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Comentários e feedback de alunos de Introduction to Data Science in Python da instituição Universidade de Michigan

26,484 classificações

Sobre o curso

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....

Melhores avaliações


28 de set de 2021

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.


9 de mai de 2020

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

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176 — 200 de 5,804 Avaliações para o Introduction to Data Science in Python

por Pascal B

27 de jul de 2019

Generally, very good selection of content. The explanations are insufficient for passing the assignments tho, which means that most of the course work is self-study from the web. The buggy auto-grader sometime made the submission of the assignments quite a pain as one has to find a way to change the code in a way that still produces the right answer but doesn't blow up the auto-grader.

por Sarah A

1 de mar de 2021

It started out great but the more difficult assignments like week3 and week4 could use better wording and better clarification on the expected submission answers. The problems could be more well defined based on expected answers. A lot more of my time was spent troubleshooting the errors of assignment submission than what was being asked to be solved in the assignment question.

por Zhechen T

20 de set de 2021

Bad :

This course sucks, all the video sound like reading pandas documentation. And I have to google the related explanation all the time.


All assignments are pretty challenging. It is a great improvement for panda skills after passing these assignments. This is a Enriching-4-week course.

These stars all for the teaching assistant, they helped me a lot on the Forum.

por Minyi Y

20 de nov de 2016

The content and assignments are certainly useful and relevant. However, the lectures are too short and do not help much with doing the assignment. As a beginner, I relied heavily on google and the discussion forum to get through the assignment. And I am not sure if i can actually tackle similar problems again without referring back to the pre-mentioned resources.

por Julien

20 de set de 2018

Interesting course covering the main introduction topics to Data Science, however there is a too large gap between the theoretical (videos, Jupiter notebook examples, ...) lessons provided and the knowledge required to perform the assignment. The time to do individual research to perform the assignment is tremendous. This is not an easy course at all.

por Jennifer W

17 de set de 2020

I didn't feel that the lecture material corresponded to the exercises. I spent all my time just looking at Stack Overflow. The exercises are also not clearly written, such that you spend time trying to adhere to solving for the solution as opposed to learning Python fundamentals.

por Sabbir A

8 de ago de 2020

I learned things, yes. But I was here to try and learn what is already there in the books; I thought it would make me understand easily and in interesting ways. I was disappointed. There is no point in taking the course if it sends me back to the books. :(

por Fanyu W

12 de nov de 2021

I have to say this course took me much more time than I expected, because I spent too much time on understanding the programming assignments. Assignment 4 is not friendly to me, because I didn't know about sports in USA.


30 de out de 2020

This is a pretty tuff course and the explanations and teachings are not up to the mark while assignments are good. To complete the course you have to take the help of youtube and web to fully understand the concept.

por Claude P

3 de jul de 2021

More concise coding tutorials and less "search on your own on the internet" needed. It is great to get to know the online community and the course needs more coding example directly relat to exams.

por Tom M

29 de dez de 2018

A lot of self directed learning, bordering on excessive. Sometimes it takes some investigation to figure out why the autograder did not pass you. Overall, I felt I learned a lot, much on my own.

por Pamela T

2 de fev de 2019

This is a great overview for python, but the materials/videos/slides are very elementary compared to the sophistication of the homework. Required many more hours than the estimates.

por Saadman S

16 de set de 2020

Statistical stuffs are really tough, it's hard to understand without any background also the assignment materials should be discussed more, they should be included in the course.

por Alice

20 de dez de 2020

Recent changes to the course made it a lot worse! Longer, less concise videos, difficult to find course notes, fewer mid-video working problems, and quizzes are pointless.

por Bárbara C G

2 de fev de 2021

The videos are ok. The assignments are extremelly centered in data cleansing. The debate in forums is very helpful, and the course staff answers regularly.

por Ryan V

10 de fev de 2021

Interesting material, poor instruction and not enough practice for things to sink in. Have to basically teach yourself everything through google searches.

por Colleen K

21 de set de 2018

I learned a lot by doing assignments, but the course materials are not helpful. Stackflow and Python documents guide me much more than the course itself.

por Mohammed A H

26 de set de 2020

During the course, the instructor was presenting with a background contains moving people which caused a big distraction to me.

por Sudharshan C

28 de out de 2020

The assignments can be better structured. I found it tough to navigate and perform operations

por Sai S

23 de dez de 2020

Needs to be packaged in more interesting way..felt course contents and presentation vague

por Khairul A

10 de ago de 2020

Too fast explanation

por Matteo S

3 de nov de 2021


1. The lectures are a chore to sit through. Dry, slow, and unorganized. The lecture on pivot tables was not used in any assignment. So why have it?

