and successfully design, code, and debug RNN models using TensorFlow. Proficiency is validated through coding tasks and real-world use case applications.
type: BadgeClass

id: https://api.badgr.io/public/badges/7Q-Bwx4JROGE9qJ8EPn4zg
name: Deep Learning with TensorFlow
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description: This digital credential certifies that the recipient successfully completed the Deep Learning with TensorFlow Badge offered by Arizona State University. The holder of this credential has demonstrated proficiency in using TensorFlow to develop and deploy deep learning models, gaining both foundational and applied knowledge in neural network design and implementation. By completing four related micro-badges, the recipient has acquired skills in importing and utilizing pretrained models, creating custom CNNs for image classification, and designing RNNs for sequential and temporal data processing. These skills enable the recipient to apply TensorFlow to solve real-world artificial intelligence and machine learning challenges.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this badge, participants are required to complete four related micro-badges. Learners must demonstrate an ability to import pretrained models from TensorFlow modules, utilize these models for feature extraction and decision-making tasks, and apply foundational concepts of neural network architectures to build and implement Convolutional and Recurrent Neural Networks. Participants are assessed through hands-on coding exercises, ensuring they can use TensorFlow’s library functions and modules effectively for creating, training, and deploying advanced deep learning models.
type: BadgeClass

id: https://api.badgr.io/public/badges/wHBHcRsTS_GEUEZ-DLSfpg
name: Industrial Ultrasonic Testing I
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image alt text: Industrial Ultrasonic Testing I badge image
description: This digital credential certifies that the recipient completed the Industrial Ultrasonic Testing I Certificate from Arizona State University, demonstrating proficiency in Ultrasonic Testing (UT), a key nondestructive testing (NDT) method. By earning four microcredentials—Basic Principles of Materials Processing and Nondestructive Testing, Basic Principles of Ultrasonic Testing, Ultrasonic Testing Techniques, and Practical Training of Straight Beam Testing—the recipient gained expertise in material properties, defect detection, ultrasonic wave principles, and UT system operation. Through hands-on training and simulation tools, the recipient developed skills in calibrating UT equipment, performing straight beam and thickness testing, and analyzing ultrasonic data. This certificate qualifies as 40 hours of UT Level 1 training, aligning with ASNT CP105 and SNT-TC-1A certification guidelines, and prepares recipients for roles in oil and gas, aerospace, automotive, and aviation industries.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this certificate, participants are required to complete all four related microcredentials: Basic Principles of Materials Processing and Nondestructive Testing, Basic Principles of Ultrasonic Testing, Ultrasonic Testing Techniques, and Practical Training of Straight Beam Testing. Learners must demonstrate an understanding of material properties, manufacturing processes, and the basic principles of ultrasonic testing. They must also show proficiency in using UT equipment for straight beam testing, performing straight beam calibration, thickness measurements, and simulating ultrasonic testing scenarios using simulation software.
type: BadgeClass

id: https://api.badgr.io/public/badges/baxXH4-cTbOJ1eFhuy_O3g
name: Basic Principles of Materials Processing and Nondestructive Testing
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image alt text: Basic Principles of Materials Processing and Nondestructive Testing badge image
description: This digital credential certifies that the recipient successfully completed the "Basic Principles of Materials Processing and Nondestructive Testing" ASU Level 1 Microcredential offered by Arizona State University. The holder of this credential has demonstrated foundational knowledge in traditional and advanced manufacturing processes, material properties, and nondestructive testing (NDT) methods. The recipient is familiar with common defects associated with manufacturing processes and the role of NDT in evaluating materials without causing damage. This microcredential equips individuals with a general understanding of material testing methods and their applications in various industries.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 1, providing foundational knowledge of materials processing, material properties, and nondestructive testing methods. To earn this microcredential, participants must demonstrate an understanding of traditional and advanced manufacturing processes, identify defects associated with these processes, and explain the relationship between material properties and destructive testing. Additionally, recipients must show their knowledge of the most common nondestructive testing methods including ultrasonic testing (UT), radiography testing (RT), eddy current testing (ET), acoustic emission (AE), mag-particle testing (MT), and penetrant testing (PT), their applications, and limitations. The microcredential includes content on casting, forging, welding, and post-processing methods, along with an introduction to over the 6 NDT methods. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

