And then, there it goes, another course in this amazing Specialization. This course of Applied Plotting, Charting & Data Representation in Python was magnificently well constructed. The videos, quizzes and assignments are pretty fun to dive into and, this is the first course in which I see Peer Assignment (because it makes sense) and it was great to learn with other people from all around the world!

This Course is really interesting for those who are already worked with matplotlib, numpy and scipy.

The first three weeks a lot of hard plotting is done for learing the modules. In the last lecture, week 4, the more easy way is shown with pandas built in plotting functions and a toolkit expansion - which makes the plotting more applicable when working with Pandas Data Frames.

Great course in all aspects. In fact, I have taken quite a few courses with the University of Michigan and the majority were top class. The only exception is Applied Machine Learning, that one is a mess. Jupiter Notebook is not functioning well, grading is not working, and pretty much no support.

its actually a good course as it starts from fundamentals of visualization to the data visualization,the assignments this course provide are exciting and full of knowledge that you learn in course ..

I am going for the specialization and I know this is just the second course in it and I haven't even seen the further courses yet, but this is already my most favourite course in the specialization.

Great course with lots of learning. The lectures were crisp and the course inspired us to look at materials beyond the course and in the internet which is a important skill for any data scientist

A beautiful Course to accompany with python and Machine Learning.

I learnt along with my peers who graded my assignments too, Thank you.

Pretty good course the material is good.

Offers good coverage and proposes some interesting problems.

Pairs grading works pretty well.

Really amazing course, Christopher Brooks salute man, you explained every details in good way that one can easily understand.

Amazing source

I am confused how to rate this course... Let me introduce my personal Pros and Cons

Pros:

1) I've learned A LOT.

2) Assignments are really interesting.

3) There is a Peers Grading system, which I personally like.

4) If you really dig into problem - there is a big chance you learn cool things.

Cons:

1) The Professor explains only the very basic stuff. It's a bit disappointing because I finally was doing the assignments with external sources of information. It was not much usage in videos.

2) You have to spend tonnes of time on your own digging into possible solutions to learn how to do the assignments. But if you care only about passing it - don't worry it will take you maximum 1 hour as there are a lot of already done examples on many sites.

3) While grading the assignments you can see a lot of copies. Once I've found my own project copied. After that I decided to leave the code with advanced charts only for my personal usage and for assignments - the simple basic charts.

To sum up - great source if you like exploring new things and digging into details on your own. It's like a tool that give you some direction and base to start. If you prefer to get information ready to use - it's not for you.

I put 4 stars out of 5 as it was a great help for me but still almost all the information I had to find on other sources.

Good course if you want to learn basic data representation in Python Matplotlib. I would suggest it needs a little work up in assignments and syllabus section to make it more competitive towards the end. Like building visual animations, heatmaps, clusters in assignments rather than just simple line and bar charts

For the last assignment, it would have been great if we could have chosen the topic of our work ourselves (e.g. sports/politics/...). I had to work on a task regarding religion, which is for me an extremely boring subject; this led me to delay the finalization of the assignment by weeks.

I did not find this topic really useful. I think there could have been more details about matplotlib and less information about 'what is a good plot'.

Good course. But I think Seaborn should have been explored in detail since it's much better.

This course provides an overview to the matplotlib and seaborn library and guides learners to create useful visualisations with Python. My main issue with the course is that the various topics are not covered in sufficient detail. Successful completion of the assignments required far too much independent learning on commands that were not covered in the course (particularly for Assignment 3).

The course also covered Principles of Information Visualisation in great detail. I thought that was refreshing and useful. However, I felt that the portion on Matplotlib Architecture could be explained in more layman and palatable terms. In addition, it would have been more meaningful if the course drew more on actual real-world datasets instead of histograms generated from a random normal distribution.

The material does not cover all of the assignments. I did learn A LOT by finishing my assignments, but the process was frustrating. I feel like a baby who have not learnt how to stand, but my parents ask me to run.

If you are good at finding solutions in original documentations (e.g., python, pandas, and matplotlib) and Stack Overflow, this is the right course for you.

However, if you are seeking for abundant materials and examples to sharpen your skill, sorry, this course may not right for you.

Learnt about the different plotting and charting techniques.How to get subplots and architecture of matplotlib. Just syntax of each type of charts is shown.

The downside is there is alot of research work on your own.Also the course diffculty is quite high as i was not much experienced in matplot. Wouldnt recommened to beginners. Also teaching style could be largely improved so as the assignments.

The lectures really need to flesh things out more, they too often feel too fleeting and leave more than they probably should to searching other resources. Questions for the final piece clearly haven't changed in at least two years and lack clarity as to what should be done

I learned a lot about pandas and plotting, but:

I spend a lot of time figuring out the instructions on assignments because were ambiguous or incomplete, I used the discussion forums searching explanations for assignments.

Plotting concepts need more deep explanation or more practice, generally the provided information wasn't meet the course's level "in my opinion."

limited theory on the subject

I didnt like the peer review

Some of the material was interesting but on a whole not nearly as engaging as course 1. I fully can appreciate how the principles of chart design are valuable to the subject matter covered in this series but on a whole I would have liked more focus on the technical skills and maybe had the academic perspective on design extra reading.

Also the peer grading portion of this course is a little rough. The people that graded my work were great but I don't expect them to engage my work in a very meaningful way. It's not realistic to ask them to give their full effort to grade 3 assignments for an online course that they pay for. My personal preference would have been to structure the assignments so that they could be automatically graded like in course 1.

The concept is good: introduce the theory of information visualization and introduce how to make charts with Python and matplotlib. Unfortunately the materials are deficient for the programming part. There aren't nearly enough practice exercises to help you learn matplotlib. The previous course in this specialization (intro to data science in python) by comparison has many more guided practice exercises, and I am disappointed that this course does not live up to the standard set by the first course. If you are taking the complete specialization, as I am, then I guess it's worth it and I hope the next courses in the series have more material.