Skip to content
CourseraArtificial IntelligenceFree courseCertificate

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

icon
Quality Score

Content Quality
/
Video Quality
/
Qualified Instructor
/
Course Pace
/
Course Depth & Coverage
/

Overall Score : 88 / 100

icon
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.

icon
Instructor Details

placeholder

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.

icon
More courses by Christopher Brooks

Free

icon
More Artificial Intelligence courses

Free

Free

Free

$79.99

Free

icon
Reviews

4.4

489 reviews in total

Select a bar to show only those reviews.Select the bar again to show every rating.

By Benjamin C on 14-Nov-19

Good course, I liked the final assignment which gives the opportunity to freely explore data.

By Nishal C on 13-Nov-19

Some good pointers in matplotlib, covers quite a lot but goes over a range of features and tools that you can come back to. Assignments were a bit simplistic/easy

By Patrik T on 10-Nov-19

CONTENT: The instructor shows some examples of different plots in python
(e.g. line, bar, scatter) and some concepts (e.g. histograms or heat maps) but
doesn't properly explain anything. Mostly you'll get an example graph with
snippets of code only working for that particular example and for the
assignment you're "strongly encouraged to use other sources". That's
not what you're supposed to get when you're paying for an online course. You
should get proper explanations.ASSIGNMENTS: You're basically told to get data from any source you like and
then plot some graphs. If you've had some experience with python and got your
explanations for plotting from somewhere else, you'll mostly spend more time
looking for data to present than for the actual assignment.I don't understand why there's no selection of graphs and data sets to
choose from so you can concentrate on programming and properly presenting data
rather than wasting your time looking at reddit like recommended by the
instructor.ASSIGNMENT GRADING: You’ll have to grade your peers’ assignments with a
rubric that’s just not working: you can give points for someone uploading an
image/writing a paragraph of text, but you have to either give 0 or 100%, so
there’s not way to properly grade partially wrong answers. Example: yes, there
is an uploaded image and the student has explained how it follows “Cairo’s principle
of beauty”, but it doesn’t follow the principle of beauty. So, how to grade: zero
or hundred percent?Likewise, your assignments are graded by your peers, so you’ll usually have
at least one or two days to add to each assignment. You should take this into
account when opting for the monthly subscription. Additionally, neither you nor
your peers are qualified to grade the assignments, because you’re just learning
how to curate and present data (if you’re not already a scientist and just want
to learn how to do this in Python).DISCUSSION FORUMS: You won’t find answers or discussions in the discussion
forum. There are only posts asking to please grade a student’s assignment
because it is urgent because the subscription is ending soon (see above).SUMMARY: If you need the certificate for Applied Data Science in Python, you
probably must take this course. Otherwise I strongly encourage you to skip it
and find other (better) resources to learn plotting in Python.

By Mariusz K on 10-Nov-19

Too little of expounding and too much of searching the net by
oneself. Too few examples. It is a self-learning but what's the Course
for then?Plus the assignments. I didn't like the peer evaluation idea, just as evaluating the others, because I don't have time for this and that's not what I came for. First - what's the motivation of random viewers to fairly and thoroughly evaluate my work? Plus it's hard to finish the course quicker for this reason, because one has to wait a couple of days to get a grade. That's the reason I resigned from waiting for the assignments evaluations for next weeks assignments and in consequence for the certificate.

By Syed A A on 7-Nov-19

By doing this course I was able to learn about visualization assignments are of top grade

By Mike W on 5-Nov-19

It's provides both the theory and principles behind great visualizations as well as the practical experience needed to learn to build these visualizations.

By Amir A C on 3-Nov-19

This course taught me various stuff in data visualization. The assignments were amazing!!

By Oj S on 2-Nov-19

Awesome course, it is good enough who is already a beginner in matplotlib too as the course is overall of intermediate level.

By Pablo B on 31-Oct-19

Great Stuff!

By Praveen R on 29-Oct-19

I enjoyed learning about different plotting schemes with matplotlib. The assignments were very information to learn and explore new plotting techniques. The interactive graphics was interesting to know. seaborn is really powerful and elegant viewing schema. I want to use this in my day-to-day work too.

By Milan V on 27-Oct-19

A well-structured and useful course. The lectures were interesting and the programming assignments had a just-right level of difficulty. Good work.

By Ricardo O on 26-Oct-19

Thanks, nice job, cool course

Showing 12 of 489 reviews