id: https://api.badgr.io/public/badges/gNhVqb9gSzC0N7OdEYxLuA
name: Basic Principles of Ultrasonic Testing
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image alt text: Basic Principles of Ultrasonic Testing badge image
description: This digital credential certifies that the recipient successfully completed the "Basic Principles of Ultrasonic Testing (UT)" ASU Level 2 Microcredential offered by Arizona State University. The holder of this credential has demonstrated foundational knowledge of ultrasonic testing (UT) methods, including the physical theory behind sound wave propagation, the equipment used in UT, and the types of ultrasound waves involved. The recipient is also familiar with the functionality of UT systems, including transducers, amplifiers, and data acquisition systems. This microcredential equips individuals with essential skills for applying ultrasonic testing in various industries.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 2, providing foundational knowledge of the physical principles behind ultrasonic testing, along with an understanding of the equipment used in UT. To earn this microcredential, participants must demonstrate their understanding of sound wave theory, the functionality of UT systems, and the ability to identify and utilize different types of ultrasound wave modes. Participants should also be able to simulate an ultrasound beam and explain the principles of various UT methods, including pulse-echo testing and immersion UT using simulation software. This includes an understanding of key concepts such as acoustic impedance, Snell’s law, and the effects of reflection, refraction, and mode conversion. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

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name: Ultrasonic Testing Techniques
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image alt text: Ultrasonic Testing Techniques badge image
description: This digital credential certifies that the recipient successfully completed the "Ultrasonic Testing Techniques" ASU Level 2 Microcredential offered by Arizona State University. The holder of this credential has demonstrated proficiency in various ultrasonic testing techniques, including contact straight beam testing for thickness measurement and corrosion inspection. The recipient has gained expertise in simulating ultrasonic testing using advanced software and has participated in cyber hands-on training, reinforcing their understanding of beam intensity, resolution, sensitivity, and the interaction of ultrasound with discontinuities. This microcredential prepares individuals to apply ultrasonic testing in real-world scenarios effectively.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 2, providing intermediate knowledge of ultrasonic testing techniques, including straight beam and angle beam testing. To earn this microcredential, participants must demonstrate their understanding of ultrasound beam parameters, sensitivity, resolution, and the interaction of ultrasound with different discontinuities. This includes successfully identifying and applying test standards, utilizing ultrasonic simulation tools, and performing cyber hands-on training to reinforce practical skills. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

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name: Practical Training of Straight Beam Testing
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image alt text: Practical Training of Straight Beam Testing badge image
description: This digital credential certifies that the recipient successfully completed the "Practical Training of Straight Beam Testing" ASU Level 2 Microcredential offered by Arizona State University. The holder of this credential has gained hands-on experience and proficiency in performing contact straight beam ultrasonic testing (UT) for thickness measurement, corrosion inspection, and composite material evaluation. The recipient has demonstrated the ability to calibrate UT equipment, perform tests on metallic and composite materials, and evaluate and report ultrasonic data according to industrial standards. This microcredential signifies the application of theoretical knowledge into practical skills using both cyber hands-on tools and state-of-the-art UT systems.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 2, emphasizing practical application and hands-on training in ultrasonic testing. To earn this microcredential, participants must demonstrate the ability to perform calibration on UT equipment, conduct thickness testing on metallic and composite materials, and perform corrosion and composite inspections. Participants will also need to evaluate and report ultrasonic testing data accurately. Successful completion involves using both cyber tools and real-world UT systems to follow industry-standard practices. This includes performing straight beam distance calibration, using DAC and TCG for data analysis, and producing clear reports based on test results. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

