About this course: 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 describe the gamut 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.
Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Taught by: Christopher Brooks
| Basic Info |
Course 2 of 5 in the
Applied Data Science with Python Specialization.
|
| Level | Intermediate |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- 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)
- LTI 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
- Notebook: 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
- Notebook: Plotting Weather Patterns
- Notebook: 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
- Notebook: Practice Assignment: Understanding Distributions Through Sampling
- Peer Review: Practice Assignment: Understanding Distributions Through Sampling
- Notebook: Building a Custom Visualization
- Reading: Assignment Reading
- Notebook: Module 4 Jupyter Notebook
- Video: Plotting with Pandas
- Video: Seaborn
- Reading: Spurious Correlations
- Video: Becoming an Independent Data Scientist
- Notebook: Project Description
- Reading: Post-course Survey
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Perfect, insightful, deep, challenging! I love the way prof. Christofer Brooks teach Data Science. Interactive IPython notebooks enables creativity to implement lecture notes right in the browser during watching lections.
I enrolled to "Applied Plotting, Charting & Data Representation in Python" course right after finishing the first "Python for Data Science" module. This is one of the best experiencies I got during my online education.
There are a very active forum discussions on this course, people and course staff are helpful.
Next, I want to enroll next courses of the Specialization.
Also I would like to say "Thank you" to course team and Coursera for the financial aid opportunity.
Insightful course indeed!
Very good course. Requires a lot of work but well worth it.
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Thanks a lot !!! Awesome stuff