Applied Plotting, Charting & Data Representation in Python
- 4.5
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Course Summary
This course teaches you how to use Python for data visualization through various plotting libraries such as Matplotlib and Seaborn. You will learn how to create different types of plots and customize them to make them more visually appealing and informative.Key Learning Points
- Learn how to use Python for data visualization
- Create different types of plots using Matplotlib and Seaborn
- Customize plots to make them more visually appealing and informative
Job Positions & Salaries of people who have taken this course might have
- Data Visualization Specialist
- USA: $71,000
- India: ₹8,00,000
- Spain: €35,000
- Data Analyst
- USA: $62,000
- India: ₹5,00,000
- Spain: €30,000
- Business Intelligence Analyst
- USA: $76,000
- India: ₹9,00,000
- Spain: €40,000
Related Topics for further study
Learning Outcomes
- Ability to use Python for data visualization
- Knowledge of different types of plots and how to create them
- Ability to customize plots to make them more visually appealing and informative
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Basic knowledge of data analysis
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Practical assignments
Similar Courses
- Data Visualization with Python
- Data Visualization and Communication with Tableau
- Applied Plotting, Charting & Data Representation in Python
Related Education Paths
Notable People in This Field
- Hadley Wickham
- Edward Tufte
Related Books
Description
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.
Knowledge
- 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
Outline
- 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
- Help us learn more about you!
- 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
Summary of User Reviews
Learn how to create beautiful and informative visualizations using Python in this top-rated course. Students rave about the engaging lectures and practical exercises that help them master the art of plotting.Key Aspect Users Liked About This Course
Many users found the practical exercises to be particularly helpful in mastering the art of plotting.Pros from User Reviews
- Engaging lectures
- Practical exercises
- In-depth coverage of key plotting concepts
- Great for beginners and experienced programmers alike
Cons from User Reviews
- Some users found the pace of the course to be too slow
- Limited coverage of advanced plotting techniques
- Not as comprehensive as other Python plotting courses
- Requires some prior knowledge of Python programming
Christopher Brooks
4.5 Raiting
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