id: https://api.badgr.io/public/badges/KsjBe1z3TKeml9mlhaj94Q
name: Industrial Ultrasonic Testing II
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image alt text: Industrial Ultrasonic Testing II badge image
description: This digital credential certifies that the recipient successfully completed the Industrial Ultrasonic Testing II Certificate offered by Arizona State University. The holder of this credential has demonstrated proficiency in advanced Ultrasonic Testing (UT) techniques, focusing on weld inspection, corrosion detection, and thickness measurement across industries such as oil and gas, aerospace, automotive, and aviation. By completing all four microcredentials—Ultrasonic Imaging and Angle Beam Testing, Ultrasonic Testing for Defect Detection, Fundamentals of Amplitude-Based UT Sizing, and Practical Training of Straight and Angle Beam UT Evaluation—the recipient has acquired skills in simulating and performing angle beam UT, detecting defects in welds, applying amplitude-based UT sizing methods, and operating state-of-the-art UT systems for real-world applications. This certificate qualifies as 40 hours of UT Level 2 training, recognized by certification guidelines CP105 and SNT-TC-1A from the American Society for Nondestructive Testing (ASNT).
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this certificate, participants are required to complete all four related microcredentials: Ultrasonic Imaging and Angle Beam Testing, Ultrasonic Testing for Defect Detection, Fundamentals of Amplitude-Based UT Sizing, and Practical Training of Straight and Angle Beam UT Evaluation. Learners must demonstrate their ability to operate advanced UT simulation software, perform calibration on UT equipment, and apply ultrasonic imaging for defect detection and weld inspection. Participants are assessed through both theoretical understanding and practical exercises, where they will simulate and apply amplitude-based UT sizing methods (e.g., DAC or TCG) and evaluate ultrasonic data to meet industry standards. This hands-on training ensures that learners can effectively use UT techniques for real-world industrial applications, including weld testing, corrosion detection, and material thickness measurements.
type: BadgeClass

id: https://api.badgr.io/public/badges/V5EzzJR9QtKqVCYBp6t8jA
name: Ultrasonic Imaging and Angle Beam Testing
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image alt text: Ultrasonic Imaging and Angle Beam Testing badge image
description: This digital credential certifies that the recipient successfully completed the "Ultrasonic Imaging and Angle Beam Testing" ASU Level 3 Microcredential offered by Arizona State University. The holder of this digital credential has gained foundational knowledge in ultrasonic imaging and scanning techniques, including angle beam ultrasonic testing (UT). The recipient has demonstrated the ability to understand and simulate angle beam UT, a critical skill in assessing internal structures and defects within materials.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 3, providing foundational knowledge in ultrasonic imaging and angle beam ultrasonic testing. To earn this microcredential, the recipient demonstrated an understanding of ultrasound imaging principles and the use of angle beam UT for evaluating materials. The recipient also successfully simulated angle beam UT in practical scenarios, reinforcing their comprehension of these key techniques. This microcredential is designed to prepare the recipient for further advanced ultrasonic testing applications. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

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name: Ultrasonic Testing for Defect Detection
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image alt text: Ultrasonic Testing for Defect Detection badge image
description: This digital credential certifies that the recipient successfully completed the "Ultrasonic Testing for Defect Detection" ASU Level 3 Microcredential offered by Arizona State University. The holder of this digital credential has demonstrated foundational knowledge and practical skills in using ultrasonic testing (UT) for defect detection, specifically focusing on applications in welds and shear wave inspections. The recipient is proficient in identifying various defects such as porosity, cracks, lack of fusion, and delamination in composite materials, using advanced simulation software and hands-on tools to evaluate material integrity in real-world scenarios.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 3, providing foundational knowledge and skills in ultrasonic testing for defect detection. To earn this microcredential, the recipient demonstrated an understanding of the basic principles of angle beam UT and ultrasonic imaging techniques for detecting defects. The recipient successfully completed UT simulations for weld testing, identifying defects such as cracks, porosity, and lack of fusion in simulated scenarios. They applied these concepts to evaluate materials using shear wave inspections and ultrasonic imaging. Successful completion was assessed through hands-on simulations and lab exercises, confirming the recipient’s ability to effectively apply ultrasonic testing for defect detection. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

