Fundamentals of Data Literacy Turkmenistan Eğitimi

Data literacy is a capability to read, use and analyse data to produce information that can be acted upon. Every organisation creates and stores data but not everyone has the skills and confidence to work with it.

This course is designed for anyone who is not a data specialist but has a need to work with data in their role.

In this course you’ll gain an understanding of the role data can play, data structures, data quality and how you can use data to produce analysis to turn data into information and create a data story.

Examples, demonstrations and exercises may use a selection of common applications including Microsoft Excel and Google Sheets.

• Understand how to write formulas using standard functions
• Experience of creating or using charts and graphs

At the end of this course, you’ll be able to:

• Identify data types and structures
• Work with data quality challenges
• Apply a structured approach to work with data
• Create a testable hypothesis
• Use fundamental analytics and statistical approaches
• Select appropriate visuals
• Build an effective data story

Module 1: Data Literacy and its Role

• Data is Everywhere
• Data-driven Culture
• Creating Value from Data

Module 2: Data Management

• Enterprise Data Management
• Data Governance and Ethics
• Data Collection

Module 3: Data Quality Management

• Identifying Data Quality Issues
• Data Quality Metrics
• Common Data Challenges
  • Incomplete Datasets
  • Duplication
  • Data Type and Formatting Issues

Module 4: Data Structures and Data Traits

• Data Types
• Tidy Data
• Quantitative and Qualitative Data
• Data Scales
• Data Models
  • Facts and Dimensions
  • Relationships and Joins

Module 5: Data Analysis Process, Terms and Concepts

• Data Analytics Lifecycle
• Business Problems and Hypothesis
  • A structured approach
  • Stakeholders’ Perspective
  • Creating a Hypothesis
  • Determining the Data You Need
  • Defining Success
• Types of Analytics
• Different types of Average
• Beyond averages
  • Distribution and Deviation
  • Percentile, Quartiles and Rank

Module 6: Data Analysis and Testing

• Regression Analysis
• Correlation and Causation
• Testing your Hypothesis
  • A/B, Control and Multivariate
• Preparing to tell the Data Story

Module 7: Visuals and Charts

• Chart Types
• Visualisation and Statistics
• Matching the Visual to the Data
• Drawing attention to Key Insights
• Focussing on the Difference

Module 8: Storytelling with Data

• What is data storytelling?
• Three key questions
• Context and Empathy
• A Data Story in Three Acts
• Defining Success Criteria
• Dashboards – Types and Design Considerations

Module 9: Data Literacy Socialisation and Communication

• Data-Led Change
• Feedback Loops