2. Assignments. Ooof. Lets break this down.

a. Autograder is poor. Aside from the oddities that just break it sometimes, the hidden test feedback is lacking. If my answer is off at the 15th decimal point because my dataframe is 226 rows and not 227. I need another assert and feedback telling me that. Instead of a lesson on logic a lot of these problems became frustrating cases of github searching for other peoples passing code and then working backwards

b. Assignments felt rushed. Each assignment had poorly written questions that frequently popped up in the forums asking for clarification. The assignments themselves had odd jumps in difficulty and assumptions. Some would build upon the lectures but other times they would jump and assume that we would figure out the middle. Oftentimes we did, but imperfectly, and the assignments penalized us for that imperfection. For example, if question says clean the data and we do using one of a dozen different ways why are we penalize if we have 224 clean rows, and the answer requires 227. If that level of end accuracy is required, then we need more guidance to achieve exactly that.

3. Forums. Useless. Filled with garbage, and the useful ones are unstructured mess. For one, the autograders output is small grey typwriter text which is undecipherable and the TAs always wanted it posted. This lead to long chains of code blocks and one line responses. I also think the TAs emphasis on posting zero code is wrong. The entire web is built on Stack Overflow, so why not allow code snippets in the forum?

por Will Y

31 de ago de 2022

This course serves more as a 'test' for those quite experienced with pandas than an actual educational course. The main issue is that it would be extremely difficult to complete this just with the course materials. You really need to spend a lot of time on stack overflow and with the recommended textbook to make any progress unless you are already quite familiar with pandas. It is then fair to ask - what is the point of the course? What value does it actually add?

The content difficulty isn't the issue its the fact that there is little support from the course in completing the assignments. The content is essentially a whistle stop tour through pandas followed by some tough quizzes and labs with little feedback for when/if you go wrong. I'm really surprised at how high the other ratings are (look at how many go onto the complete the specialization to see a truer reflection!). Again I don't think it is the difficulty per se, there are other courses on this site which cover quite challenging topics (ML, Deep Learning etc...) that do so a lot better than this course. If you have done one to compare against its unlikely you’d give this 5*s.

Instead of doing this course I’d either recommend going directly to the source and read the pandas textbook by the actual author of pandas to get started or do various Kaggle challenges if you want some practical challenges. The only thing this course provides value wise is the certificate.

por Brian L

3 de out de 2020

TLDR - poor design of class makes for a bad experience and wasted time.

Longer - There is a disjunct between what is covered in the videos and what is tested in the assignments, and there are problems with the assignments (the autograder and the extremely dated pandas version supported) . The videos function as a partial reference guide, which is only very loosely what is tested in the assignments. If I didn't pay for the class, I would see value in the assignments themselves. Since I did pay for the class, I expect more value from the videos. For the 3rd and 4th weeks I proceeded more quickly by relying much more heavily on stack overflow and pandas documentation, to the point of sometimes ignoring the videos entirely. As-is the gaps between videos and assignments, on the one hand, and ongoing difficulty of knowing how to navigate the black box which is the autograder (using an outdated version of pandas) are shunted to the forums and other students, volunteers, and assistants. While the forum is helpful (always start there with the assignments, so you will waste less time on poorly designed / assessed questions!), it is poor pedagogy and a bad experience to offload bad course design onto it.

por Lucas C

1 de set de 2019

Overall: I felt this course was useful but pretty time-consuming. The course had relatively limited taught material and relied a lot on searching & self-studying. If you have a fair amount of time it is a good choice.

Pros: You learn through doing assignments which are well supported by mentors/community. Also, you get used to studying through googling problems and learning from websites such as Stackoverflow.

Cons: Whilst this learning method definitely had its merits, it could be quite time-consuming for someone seeking to gain introductory-level skills quickly. You could find yourself in situations where you spend hours searching for something quite elementary and could easily have been taught to you, which could be frustrating. I personally think this course could be improved by adding a bit more small quizzes for beginners to play around with the basics, before requiring them to self-learn through searches.