Fundamentals of Data Literacy

12 hours of instruction

This 12-hour workshop educates participants on the fundamentals of data science and how to apply them in such a way that it is relevant even to those who will neither manage nor consume data regularly. Attendees will learn: data science concepts and associated terminology; why data science is important; what it means to work in a data-driven culture including the skills necessary to thinking critically about data; common issues in data collection and analysis such as bias, data gaps, and imprecision; strategies for interpreting data visualizations produced by others; and foundational steps those who are not data scientists can take to incorporate data analysis into their work.

OBJECTIVES

1. Explain what it means to work in a data-driven culture
2. Discuss data science concepts and terms
3. Interpret data visualizations to make strategic decisions

PREREQUISITES

No background in math or data analysis is required.

SYLLABUS & TOPICS COVERED

1. Creating a data-driven culture
   • Data-driven decision making
   • The data science process
   • Choosing feasible and meaningful projects
   • Designing and revising data projects
2. The benefits of data
   • Introduction to data analytics
   • Data governance strategy
   • Selecting data tools
   • Structuring data teams
3. Foundational data science methods
   • The basics of machine learning
   • Clustering and its uses
   • Classification and its uses
   • Regression and its uses
   • Questions to ask about data science processes
4. Advanced data science methods
   • Text mining and its uses
   • Graph analysis and its uses
   • Neural networks and their uses
   • Questions to ask about data science processes
5. Principles of data visualization
   • The impact of data visualization
   • Selecting charts and graphs to present results
   • Tailoring visualizations to an audience
   • Designing data visualizations
   • Recognizing misleading and inaccurate visualizations

SOFTWARE REQUIREMENTS

None