id: https://api.badgr.io/public/badges/8PzxD2RiSNS0GiSGpmGV8w
name: Fundamentals of Amplitude-Based UT Sizing
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image alt text: Fundamentals of Amplitude-Based UT Sizing badge image
description: This digital credential certifies that the recipient successfully completed the "Fundamentals of Amplitude-based UT Sizing" ASU Level 3 Microcredential offered by Arizona State University. The holder of this digital credential has acquired foundational knowledge and practical skills in the widely-used defect sizing methods in ultrasonic testing (UT), including Distance Amplitude Correction (DAC), Time-Corrected Gain (TCG), and Distance Gain Size (DGS). The recipient is proficient in applying these methods in various applications, such as forging, casting, welding, and advanced manufacturing processes, using advanced simulation software and cyber hands-on training to reinforce their understanding.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 3, providing foundational knowledge and skills in amplitude-based ultrasonic testing sizing methods. To earn this microcredential, the recipient demonstrated a fundamental understanding of DAC, TCG, and DGS methods used in ultrasonic testing. The recipient successfully simulated amplitude-based ultrasound testing, applying these sizing techniques to real-world scenarios through advanced simulation software. Additionally, they showcased their knowledge of relevant test standards, reinforcing their understanding through cyber hands-on training and exercises. This demonstrated the recipient’s ability to effectively utilize these methods in evaluating and sizing defects in materials used in forging, casting, welding, composites, and advanced manufacturing. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

id: https://api.badgr.io/public/badges/fp78jXk1S7WomzYqeAoBVQ
name: Practical Training of Straight and Angle Beam UT Evaluation
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image alt text: Practical Training of Straight and Angle Beam UT Evaluation badge image
description: This digital credential certifies that the recipient successfully completed the "Practical Training of Straight and Angle Beam UT Evaluation" ASU Level 3 Microcredential offered by Arizona State University. The holder of this digital credential has acquired practical knowledge and skills in performing straight and angle beam ultrasonic testing (UT) evaluations, including weld inspections and shear wave inspections. The recipient has gained the knowledge on Time of Flight Diffraction (TOFD), guided waves, and phased array ultrasonic testing technologies. The recipient has demonstrated the ability to utilize advanced ultrasonic software and testing systems and cyber hands-on tools to evaluate real-world samples containing internal discontinuities, such as cracks, while adhering to industry standards.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This microcredential is offered at ASU Level 3, providing foundational knowledge and practical skills in performing straight and angle beam ultrasonic testing evaluations. To earn this microcredential, the recipient demonstrated the ability to simulate TOFD and phased array in UT software as well as calibrate UT equipment, perform weld inspections using angle beam techniques, and evaluate ultrasonic data for internal discontinuities, such as lack of fusion, lack of penetration, porosities as well as cracks. The recipient also successfully reported their findings according to common industry standards. This was achieved through hands-on training using advanced ultrasonic testing systems and cyber tools, reinforcing their practical skills for real-world applications. The categorization of levels follows the ASU microcredential description and is not associated with the SNT-TC-1A levels.
type: BadgeClass

id: https://api.badgr.io/public/badges/Qn81vlDISRaj4u4ikOMP_Q
name: Microcontroller Sensors and Actuators
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description: This digital credential certifies that the recipient successfully completed the "Microcontroller Sensors and Actuators" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated the ability to integrate sensors and actuators into microcontroller systems, control motors using H-bridges, and differentiate between analog and digital signals. They have applied these skills through hands-on labs to design and implement simple microcontroller-based control systems.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2. Level 2 micro-badges empower learners to apply their foundational knowledge through hands-on practice, allowing them to demonstrate their skills with tangible evidence. To earn this micro-badge, participants must demonstrate their ability to differentiate between analog and digital sensors and apply appropriate usage techniques for each. They must successfully integrate sensors and actuators into a microcontroller-based system, including selecting, mounting, and controlling various actuators such as DC motors, servos, and linear actuators. Participants must also implement an H-bridge circuit to control motor speed and direction and use a microcontroller to process sensor input, adjusting actuator behavior accordingly. These skills are assessed through hands-on labs and activities that require learners to design, implement, and troubleshoot functional sensor-actuator control systems.
type: BadgeClass

