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Learner Reviews & Feedback for Introduction to Data Science in Python by University of Michigan

4.5
stars
26,891 ratings

About the Course

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....

Top reviews

HC

May 3, 2018

It's very useful specially for new learner because it only dives into the part of python that data science need. I strongly recommend to anyone even if you don't have experience in programming before.

YH

Sep 28, 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.

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5076 - 5100 of 5,915 Reviews for Introduction to Data Science in Python

By john w

•

Jan 29, 2018

While there are some great things about this course, I was still somewhat disappointed in the manner of teaching. Too often, what was discussed was basic examples of pandas without really explaining how pandas functions. This lead to frustration and excessive scouring of the online API ,Stackoverflow, or the forums to find out how to program a task. Personally, I found learning SQL easier than the pandas library. There is a great deal of good stuff here though, such as the read and response tasks. These add a great deal of depth and perspective to the class and Data Science in general. Also, the subject of the assignments are mostly interesting and realistic problems. As is, though, I'm not sure I'd recommend this class. On one hand, the assignments do set deadlines and motivate a person to learn pandas and data manipulation. On the other hand, much of the pandas learning occurs using outside resources, which could be done without the class. On the whole, however, I have gained Data Science skills, knowledge and perspective from taking this class, and will continue with this series.

By Mark N

•

May 18, 2021

I was really looking forward to this course. The lectures and the readings are great. I learned much there. I spent an inordinate amount of time on the homeworks, though. The problem formulation and grading were a fiasco. Even when the questions were stated clearly, you had to contend with hidden assertion tests, that offered very little in output describing how to correct your answer. Also, I checked my work always against the files in which we were supposed to perform our exercises. Assignment 3 was particularly atrocious; in that, the population sums the autograder expected could not be had, since the estimates were normalized differently in the actual file. Additionally, the quizzes were not well planned. Once, we were asked for the top 3 ranked individuals in a class (greater than 4), but the answer required by the autograder wanted fourth rank included; you received credit for answering incorrectly and were dinged for answering correctly. I pointed this out, but received no response.

By Anastasios B

•

Jan 18, 2022

I think the pace of this course started out alright, but by Week 3, the assignment really turns it up to a new level. Similar for Week 4's assignment, even though there is barely any material covered in Week4. Almost seems like they rushed through half the course, essentially. It's nice that the Jupyter notebooks for the lectures are prepared with commentary, but it can get dull watching a lecture video which is essentially the instructor reading the notebook (and typing it out at the speaking pace). Unfortunately, while I learned a few things, I definitely still don't feel very well versed in the Python topics covered. Mostly I have lists of functions/methods in some libraries/classes to reference, with some idea of how to use them. But I would not feel confident working on a Python assignment yet, even if it was intended to only require Pandas, NumPy (and maybe a little RegEx).

By Antonio F

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Feb 19, 2017

The course is fast paced and the videos do not cover all of what is necessary to know to pass the assignments. However, this is not a problem as all the necessary references are given. The problem is the way the programming assignments are designed and the automatic grader, which should definitely be improved. Some times you spend hours to figure out what is wrong and you finally find out that you have a precision error because of using a library function instead of another (same purpose) or you spend our to manipulate a Pandas dataframe to respond to the specifications, except that all that time is wasted as the grader will accept the first version even if the index does not respond to the requirement (for instance). So, if you want to take it, prepare to fight with the grader. Positive point: Excellent help on the forums, you will not be left alone.

By Yatin B

•

Jul 9, 2020

Well I would agree with many other low rated reviews that the course could have been more systematic less focused on self learning but in practical, work won't be straight like question and answer, in some cases there would be no solid answer, skimming through books, stackoverflow and looking things from others' perspective will make one's project/work really interesting and worthwhile. Plenty of resources already there on internet just we have to be more efficient in getting those. I won't recommend this course to a new candidate looking for very structured course but to those who are quite already familiar with programming field as the course says and self-learners. Course can be much better if instructor could provide more tips and tricks or simpler way things could have been done because at the end improvising is the goal.

By Brian D

•

Feb 26, 2017

Only 37 minutes of video, average per week. Really nice, pleasant video, but don't expect to learn how to solve the problems, because there is little connection from the problems to the videos.

Teaching assistants are hard -working and knowledgable and each has a different way to do things.

Very little in the way of effective educational design.

But they do create useful questions to answer, if you are stubborn enough not to need actual instruction.

The estimate of time required is woefully inadequate.

The best thing you can do is lookup Brandon Rhodes on Youtube. He will actually explain Pandas. Expect to invest about four hours in his videos and still have questions. Use the course forum extensively — only there will you get a hint of how to do what they ask.

