Associate Professor of Information
Applied Plotting, Charting & Data Representation in Python
What You'll Learn
- Describe what makes a good or bad visualization
- Understand best practices for creating basic charts
- Identify the functions that are best for particular problems
- Create a visualization using matplotlb
4 Modules
24 Hours
6 hrs per module (approx.)
Rating
About Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.
This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
Skills You'll Gain
- Data Presentation
- Data Visualization
- Graphing
- Matplotlib (Python Package)
- Python For Data Analysis
- Python (Programming Language)
- Statistical Graphics
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Applied Plotting, Charting & Data Representation in Python, a course focused on designing effective data visualizations. You will learn visualization principles, explore matplotlib functionality, and apply design choices to communicate insights clearly. The course emphasizes critical evaluation of graphics and hands-on visualization projects.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Principles of Information Visualization
- Video: Introduction
- Reading: Syllabus
- Reading: Help us learn more about you!
- Video: About the Professor: Christopher Brooks
- Video: Tools for Thinking about Design (Alberto Cairo)
- Reading: Notice for Coursera Learners: Assignment Submission
- App Item: Hands-on Visualization Wheel
- Video: Graphical heuristics: Data-ink ratio (Edward Tufte)
- Reading: Dark Horse Analytics (Optional)
- Video: Graphical heuristics: Chart junk (Edward Tufte)
- Reading: Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts
- Video: Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
- Video: The Truthful Art (Alberto Cairo)
- Discussion Prompt: Must a visual be enlightening?
- Reading: Graphics Lies, Misleading Visuals
- Graded: Graphics Lies, Misleading Visuals
Module 2: Basic Charting
- Ungraded Lab: Module 2 Jupyter Notebook
- Video: Introduction
- Video: Matplotlib Architecture
- Reading: Matplotlib
- Reading: Ten Simple Rules for Better Figures
- Video: Basic Plotting with Matplotlib
- Video: Scatterplots
- Video: Line Plots
- Video: Bar Charts
- Video: Dejunkifying a Plot
- Ungraded Lab: Plotting Weather Patterns
- Graded: Plotting Weather Patterns
Module 3: Charting Fundamentals
- Ungraded Lab: Module 3 Jupyter Notebook
- Video: Subplots
- Video: Histograms
- Reading: Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)
- Video: Box Plots
- Video: Heatmaps
- Video: Animation
- Video: Interactivity
- Ungraded Lab: Practice Assignment: Understanding Distributions Through Sampling
- Peer Review: Practice Assignment: Understanding Distributions Through Sampling
- Ungraded Lab: Building a Custom Visualization
- Reading: Assignment Reading
- Reading: Understanding Error Bars
- Graded: Building a Custom Visualization
Module 4: Applied Visualizations
- Ungraded Lab: Module 4 Jupyter Notebook
- Video: Plotting with Pandas
- Video: Seaborn
- Reading: Spurious Correlations
- Video: Becoming an Independent Data Scientist
- Ungraded Lab: Project Description
- Graded: Becoming an Independent Data Scientist
Grading Policy
Assessment is based entirely on four peer-reviewed projects, each worth 25% of your final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Some related experience required