id: https://api.badgr.io/public/badges/zjPvb0ppQQmkmx16ceNQMw
name: Introduction to Industrial Robotics: Fundamentals
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description: This digital credential certifies that the recipient successfully completed the "I2R: Fundamentals" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated knowledge of industrial robotics, including robot configurations, motion control, and safety standards. They have also gained hands-on experience in programming and controlling basic industrial robot tasks while applying risk assessment and safety measures in simulated environments.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2, providing applied knowledge of industrial robotics, robot programming, and safety standards. To earn this micro-badge, participants must demonstrate their ability to program and control basic industrial robot tasks, assess risks in robotic workcells, and apply safety standards in a simulated environment. This includes successfully programming an industrial robot for tasks such as pick-and-place, welding, or material handling, conducting a risk assessment for a robotic workcell by identifying hazards and proposing mitigation strategies, analyzing a case study on industrial robotics applications to evaluate the benefits of automation, and implementing ISO 10218 safety standards in a simulated work environment.
type: BadgeClass

id: https://api.badgr.io/public/badges/JL4JlOicTbmOkIlv-MHZww
name: Semiconductors and Junctions
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description: This Level 3 microcredential provides a foundational understanding of semiconductor materials and crystal structures and how they influence device performance. Learners explore charge carrier dynamics, P-N junction behavior, and band diagrams, along with key concepts such as diode operations under various bias conditions. Practical insights into industry applications, fabrication techniques, and metal-semiconductor junctions prepare learners for advanced studies in semiconductor devices.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your ability to analyze crystal structures, charge carrier dynamics, and P-N junction behavior, and apply these concepts to evaluate device performance, fabrication techniques, and industry applications of semiconductor junctions.
type: BadgeClass

id: https://api.badgr.io/public/badges/DI5cS5zMS0i2FYFOxxOMig
name: Semiconductor MOS Devices
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description: This Level 3 microcredential covers foundational concepts in semiconductor device physics, focusing on field-effect transistors (FETs), MOS capacitors, and MOSFETs. Learners will explore device operation, threshold voltage, and advanced effects such as tunneling and oxide breakdown, gaining essential insights into long-channel device characteristics and industry applications.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on the assessments. These assessments will test your ability to analyze FETs, MOS capacitors, and MOSFETs, explain device operation and threshold voltage behavior, interpret band diagrams and C-V curves, and evaluate advanced effects such as tunneling, oxide breakdown, and subthreshold characteristics in long-channel MOSFET devices.
type: BadgeClass

id: https://api.badgr.io/public/badges/Q9KIurWiSoiILoYRFXKhQQ
name: Advanced MOS Devices 1
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description: This Level 4 microcredential deepens learners' expertise in MOSFET physics and device behavior through the analysis of I-V curves and their responses to variations in doping, capacitance, and bias conditions. Learners will explore advanced scaling techniques, second-order effects, and concepts such as DIBL and GIDL, applying models to optimize performance and address real-world semiconductor design challenges.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on assessments. These assessments will evaluate the ability to analyze MOSFET I-V curves, model advanced device architectures, assess second-order effects like DIBL and GIDL, and apply scaling techniques to optimize device performance.
type: BadgeClass

id: https://api.badgr.io/public/badges/LC2zEvHMSAiRVjhbanz4pA
name: Advanced MOS Devices 2
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description: This Level 4 microcredential equips learners with a practical understanding of MOSFET reliability, advanced scaling techniques, and innovations like FinFETs and Gate-All-Around architectures. Participants will explore reliability concerns such as hot carrier effects, latch-up, and oxide breakdown, while analyzing memory and logic applications. The course emphasizes strategies to enhance performance and reliability, highlighting design and fabrication trade-offs in modern semiconductor technologies.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this micro-badge, participants must complete all course sessions and achieve a score of 70% or higher on assessments. These assessments will evaluate the ability to analyze MOSFET reliability issues, interpret I-V characteristics for memory and logic applications, compare FinFET and GAA architectures, and apply advanced fabrication techniques to optimize device performance and longevity.
type: BadgeClass

