EDF Data Literacy Professional Certification Exam

This is only the EDF Data Literacy Professional Certification Exam. The course that will prepare you for this exam is EDF Data Literacy Professional.
The EDF Data literacy certification is part of the Effective Data Foundation portfolio. It signifies an individual’s comprehensive understanding and awareness of data literacy, enabling them to foster its adoption and application for informed decision-making.
Certified candidates have demonstrated their ability to comprehend the nature of data, ask critical questions, summarize data accurately, and understand the processes involved. They possess knowledge of data quality, utilize data for performance measurement, and are conscious of the impact of expectations and biases in analysis. Certified candidates recognize human biases, understand different types of data analysis, consider contextual factors, and apply storytelling principles to communicate data arguments effectively. The following table is an overview of the topics examined in the certification exam.



#questions
Level(s) of Cognition
Category


1
2
3


15
✓
✓

Reading data

Describe the key properties of data
Describe how to summarize data
Recognize typical pitfalls when consuming data
Describe the main questions to check any data expression




15
✓
✓

Working with data

Describe how data is created
Describe the differences between machine and human generated data
Describe the differences between mandatory and optional data
Recognize the data quality dimensions
Recognize the best data structure for analysis
Describe the three basic data cleaning phases
Describe how to define performance measures




15
✓
✓

Analyzing data

Recognize the impact of our expectations on our analysis results
Describe the principal thinking shortcuts
Describe the main types of analysis
Describe the main analytical skills




15
✓
✓

Arguing with data

Describe the key properties of the Explore phase
Describe the key properties of the Explain phase
Recognize the activities belonging to a specific phase
Describe the typical data forgeries
Describe the main steps in the data storytelling arc