Data Visualization with Python

Good course, some of the lab assignments did not load properly so it was difficult to practice... (week 2 & 3). Assignment was good after using Jupyter Notebooks as the scripting interface. Thank you!

It's a really great course with proper hands on time and the assignments are great too. i got enough opportunity to explore the things which were taught in the course. Really Satisfied. Thanks :)

More in class projects similar to final assignment where we can challenge our knowledge as we are all remote and it takes time to communicate through the available coursera forums.\n\nThank you.

This course gives very well knowledge about different types of visualization techniques and helps to start with visualization. Coursera provided an amazing course with an amazing instructor.

Poorly put together course - especially the labs. Frequent misspellings, incorrect links and confusing instructions. The technical problems are a greater challenge than the course material.

Good coverage of different plots. Videos are somewhat repetitive regarding the dataset (most of them could be about 20% shorter due to this). Labs (in Jupyter Notebooks) are great practice.

The course was beautifully structured. I would like to request to add the conditions on which tiles Mapbox Bright works. At times the tiles dont work and we are not sure of the root cause.

Kindly update the final assessment of this course work since it is quite difficult to work with it, as the content related to the assessment cannot be found in the course videos. Thanks !

This module is extremely important for Data Scientist. This module gives you the confidence on how to explore, manage, display and visualize data concept to meet end-users requirements...

Good course, but I found the final assignment hard to complete, spent quiet sometime researching to be able to complete it. Providing the correct solutions would be helpful\n\nThanks

The course had a great examples and samples for common and uncommon visualizations. The course lacked the background to be able to import the geojson properly for the final though.

Excellent tutorials, great labs and fun exercises - visualization is one of the most satisfying things about data science, and it is no surprise that this course is very enjoyable!

The final project was somewhat more challenging due to some file downloading issues. But I was able to get some help in the forums for that, which helped me accomplish my goals.

The best course in this specialization, so far. Great balance between theory and practice. Interesting and demanding exercises and assignment. Theory explained in friendly way.

The course contents and lab work are appropriate and useful, however the final lab assignment using folium 0.5 to generate a choropleth layer was fraught with technical errors.

Great Learning. A truly good course which is very apt to the title. Learning clearly on the data distribution plots like Scatter, Boxplots, Folium and Geopandas. Must Learn.

Excellent course, especially the labs were very useful and served as a recap of what was learnt in the previous courses of this certification course. Very clear explanation.

This course gives a solid knowledge on creating visualizations with Python. Please have in mind that you have a good familiarity with Pandas to take the most of this course.

Best of the 5 IBM Data Science Courses I've taken so far. Some problems connecting with the labs, but you can bypass these by downloading the ipynb's from cognitiveclass.ai.

This course, although useful was difficult to follow at times. It did not get that into the Artist Layer of Matplotlib but the final project requires the student to use it.