id: https://api.badgr.io/public/badges/HHYyNb8_Sh-EF_fND-24kA
name: Applied Semiconductor Physics for Device Engineering
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description: The Applied Semiconductor Physics for Device Engineering microcredential is designed for device engineers and semiconductor professionals, providing comprehensive training in semiconductor theory and practical device physics. This program equips participants with the skills needed to analyze and address unexpected device behavior by applying foundational knowledge of device physics to real-world scenarios. Through the completion of four micro-badges, learners will deepen their understanding of semiconductor devices, including MOSFETs and advanced scaling techniques, while strengthening their ability to resolve issues and optimize processes. By integrating theoretical knowledge with hands-on application, this microcredential prepares professionals to meet the challenges of modern semiconductor device engineering.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  id: https://asuengineeringonline.com/stackable-microcredentials
  narrative: To earn this full microcredential, participants must complete all course sessions and pass the four associated micro-badges. This includes achieving a score of 70% or higher on all assessments across each micro-badge, covering topics such as semiconductor junctions, MOS devices, and advanced MOSFETs. Participants must demonstrate their ability to apply device physics concepts to real-world challenges and optimize semiconductor processes.
type: BadgeClass

id: https://api.badgr.io/public/badges/9-4nxIlnRyeYm3iNODOkMg
name: Assistive Technology Microcontroller Applications
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description: This digital credential certifies that the recipient successfully completed the "Assistive Technology Microcontroller Applications" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated the ability to apply the engineering design process to develop an assistive technology prototype utilizing a microcontroller. Through hands-on activities, they have explored prototyping strategies, material selection, and iterative design to create functional solutions for real-world accessibility challenges.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2, building on foundational knowledge of microcontrollers, electronics, and programming. To earn this micro-badge, participants must integrate and apply skills from prior coursework to develop a real-world assistive technology project using a microcontroller. They must demonstrate proficiency in prototyping, material selection, and iterative design by successfully designing, building, and presenting a functional prototype.
type: BadgeClass

id: https://api.badgr.io/public/badges/mZoFGEFmTbGTx38NcstzZQ
name: Microcontroller Applications: Programming and Electrical Basics to Integration
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description: This digital credential certifies that the recipient successfully completed the Microcontroller Applications Badge offered by Arizona State University. The holder of this credential has demonstrated proficiency in applying microcontrollers to real-world engineering challenges, from basic circuit design to advanced assistive technology applications. By completing all four micro-badges—Introduction to Microcontrollers and Electronics, Microcontroller Programming Basics, Microcontroller Sensors and Actuators, and Assistive Technology Microcontroller Applications—the recipient has gained hands-on experience in electrical circuits, microcontroller programming, sensor integration, and prototyping. These skills equip the recipient to develop intelligent, real-world solutions utilizing microcontroller systems.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this badge, participants are required to complete all four related micro-badges: Introduction to Microcontrollers and Electronics, Microcontroller Programming Basics, Microcontroller Sensors and Actuators, and Assistive Technology Microcontroller Applications. Learners must demonstrate their ability to design, program, and integrate microcontroller-based systems, progressing from fundamental electrical and coding concepts to advanced assistive technology applications. Participants are assessed through simulations, hands-on labs, debugging exercises, and prototyping projects, ensuring they can effectively develop and implement microcontroller-driven solutions.
type: BadgeClass

id: https://api.badgr.io/public/badges/XPSgk4PcRCybMbVjClj7-w
name: Analyzing the Movement of Industrial Robotic Arms: Kinematics
image:
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description: This digital credential certifies that the recipient successfully completed the “Analyzing the Movement of Industrial Robotic Arms: Kinematics” Level 2 Micro-badge offered by Arizona State University. The holder of this credential has demonstrated applied knowledge of robotic arm motion through kinematic modeling, including forward and inverse kinematics, differential kinematics, and trajectory planning. Using tools like MATLAB and Simulink, the recipient has engaged in both analytical and hands-on exercises to simulate and solve real-world robotic motion problems.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2, providing applied knowledge of robotic arm motion through the study of kinematics and trajectory planning. To earn this micro-badge, participants must demonstrate their ability to perform forward and inverse kinematics, apply differential kinematics using Jacobian analysis, and model robotic arm motion in simulation. This includes successfully completing a final project involving trajectory planning and obstacle avoidance using MATLAB and Simulink.
type: BadgeClass

