Applied Plotting, Charting & Data Representation in Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representat

Created by: Christopher Brooks

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Overall Score : 90 / 100

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Course 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. 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.

Instructor Details

Christopher Brooks

Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Digital Education & Innovation at the University of Michigan. His research focus is on the design of tools to better the teaching and learning experience in higher education, with a particular interest in understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization.



116 total reviews

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By Jan on 4-Apr-17

The course can be summarized as: "OK, here are some tools that can be used: now read the documentation, Stack Overflow and some papers that we give you links to". Each week there are just few videos. What I expect from a Course are baby steps and clear guidance about good practices. Of course you can learn it all from the Internet - I am taking a Course to get something I will not easily find elsewhere: a good teacher who will guide me through optimal approaches.

By scott m on 27-Nov-18

I don't think the tutorials walk us through what we are supposed to do. I find myself on youtube watching free tutorials on the very subjects I am paying to learn.

By Huang L on 11-Apr-19

The grading by peers system coupled with the unlocking next week lesson is really aweful. I can't change to previous session event though I worked through the first lesson fast. Everyone has the same issue on the forum but nobody dares to reply. We get no help nor assitance from the platform. This course is purely money stealing. Run away from this course as soon as possible.

By Alexandre M on 16-Jan-19

This is an interesting course, but the professor really does not spend enough time teaching the topic. It's like as if he expects that giving us a very high level overview on a subject (e.g., "These are the principles of beauty to follow when making a chart!"), followed by 1-2 very specific cases ("here's how to build a scatterplot!") is enough. We're then expected to teach ourselves in order to be able to turn in assignments. I understand that a core skill of any programmer is the capacity to search for code snippets online as well as ask questions to the community, but for an introductory course on Matplotlib, I'd expect more teaching of the subject matter.

By Sourav P on 26-Sep-18

The lectures are overloaded with too much information, and the concepts are presented in a complicated way. I wish the courses in this specialization were self sufficient. It just does not feel like I am getting proficient at any of this even though I can get the assignments done on my own. there should be ample practice exercises with the aim of burning the syntaxes and concepts to memory which is usually not the case. This eventually leads to half hearted learning where students are expected to do every thing on there own. I am disappointed. The course can be improved by going deep into the concepts and providing additional resources for students to explore.

By David S on 26-Nov-18

assignments are unclear and provide few explicit resources for users less familiar with statistics. crucial topics are not discussed and we are instead told to go google them for ourselves, which i could have done without paying for a course.

By William T on 15-Sep-18

Not very good instructions. The Assignments required just self learning online via other video tutorials/documentation....which defeats the purpose of taking an online course

By Farhad S on 27-Jun-18

The instructor just read a text without any interest and passion.

By John R on 22-Nov-18

Cant submit for two weeks but billed monthly, this is bogus.

By Abu S on 6-Mar-18

Very helpful to understand what it takes to make a scientific and sensible visual. Recommended for someone who is interested in learning data visualization and does not have a background.

By Yousef A S A on 16-Feb-19

The course takes into account the theoretical approach when creating charts which is something I have never thought of! And I don't think you'll find instructors that will go that deep into theory instead of programming. To be honest, I don't believe that charting needs any programming skills at all, it is similar to creating front-end apps, so I think their focus (a week is dedicated for it) on the theory was a great choice.

By Karol S on 11-Dec-17

Great course, great instructor! I think for me the more difficult was lack of simple practice tasks during course - it will much improve to understand material. Regards, and thanks!