Applied Plotting, Charting & Data Representation in Python
5.0 /5- Offered byCoursera
Applied Plotting, Charting & Data Representation in Python at Coursera Overview
Duration | 20 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Applied Plotting, Charting & Data Representation in Python at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 5 in the Applied Data Science with Python Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 20 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Applied Plotting, Charting & Data Representation in Python at Coursera Course details
- 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.
Applied Plotting, Charting & Data Representation in Python at Coursera Curriculum
Module 1: Principles of Information Visualization
Introduction
About the Professor: Christopher Brooks
Tools for Thinking about Design (Alberto Cairo)
Graphical heuristics: Data-ink ratio (Edward Tufte)
Graphical heuristics: Chart junk (Edward Tufte)
Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
The Truthful Art (Alberto Cairo)
Syllabus
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Notice for Coursera Learners: Assignment Submission
Dark Horse Analytics (Optional)
Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts
Graphics Lies, Misleading Visuals
Module 2: Basic Charting
Introduction
Matplotlib Architecture
Basic Plotting with Matplotlib
Scatterplots
Line Plots
Bar Charts
Dejunkifying a Plot
Matplotlib
Ten Simple Rules for Better Figures
Module 3: Charting Fundamentals
Subplots
Histograms
Box Plots
Heatmaps
Animation
Interactivity
Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)
Assignment Reading
Understanding Error Bars
Module 4: Applied Visualizations
Plotting with Pandas
Seaborn
Becoming an Independent Data Scientist
Spurious Correlations
Post-course Survey
Applied Plotting, Charting & Data Representation in Python at Coursera Admission Process
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