id: https://api.badgr.io/public/badges/PNnHZdfHRiyJe8xGQ70elw
name: Understanding the Forces and Motion in Robotic Arms: Dynamics
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description: This digital credential certifies that the recipient successfully completed the "Understanding the Forces and Motion in Robotic Arms: Dynamics" Level 2 Micro-badge offered by Arizona State University. The holder of this digital credential has demonstrated the ability to apply core concepts of robot dynamics to analyze the forces and motion within serial-link robotic arms. This includes deriving equations of motion using Lagrangian mechanics and utilizing MATLAB and Simulink to evaluate robotic behavior under defined operating conditions.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: This micro-badge is offered at Level 2, emphasizing the application of foundational robotics knowledge in practical and structured scenarios. To earn this micro-badge, participants must demonstrate their ability to analyze and model the dynamic behavior of robotic arms using appropriate mathematical tools and software. Learners complete guided lab activities, engage in peer feedback, and apply their skills through a final project involving the computation of joint forces and torques for a robotic arm following a specified trajectory.
type: BadgeClass

id: https://api.badgr.io/public/badges/eu_kfo0iRgadFVch_ZSiIA
name: Materials Characterization with Scanning Electron Microscopy
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image alt text: Materials Characterization with Scanning Electron Microscopy badge image
description: This microcredential certifies that the recipient has completed training in the principles and practical use of scanning electron microscopy (SEM) for materials characterization. Participants learn the underlying physics of SEM, sample preparation methods for metals, polymers, and ceramics, and safe operation of a benchtop SEM. Through guided laboratory activities, learners acquire and interpret SEM images to evaluate material structure and properties.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this badge, participants must successfully complete the ten-hour course, including three live instructional sessions and hands-on laboratory activities. Learners demonstrate competency in SEM fundamentals, sample preparation techniques, safe operation of a benchtop SEM, and acquisition of high-quality images for materials analysis.
type: BadgeClass

id: https://api.badgr.io/public/badges/ANB2PMIGRemPzf952vlKNw
name: Introduction to Root Cause Analysis
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image alt text: Introduction to Root Cause Analysis badge image
description: This microcredential certifies that the recipient has completed training in foundational root cause analysis (RCA) methods for structured problem solving. Participants learn to define clear problem statements, collect and interpret relevant data, and apply tools such as the 5 Whys and Fishbone Diagram to identify underlying causes. Through applied case-based activities, learners develop practical skills to distinguish between symptoms and root causes in real-world scenarios.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this microcredential, participants must successfully complete all course sessions, applied activities, and a final project. Learners demonstrate competency by defining structured problem statements, collecting and analyzing qualitative and quantitative data, and applying RCA tools (including 5 Whys and Fishbone Diagrams) to identify contributing factors and root causes. The final project requires learners to conduct a complete root cause analysis on a structured case scenario, integrating all course concepts. All work must meet established evaluation criteria, with opportunities for feedback and revision to achieve proficiency.
type: BadgeClass

id: https://api.badgr.io/public/badges/yMT6IOAiT-e2c8OuO3bKkg
name: Industry 4.0 Foundation for Semiconductor Manufacturing
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image alt text: Industry 4.0 Foundation for Semiconductor Manufacturing badge image
description: The Industry 4.0 Foundations for Semiconductor Manufacturing (SACA101-A) badge recognizes learners who have demonstrated essential skills for safe and effective participation in modern manufacturing environments. Earners have foundational knowledge of workplace safety practices, hazard communication, measurement tools, and technical documentation, along with an introduction to automation and Industry 4.0 concepts. This credential signals readiness for entry-level roles and further training in advanced manufacturing, robotics, and automation systems.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this badge, learners must successfully complete the microcredential by demonstrating competency in the following areas: Identify appropriate personal protective equipment (PPE) and interpret key information from Safety Data Sheets (SDS) Explain the purpose and basic procedures of Lockout/Tagout (LOTO) Accurately measure and record dimensions using tape measures and machinist rules Perform basic unit conversions between inches and millimeters Interpret technical drawings, including title blocks, revisions, materials, and basic tolerances Apply safety, measurement, and documentation skills in an integrated, real-world scenario assessment Learners must complete all required activities and pass the final practical assessment to receive the credential.
type: BadgeClass

