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DMU Courses

Data Literacy Master Course (DL200)


Description
Duration: Nineteen (19) chapters, short chapter quizzes, final exam

This course guides individuals in the study of the major areas of data literacy. The purpose of this course is to help the learner become a master data literacy professional. Learners that can successfully pass each chapter's short quiz and the course's final exam will become a certified Data Literacy Master through Data Management University.

Course Content
• Data Literacy Master Class Course Overview
• Background & Fundamentals
• Return on Investment (ROI)
• Data Literacy Levels, Roles, & Personas
• Enterprise Data Management
• Data Governance
• Data Quality Management
• Metadata Management
• Data Ethics
• Data Analysis Terms and Concepts
• Data Analysis Distribution and Testing
• Data Structures & Data Traits
• Qualitative and Quantitative Data
• Types of Graphs and Charts
• Common Data Analysis Mistakes
• Storytelling with Data – 1
• Storytelling with Data – 2
• Data Literacy Socialization & Communication
• Conclusion, Summary, and Final Exam
Content
  • 1. Data Literacy Course Overview
  • Chapter 1 - Data Literacy Master Class Course Overview
  • 2. Data Literacy Background and Fundamentals
  • Chapter 2 - Backgrounds and Fundamentals
  • Chapter 2 Questions – Data Literacy Background
  • 3. Data Literacy Return on Investment
  • Chapter 3 - Data Literacy Return on Investment
  • Chapter 3 Quiz – Return on Investment
  • 4. Data Literacy Levels, Roles, and Personas
  • Chapter 4 - Data Literacy Level,s Roles, and Personas
  • Chapter 4 Quiz– Data Literacy Levels Roles and Personas
  • 5. Enterprise Data Management
  • Chapter 5 - Enterprise Data Management
  • Chapter 5 – Enterprise Data Management
  • 6. Data Governance
  • Chapter 6 - Data Governance
  • Chapter 6 – Data Governance
  • 7. Data Quality Management
  • Chapter 7 - Data Quality Management
  • Chapter 7 – Data Quality Management
  • 8. Metadata Management
  • Chapter 8 - Metadata Management
  • Chapter 8 – Metadata Management
  • 9. Data Ethics
  • Chapter 9 - Data Ethics
  • Chapter 9 – Data Ethics
  • 10. Data Analysis Terms and Concepts
  • Chapter 10 - Data Analysis Terms and Concepts
  • Chapter 10 – Data Analysis Terms
  • 11. Data Analysis Distribution and Testing
  • Chapter 11 - Data Analysis Distribution and Testing
  • Chapter 11 – Data Analysis Distributing and Testing
  • 12. Data Structures and Data Traits
  • Chapter 12 - Data Structures and Data Traits
  • Chapter 12 – Data Structures and Traits
  • 13. Qualitative and Quantitative Data
  • Chapter 13 - Qualitative and Quantitative Data
  • Chapter 13 – Qualitative and Quantitative Data
  • 14. Types of Visualizations, Graphs, and Charts
  • Chapter 14 - Types of Graphs and Charts
  • Chapter 14 – Types of Graphs and Charts
  • 15. Common Data Analysis Mistakes
  • Chapter 15 - Common Data Analysis Mistakes
  • Chapter 15 – Common Data Analysis Mistakes
  • 16. Storytelling with Data - Part 1
  • Chapter 16 - Storytelling with Data - Part 1
  • Chapter 16 – Storytelling with Data Basics
  • 17.Storytelling with Data - Part 2
  • Chapter 17 - Storytelling with Data - Part 2
  • Chapter 17 – Storytelling with Data, Data & Visual Design
  • 18. Data Literacy Socialization and Communication
  • Chapter 18 - Socialization and Communication
  • Chapter 18 – Data Literacy Socialization & Communication
  • 19. Conclusion, Summary and Final Exam
  • Chapter 19 - Conclusion Summary and Final Exam
  • Data Literacy Final Exam
Completion rules
  • All units must be completed
  • Leads to a certificate with a duration: Forever
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