Excellent learning materials. Clear concise explanations, but with the focus and majority of time devoted to activity-based learning: exploring the docs, practicing skills, and developing solution code. Even better is how subsequent lessons not only build on previous skills, they actually help guide and refine approaches even further. Well orchestrated progression of zone of proximal development. Thanks for a great learning experience!

Great Course and Awesome Instructor. The course is very practical and hands-on. All assignments starts with messy data and leave it up to you to start cleaning and manipulating the data with some modeling objective in mind which is what a real data scientist typically do. Thanks for the course , it was a really cool experience ! I really enjoyed the course and it was a bit challenging sometimes!

This course is really wonderful and tasking. You'll get to know the core foundations of Data Science and useful libraries Data Scientists use to manipulate data. The assignments are very thorough and deep. Many thanks also to all the teaching assistants who were available to help, especially to Sophie Greene and also to Yusuf Ertas. I look forward to completing the specialization!

Overall the course is great for people who want to begin with data science. The skills it incorporate are very useful. The only thing to improve is that we could be given more hints when doing assignments. Sometimes we are not familiar with what can be done with Pandas, so it took a lot of googling to complete the assignment.

Excellent course, even if you have taken other Python courses or if you use Python regularly. This course provided a good introduction to many ways you will use Python in real-world applications. The assignments were challenging enough and some required research on your own, which is exactly what you do with real problems.

Very interesting and hands-on course. Especially with the assignments not being timed and instead being allowed to google, but the assignments still being appropriately hard. The assignments promote problem-solving on the fly and finding the help you need on stackoverflow, google, etc.

This course is excellent if you're looking to learn how to use Pandas inside Jupyter Notebooks. Assignments are autograded and feedback can be received immediately. Course is a few years old and discussion forums contain answers to common questions

Great introduction to applied data science. The weekly assignments are challenging and varied, and students are required some independent studying outside of the lessons. The forums are also quite helpful in approaching the assignments.

Really impressed by the material and quality of instructions to teach this complex topic. I felt the assignments were appropriately challenging to apply concepts taught in the lecture. Well done and thank you!

Um curso intenso e bastante prazeroso. Gostei de todas as etapas, os videos funcionam bem e estão construidos numa base introdutória, mas o desafio é pesquisar e pesquisar. Muito interessante mesmo!

the course is designed very well. It covers data manipulation topics very well. It has excellent assignments which help in understanding the course concepts more better

Frankly, I did not watch the lessons at all, although they seem good.

The assignments were really great !

Challenging and very rewarding.

Really recommend the course !

Very interesting course to guide you through from the basics of pandas. Teaching staff is of great help throughout the learning process, with speedy replies.

Great course that teaches about how to process data in Python. The lectures are very code-based and the programming assignments help you learn new methods.

Excellent course with assignments, But some elaborated videos on topics could help much better in solving the assignments in time.

Prof Brooks, Yusuf and Jimi have worked out engaging but also demanding applied course on the subject. Thank you very much

Discussion forums are very useful. Exam questions are challenging for beginners but doable. Good teaching.

Great course, but expect to really work. The assignments force you to get a good grasp of the material.

Excellent course! The professor is super clear and the content was really well organized!

Very helpful and practical course, great intro to data science.

This is the only course worth in the specialization

Great course to bulild strong base on Pandas.

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It took me a while, but I finally figured out the problem with this class. The lectures provide some good information, but only rarely do they go into WHY a particular action/method/approach is used or WHY it will be important later. I had to do my own deep dives into available documentation to figure out how most of the functionality covered in lectures really works. This is not necessarily a flaw in the class, but it does mean the suggested time commitment for each week of class is significantly underestimated.

The assignments, while interesting, have the same issue as the lectures. Most of the time is spent using of Google to look up Pandas and Numpy functions or methods, or if we really get stuck, to see if someone on StackOverflow already addressed any questions you might have.

Put simply, the only different between this class and learning from a book is the class sets deadlines for the students to meet in the form of graded assignments.

Of course, the setting of deadlines is an excellent way to stimulate learning, and this is why I will continue on with the Data Science specialization.

The course will teach you basics of the Pandas library, which is an essential skill. It also gets involved with some issues related to data cleaning, which is also essential, but felt a little like

There is very little peer-to-peer learning because there are no practice sets that peers can talk over, only assignments which Coursera's Honor Code naturally prohibits discussing. Hence, the learner never sees optimized code for solving real-world problems. I'm pretty sure I would have learned more if this course had provided more practice problems for learner discussions. For example, very inefficient iterations can be used to solved problems that should be solved in better ways with Pandas. I know that sometimes I was doing it right, but I think sometimes I wasn't and it would have been nice to see better code.