Applied Plotting, Charting & Data Representation in Python

University of Michigan via Coursera

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

1 day 27 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Korean, Thai, Indonesian, Kazakh, Hindi, Swedish, Greek, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish, Hungarian, Bengali, Pashto, Urdu, Azerbaijani, Farsi

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Applied Data Science with Python

Overview

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.

Syllabus

Module 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.

Module 2: Basic Charting

In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.

Module 3: Charting Fundamentals

In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.

Module 4: Applied Visualizations

In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.

Taught by

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

Reviews

2.4 rating, based on 9 Class Central reviews

4.5 rating at Coursera based on 6251 ratings

Start your review of Applied Plotting, Charting & Data Representation in Python

Ronny De Winter @RonnyDeWinter

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 pr…

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 stick. Also module 4 on pandas and seaborn could be more elaborate.

The lectures are based on videos in combination with jupyter notebooks. I am big fan of jupyter notebooks but they combine not very well with video instruction. Too often you see the face of the instructor at times you really want to thinker about the code, sometimes the notebook cells scroll out of the video window so you miss the relation with the initial parts. These factors, together with a fast speaking instructor, needed me to often stop and rewind the video to get full understanding of the material. Not very handy.

For the exercises you will probably need more time than indicated if your main experience where the video lectures and jupyter notebooks from the course. A lot of time (80%) is needed to upfront wrangling the data. This is inline with data analysis practices, however this is a course about visualisation only, so one would expect that the focus of the exercise is more on the visualisation part. Expect to spend a lot of time on stackoverflow and the matplotlib documentation. This time could be reduced if the the course material went a bit deeper in some important areas.

As usual with Coursera courses today you cannot submit your work and get it peer reviewed, neither do peer review of other student's work, if you don't pay for a certificate.

For this topic (plotting with python/matplotlib) I would prefer some good online tutorials, for example:
- Matplotlib tutorial (Nicolas P. Rougier) https://www.labri.fr/perso/nrougier/teaching/matplotlib/
- Scientific Python lectures (J.R.Johansson) lecture 4: matplotlib http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb

I believe these tutorials will bring you faster up to speed with matplotlib than this Coursera course.
Michal Kosinski

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.
Anonymous

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…

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.
Scott Stevens

Crap teaching. Professor is more interested in the philosophy of others as to the presentation of data. He spend way too much time and energy on the academic papers for advanced topic coverage.

For a class that should be teaching how to do something, rather than presenting new and informative information, the peer review was a waste and a rubber stamp.

His presentation of method of data visualization were uninspired and boring. No attempt to engage the student in the learning process. Perhaps as a review for someone already accomplished, but as to new learning, this is just terrible.
Anonymous

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
Anonymous

I expected to have more of a hands-on approach and receive a general introduction to multiple libraries available in Python for plotting and making charts and dashboards. Instead I got a one week intro to theory of visualizations. That would be OK b…

I expected to have more of a hands-on approach and receive a general introduction to multiple libraries available in Python for plotting and making charts and dashboards. Instead I got a one week intro to theory of visualizations. That would be OK but maybe for a 10 minutes video following recommended literature (the recommended book seems to be a good option). But not for one full week.

2nd week I got an unnecessary in-depth explanation of the backend graphical mechanisms of Python. I just want to plot the thing! To this point I'm not sure if there is something in Python similar to ggplot2 from R, or something like plot.ly that enables the creation of out of the box interactive dashboards. This is the kind of stuff you have to cover, not theory or inner mechanisms of the programming language. I'm dropping from this course and especialization.

Furthermore, peer grading is full of plagiarism from seemingly the same GitHub repositories. Then, if you don't copy/paste and just rename variables and change format, you risk getting a bad grade from some peer reviewers. Assignment #2 is not that hard but it takes time (more than the recommended time). If they gave simpler exercises the first time instead of one "complex" chart, then people shouldn't have to go and copy/paste from GitHub.
Ilya Rusin

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.
Raivis Joksts

As with previous course in this specialisation, be prepared to do a lot of independent work in learning matplotlib. That is not a bad thing, however, as you will come out much more comfortable with plotting, and the process of learning should be enjoyable to you If you really find the topic interesting. The final assignment give opportunity to do a little independent data gathering / wrangling / visualisation mini-project.
Anonymous

This course is horrible. Lectures are almost useless. Assignments are really difficult for two reasons: instructions are unclear and the lectures don't help at all. One can expect to spend many hours on forums to learn the plotting concept on their own. I'm sure any youtube tutorial on matplotlib is by far better than this MOOC. Don't lose your time, there are many other options.