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Applied Plotting, Charting & Data Representation in Python

University of Michigan via Coursera

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  • Provider Coursera
  • $ Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long

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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.

Taught by

Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran

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Reviews for Coursera's Applied Plotting, Charting & Data Representation in Python
3.0 Based on 5 reviews

  • 5 star 20%
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  • 1
Ronny W
3.0 10 months ago
by Ronny completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
I found in general this course too short and too superficial to become fluent with matplotlib. Module 1 provides philosophical background based on the work of Eduard Tufte and Alberto Cairo, an execellent introduction in the general practices and principles to data visualisation, independent on what tools you use (not python/matplotlib related). Modules 2 and 3 are about the matplotlib architecture, basic plotting (line chart, scatter, barchart, histogram, boxplot) and dynamic plotting (animation and interaction), areas that definitely need to dive a little bit deeper to make the concepts stic…
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Anonymous
2.0 a year ago
Anonymous completed this course.
This is course 2 in the series. I found course 1 challenging and useful. This was a huge disappointment. Far too much time was spent on the philosophy of visualizations--what makes a visualization interesting/useful. That's good but it should be a minor aspect of the course, not full sections devoted to it. I took the course because I wanted to learn the features of plotting in Python using Matplotlib/Seaborn. Seanborn is barely mentioned. The homeworks are all peer reviewed with the grading criteria so broad it doesn't take much to get full credit. If you attempt the assignment and submit something you will likely get full credit. I didn't learn nearly as much as I hoped and will end up reviewing Udemy's Python for Data Science and Machine Learning Bootcamp for more material on charting in Python.
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Michal K
4.0 12 months ago
Michal audited this course, spending 5 hours a week on it and found the course difficulty to be easy.
This course provides solid basis for plotting in matplotlib. It's structure is very convenient, although I'd prefer it to cover more in detail both theory of visualization and practice using Python at a cost of being longer (hence 4 stars). Provided examples and problems are very concise and give a lot of useful tricks. However, Matplotlib is a beast and there's lot going on under the hood, so you better be prepared to dive deep into documentation beside the course material.
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Ilya R
5.0 9 months ago
by Ilya completed this course, spending 20 hours a week on it and found the course difficulty to be medium.
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.
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Anonymous
1.0 7 months ago
Anonymous completed this course.
I completed the first course in the Applied Data Science series and found it useful. The applied plotting course however, is totally useless. Don't bother wasting your money on this course. Matplotlib demos and stackexchange will teach you more than the lectures
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