This course focused on hands on examples for practicing Matplotlib and python on IBM cognitive lab for data visualization using different tools. Also all videos and lecturers made are great and helpful to show graph, plot , charts and maps. The course contents are clear, precise and lecturer is very knowledgeable.

Joining and getting help from course mates and moderates in discussion forum is Excellent!

Ashfaque A. Joarder

I enjoyed the course in all my journey.Most of the visualization tools and packages described in the course very easily.Even I was a beginner to data science,I understood all the labs, videos fluently as it was well-furnished by the IBM Data Science team.Thanks a lot to the dedicated team who furnished the course very considerately. I recommend the course with certainity who are the beginners in the Data Science field.

Firstly, I want to thank wholeheartedly to the instructor Alex Akson sir, all the lab sessions were great. The content was very elegantly balanced as it was neither hard nor easy, I feel like I learnt a lot of things. The explanation of every plot/topic was very easy . Final assignment took a little bit more time but it was totally worth it. Thank you again for teaching this course. I appreciate all your efforts.

I enjoyed this course and learnt a lot. I did not have many problems and did not mind the change in tool mentioned in other reviews. Ultimately code is code regardless of the interface - if learners can better focus on concepts by temporary switching to another tool, then it is fine for me. The final assignment was problem-free for me - I do not know if this was because of recent updates or just luck.

Taught quite a lot and with plenty of hands-on practice. Covered a large variety of plots/graphs/etc and even went into making a dashboard.

If I could request one thing to change it would be about the sudden change from matplotlib/seaborne to plotly. Most of the course we learn how to visualize data using matplotlib and/or seaborne so it would be nice to stick to that when creating the dashboard.

This course was fun, interesting and challenging. The final assessment was a true challenge because it require a combination of materials that were covered in the course, but also required a bit of additional information and expertise as well. Finishing those assignments gave me much more confidence in my ability to perform these kinds of assignments in real engagements and assignments.

One of the best courses in the certification. The assignment is tougher than previous courses and that is one of the reasons I loved the course. Also, the labs are very well structured and presented. Gives a nice overview of Pandas in the 1st lab.

Overall, I think this instructor is very good at communicating his ideas and has a good grasp on what his teaching goal and methodology.

The course contains a good length of data visualization knowledge with python . This course contains number of hands on labs and notebook session which is an excellent feature to enhance skills i.e. practical work is more as compared to the theory work .

The lectures are beautifully taught and designed by the Alex sir .

Thanks to Coursera and IBM , and a big thanks to Alex sir .

I thought this course was a bit tough but still very interesting. I gained lots of insights and ideas on Data Visualization methodology and techniques and I am looking forward to applying this knowledge going forward. Recommended course if you want to get good insight and ideas on how to work with data viz and how to prepare the data in order to present it more professional.

In this IBM program, the discussion forum is well managed.

Whenever I posted my questions, I got help from teaching staff.

Although there is a variability in response time, they responded to all my questions.

Thanks to that, I managed to pass this course with 100% grading.

Especially, I want to give a good credit to Lakshmi Holla for mentoring the students including me.

Thanks

Thanks to instructors. But as always a few improvements are possible. 1) Personally I missed the voice of the speaker from previous 2 courses. 2) I think that extra quizes inside the lessons will be very useful - Don't hesitate to add more tasks for students as intermediate progress assessment.

Anyway, I gained a valuable Data Vis experience from this course. Thanks!

There were some issues I spent a lot of time on in the final assessment. The first was loading the file into the notebook. The second was troubleshooting the choropleth map. I had written the key_on code slightly wrong. You can consider explaining in some more detail how to upload files - and also the code you use - !get.

The course was all in all very interesting.

I thought the syllabus will cover only matplotlib and seaborn. I didn't expect to learn geospatial data visualization in this course. I learnt a great deal about folium and it's versatility. I feel it's a great addition to the course. Such modules will enable aspiring data scientists to expand their domain knowledge and excel in unorthodox data science areas.

A very robust course that is really tailored for someone who really wants to know how a data scientist creates different types of graphs for different types of scenarios. It was challenging but the effort the instructor put into making the course really helped me understand every material as a beginner. Thankfully the practices were actually helpful!

Not sure if the creator gave enough tips and tricks to help provide solutions for the final assignment. I had to use alternate solutions not provided.

The examples given are good but quite simple in places, and only work in specific circumstances. This may be because there are simply too many solutions to go through in this short course.

While the course's material is very interesting and extremely helpful, there is a vast variety of different ways to visualisation and I'm afraid I had to search google for most of the last assignment's requirements.

I believe three weeks is not enough and there should be more exercises in this course. Other than that it was a thrill :)

The Data Visualization with Python course is really outstanding when compared to the others in this specialization. Alex is doing a great job explaining, the slides and examples are comprehensive, yet easily digestable. The in-depth exercises are the best in the whole specialization, quite challenging and therefore very effective.

This course outlines the importance of Visualization and where and how to use different type of representation such as Bar Graph, Area plot, Pie chart Scatter Plot, Box plot and others. The course also introduces to use of Plots to show Geo spatial data. and also introduces one to the different libraries used for plotting. .

The first properly challenging course I've encountered on Coursera and I loved every bit of it. I have an engineering background and I loved having to look for the answers on the internet and in the documentation. It wasn't simply spoonfed to me. If you pass this course, you will definitely gain proficiency in this subject!

Truly Awesome, I get a in-depth knowledge in this specialization and just precise content as well up to date. Correct combination of theory and explanation in detail then practical lab over recent topic covered then Project in each topic. Also final Capstone project is really rigorous. Thanks to Instructor and Coursera.

Getting closer to the end of this professional certificate by IBM, with this Data Visualisation course, which I must say, had a high level if I compare it to the subject I've just completed in the Master. As always, if you guys have any questions about what is like to study online at Coursera or Datacamp, hit me up.

I completed this course as part of the IBM Data Science Professional Certificate and it was by far the best (so far, I have one more left). The videos were short and easy to understand, the labs were on point and many additional resources were offered. The instructor did a really good job when putting this together.

A well done course that really gives a decent overview and exposure to the python tools for data visualization. Although the projects take a bit of extra self studying and research to complete, the course provides a majority of other good sources to continue your understanding of the material beyond the lectures.

The course effectively bridges theory and practical application, allowing learners to build diverse plots from scratch using Python. What stands out is the emphasis on interactive, hand-on labs that mimic real-world scenarios, providing valuable practice in applying visualization techniques to real-life datasets.

Very interesting course and a must take for people interested in learning powerful techniques with Python's Panda, Matplotlib and other libraries. At the end of the course, you will feel confident reading, manipulating and displaying data analysis in a wide array of graphs, charts and other stunning visual media.