I like this course very much. I would rename the topic like data manipulation using python. It is definitely not just an introduction program to Data science. The questions are very challenging especially from week 3. But, I am very confident on taking any problems for data science to manipulate it successfully.

The assignments were really interesting and challenging. But the lectures could have explained the syntax a bit more clearer. I was forced to look at the documentation for every single line of code during the lecture. The pace should be reduced. Otherwise its really a good course and you can really learn a lot.

In the begining, there were practice questions at the end of each video. As I progressed, I obseved that only assignments are there. I would request you to provide more questions so that we could better insights and get more hands-on experience. It will help us to build strong problem-solving skills.

Thank You.

Buen curso para acabar de apuntalar los conocimientos en Pandas y lectura de datos. Hace falta un conocimiento previo de dicha librería. La introducción de Regex es de vital importancia para este curso. No le doy 5 estrellas porque hay muchos momentos donde se tira demasiado de Google para acabar las tareas.

Great course. Well explained, and the assignments challenge and push you to explore by yourself. Sometimes however the system that reviews your assignment can be frustrating as it expects the output of your answers in a particular format/way. I guess but that forces you to modify your code in that manner to.

Content was generally well covered. One improvement could have been slightly better examples of how there are multiple different ways to solve the same problems; the instructor would often allude to this but would just advise the student to read through the pandas docs, which could be non-intuitive at times.

El ejercicio de programación de la semana 4 de la sección 1, donde se hace trabajar con datos de estados, ciudades y equipos de deportes, me parece poco útil, demasiado manual y muy lento. Considero que es un muy mal ejercicio. Todos los demás estuvieron muy acorde a los problemas y la materia que se enseña.

I learned a lot, but the assignments really could have been explain better. It was not clear to me what type of format/output I should give at multiple times. What I did was keep submitting while adjusting the output until it was right. A hypothetical screenshot showing what the result looks like would help.

Good course; a great intro to relevant concepts with guidance towards realistic tasks. Only negative is that sometimes problems are unclear (maybe its just me) and spent too much time trying to get outputs exactly right to pass auto-grader. Good experience, and I plan on continuing with the specialization.

I liked assignments they actually helped me to learn.Also book - Python for Data Analysis , 2nd Edition - Wes McKinney was great learning. I would just like to add in that if the videos can have better course material for Week3 and Week4 as assignments were bit higher level than what was included in videos.

This course was nicely curated. I learned about the Python lambda functions,dates ant time,objects and map() in week 1.In week 2 and 3 I learned about pandas.In week 4 I learned about the distribution and Hypothesis testing in Python.In video quizzes cleared the doubts regarding fundamentals of the topic.

One of the best courses along with a highly capable instructor. Very helpful teaching stuff and discussion forums. However, an assignment asked for the mean of a value but produced wrong answer for mean and showed right answer for sum. This needs to be reviewed. Also, many assignments need better clarity.

very good introductory course though with a steep outside the lectures learning curve. good for you as that is how much of learning takes place, not only through some one pointing the way but having a destination (hw, assignments etc) and finding your way to that goal along with others (discussion forums)

Good class and the topics covered are great. Challenging and useful. The lectures are a little light on detailed information and some text or deeper/more detailed lectures would make this perfect. You will spend a lot of time looking for things to support what you learn to really get an understanding.

Videos are great and easy to learn. Forums are great and help with guidance. Improvements would be: better ways to debug your code and more importantly your results. Your code may get results but its not clear what is incorrect on them. Overall UofM produces some of the best work. Great professors!

one of the hardest deadlines I ever did, missing some more hints for where the error is,

but really powerful makes you think in different ways as if you a real data scientist, I liked it

and I recommend it even if you were old in the field of data science, this course will really

make you try your best.

I commend the quality of the course and am very thankful to the instructors for developing the contents of the course.

My only suggestion would be to align the lectures better to suit the level of the assignments so that people who don't have a strong programming background can keep up with the course.

The course was brilliant. It tried to cover a lot of aspects in a short span. The only thing that was not good was lack of explanation of the datasets & tasks provided in the assignment. Clarity in the expected output was not there as a result of which correct assignment submission became difficult.

The course poses itself as a good brief introduction to make you more comfortable at approaching data science project. Recommended time would be 1-2 weeks. However, point deduced for the hassle with auto grader! I also hope that the project could involve people Skyping or collaborating in some way!

Not for beginners.

Level of difficulty increase very fast.

You need to dig external reference sites and use discussion forums in order to figure out some assignments requirements.

Overall quality is very good.

Should have some better tips and validation scripts in order to help debugging assignments.

Solid intro to Python for DataScience purposes. Definitely requires a lot of independent research and it's sometimes nearly impossible to achieve the assignment on the first go (everything is rated automatically, so even a slight deviation from the requirement format will lead to an initial fail).

This was an informative and challenging course. I definitely learnt a lot, and would recommend anyone with some previous pandas and numpy experience to take it! The only downside for me was understanding the question, I spent more time trying to understand what I had to do than actually doing it.

Please make the lecture materials more thorough. The assignments are very difficult and most of the time require learning from other sources rather than this course. I have sent more than 8 hours per assignment since I had to learn a lot of materials outside this course to answer those questions.

The duration of videos is very small compared to the time needs to be spent to solve the assignments. There is a huge gap between what is taught and what is asked.Being a working professional with limited amount of time difficult assignments tends to reduce motivation to continue with the course.

The assignments are not well-prepared, TAs did really really good job though on clarifying vague concepts in discussion group. I expected to read assignments and complete them without digging discussion forums.

All in all it was a good class to learn new materials, but it is challenging though.