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
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| 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 |
good course to get started with matplotlib, needs more hands on exercises though!
The course taught me well how matplotlib works, as well as quite a bit of theory of making plots and what to look for. This knowledge is very useful and applicable in many situations when doing data science. On the other hand, it required perhaps too much work. Ocassionaly I had a feeling some of the work was repetitive and not leading to new knowledge any more. But overall, very well worth it!
This was an interesting course. The professor was excellent and the practical exercises, in particular, were beneficial in learning the material. My only complaint would be that a lot more time in the exercises was spent formatting and manipulating Pandas dataframes than applying the matplotlib libraries to produce charts and graphs of the data. I would have preferred to spend more time experimenting and using the graphics libraries and less on trying to manipulate data to get it into formats acceptable for grading.
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Awesome course