id: https://api.badgr.io/public/badges/uf0o9vykTMGzseDQc-xuSg
name: Industry 4.0 Operations for Semiconductor Manufacturing
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image alt text: Industry 4.0 Operations for Semiconductor Manufacturing badge image
description: The Industry 4.0 Operations for Semiconductor Manufacturing (SACA101-B) microcredential recognizes learners who have demonstrated foundational operational skills used in automated manufacturing environments. Earners have developed competency in interpreting HMI data, responding to machine alarms, understanding preventive maintenance practices, and applying basic automation concepts related to PLCs, robotics, and pneumatics. This credential signifies readiness for entry-level technician pathways in advanced manufacturing, semiconductor operations, and industrial automation.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this microcredential, learners must successfully demonstrate competency in the following areas: Interpret HMI production metrics such as cycle time, throughput, and reject counts Classify machine alarm types and identify appropriate first-response actions Navigate and interpret basic machine and operational documentation Explain preventive maintenance concepts and lubrication fundamentals Demonstrate awareness of basic PLC, electrical controls, robotics, and pneumatic systems Apply troubleshooting and operational decision-making skills in an integrated automation scenario Learners must complete all required course activities and successfully pass the final practical assessment to receive the credential.
type: BadgeClass

id: https://api.badgr.io/public/badges/F_HDhl68SiSNfCoqVMbzAw
name: Fundamentals of Vacuum Technology & Practices
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image alt text: Fundamentals of Vacuum Technology & Practices badge image
description: The Fundamentals of Vacuum Technology & Practices microcredential recognizes learners who have demonstrated foundational knowledge and practical skills related to industrial vacuum systems and applications. Earners of this credential have explored vacuum theory, vacuum levels, pumps, sensors, fittings, and contamination-control practices while gaining hands-on experience assembling and testing a functional vacuum chamber. Participants developed industry-relevant skills in measuring vacuum system performance, interpreting pump-down data, and understanding the role of vacuum technology in modern manufacturing and semiconductor industries.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this microcredential, learners successfully completed both knowledge-based and hands-on assessments related to vacuum technology fundamentals and practices. Participants demonstrated the ability to: Explain foundational vacuum concepts including Boyle’s Law, conductance, vacuum levels, and industry terminology Identify common vacuum pumps, sensors, fittings, and system components and describe their functions Understand contamination-control concepts including cleanliness and bake-out procedures Follow standard operating procedures (SOPs) to assemble and operate a vacuum chamber system Collect, measure, and interpret vacuum system data including pump-down curves and rate-of-rise measurements Demonstrate safe and effective operation of vacuum equipment in a laboratory environment Successfully complete a written assessment and hands-on vacuum chamber activity demonstrating practical competency in vacuum system assembly and analysis
type: BadgeClass

id: https://api.badgr.io/public/badges/6y4j8BLvRGankPneX0hEsA
name: Clean Room Standard Operating Procedures & Fundamentals
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image alt text: Clean Room Standard Operating Procedures & Fundamentals badge image
description: The Clean Room Standard Operating Procedures & Fundamentals microcredential recognizes learners who have demonstrated foundational knowledge and practical skills required to work safely and effectively in controlled cleanroom environments. Earners of this badge have explored contamination control principles, cleanroom classifications, safety procedures, personal protective equipment (PPE), and standard operating procedures (SOPs) commonly used in industries such as semiconductor manufacturing, biotechnology, pharmaceuticals, aerospace, and medical device production. Participants also gained hands-on experience practicing proper gowning procedures, cleanroom behavior, and contamination-reduction techniques in a cleanroom laboratory setting.
issuer: https://api.badgr.io/public/issuers/krV7W0CFSUOgzvdGuCX1sQ
criteria:
  narrative: To earn this badge, learners successfully completed both knowledge-based and hands-on assessments related to cleanroom operations and safety practices. Participants demonstrated the ability to: Explain the purpose and importance of cleanroom environments and contamination control Identify common contamination sources and apply contamination-reduction techniques Demonstrate proper gowning and de-gowning procedures while following cleanroom SOPs Follow cleanroom entry, exit, movement, and workplace behavior protocols Identify and properly use PPE commonly required in controlled environments Demonstrate awareness of cleanroom safety practices, including ESD, emergency procedures, SDS, GHS labeling, and TO/LO procedures Handle tools, materials, and components appropriately while maintaining cleanroom standards Successfully complete a written/verbal assessment and hands-on cleanroom activities demonstrating practical competency in controlled-environment procedures and compliance
type: BadgeClass