Data Visualization
Why?
Data visualization is a critical skill for data scientists that transforms complex data into intuitive visual formats. Strong visualization skills help you communicate insights effectively to both technical and non-technical audiences, identify patterns that might be missed in raw data, and tell compelling data stories. In today's data-driven world, the ability to create clear, accurate, and impactful visualizations is often what separates good analysis from great analysis.
What?
This course teaches the principles and practices of effective data visualization. You will learn the fundamentals of visual perception, design principles for creating clear charts and graphs, how to avoid common statistical pitfalls, and techniques for selecting the right visualization type for different kinds of data. The course emphasizes both the theoretical foundations of good visualization and practical implementation skills.
Curriculum:
Introduction
Overview of what data visualization is, why it matters in data science, historical development of data visualization, and the role of visualization in the data analysis workflow.
Graphical Perception
Understanding how humans perceive visual information, the psychology behind effective visualizations, and principles of visual encoding that make data more easily interpretable.
The Golden Rules of Data Visualization
Essential principles for creating effective visualizations, including clarity, accuracy, efficiency, and audience-focused design. Guidelines for color use, labeling, scales, and context provision.
Statistical Traps: How Not to Fall in Them
Common pitfalls in data visualization that lead to misleading conclusions, including selection bias, scaling issues, correlation/causation confusion, and how to avoid these mistakes in your own visualizations.
Plots: Find the Correct Plot for Your Data
Systematic approach to selecting the appropriate visualization type based on data characteristics and analysis goals. Coverage of common plot types (scatter plots, bar charts, line graphs, etc.) and specialized visualizations for different data scenarios.
Notes
This course primarily covers the theoretical aspects of data visualization. To get the most from it, we strongly encourage you to explore visualization tools on your own. Popular options include Tableau for interactive dashboards, plotly for Python users, ggplot2 for R users, D3.js for web-based visualizations, and Power BI for business intelligence. Hands-on practice with these tools will help reinforce the principles taught in class and build practical skills that employers value.