Google and Stack Overflow: that's their extensive list of references.

By Nathan Z

•

Nov 16, 2016

The course was challenging, and although I would have liked a bit more information from the video lectures, and a bit more practice on using the basic functions, I learned a lot.

I thought the video lectures were a little sparse. There were only 20-30 minutes worth of video lectures per week and the assignments required you to stretch the knowledge that you gained from the lecture videos quite a bit.

More examples in video lectures would have helped. I would have also liked to see a few simple problems in each assignments just to get comfortable using the various functions introduced in the lecture.

So overall, the course was challenging and definitely a fair amount of work coming from someone new to python (but moderately experienced in C programming), and although I would have liked a bit more guidance, I learned a lot.

By Mante

•

Feb 4, 2017

Good introduction of working with tabular data in Pandas dataframes. I've learnt how to access columns, rows, and specific values in a dataframe. How to transform the data. And how to merge dataframes together. The assignments are a lot of do-it-yourself by reading documentation and Q&A sites. So you're forced to read a lot, and learn more than actually necessary. Teacher and mentors gave hints on the discussion forum which helped. Could have been better if part of the solution is given. E.g. answer = 0.0073.........64 without actually giving the full answer. I also think the automatic grader should give an explanation or example of a correct answer when the assignment is answered correctly. It would have been better that way, because I could review the strengths and weaknesses of the methods I used to get to the answer.

By Mihkel R

•

May 20, 2021

Overall nice and I was able to move at the right pace for me, which is pretty fast.

I was annoyed by the many little mistakes, though, hence the low rating. More than 10% of the quiz questions had something wrong with them so that there was no one unambiguous right answer you could give. In a couple of cases you had to choose an answer that was clearly wrong to get the points for it. What made it worse is that given the high ratings and the number of students already enrolled I fully expected everything to be stellar.

I reported all the errors I spotted to the discussion forums, but will not be bothering to do that in the future since the reports and trying to explain my cases proved in general more trouble than they were worth. Some things were promised to be fixed, some were ignored.

So, mixed feelings and a mixed rating

By Ruben W

•

Aug 14, 2019

First of all, I would say that the course is right for people who are willing to learn a lot by their self. That's because the assignments are highly beyond the scope of the lectures. It sometimes took me 3 hours to complete a single assignment.

Furthermore, you should have some fundamentals in programming, since some concepts are handled as prerequisites.

The Quality of the videos are quite okay, but often too fast and not detailed enough.

I don´t like the Autograder and would love to have some peer assignments instead. And all external (ungraded) tools are currently offline. So that's a pitty and should be fixed as soon as possible!

For those who learn through google (Stackoverflow) and of course, trial and error (I think that's the daily business of a Data Scientist / Data Analyst), I would recommend the course.

By Raul M M

•

Jun 3, 2018

Very disappointing course.

The grader threw exceptions around when either my Jupyter Note Book nor the Spyder IDE I use did. I understand the use of documentation is part of the course experience, but when you find yourself learning more by your own than using the course content, what do I need it for?

When I enrole in a course I hope to hack my way to knowledge, and not just to be tested and loose huge amounts of time looking for the right answer to a specific problem. I want it to teach me how to solve those problems, not to tell me where I can look for possible solutions. Exercises should be a playground for assimilation of concepts and an affordable challenge. That is my opinion at least.

A true shame considering how good the Python for Everybody Course is.

I can't recommend this course.

By Douglas P

•

May 22, 2018

The lectures are of good quality. They are on a practical and introductory level as implied by the course's title.

The Python programming assignments are not very good and do not match the quality of the lectures. The tasks do provide meaningful practice but there are many technical and quality issues. I will just list two of them: One issue is that some functions you need to fill in do too much and when automatic grading software judges your function incorrect then the feedback given is far from sufficient for debugging. Another issue I had is the grader was not grading the version of my code I saw in the jupyter notebook but rather an earlier version it had saved. Issues like make this course significantly inferior to other coursera courses I've taken.

By Alex W

•

Oct 23, 2019

I LOVED the content but the assignments were WAY too hands-off for my taste. The lack of video explanations for how to go about approaching the assignments, and lack of written instructions, lead me to feel like I was spending hours upon hours teaching myself instead of learning from someone. That may be a great way to teach but it is very time-consuming and not ideal for busy students. My other gripe was that the automatic grader was very unforgiving for even slight variations, e.g. I spent approximately 2 hours converting my PeriodIndex values for assignment 4 from pd.Period('2001Q3') to strings in the form of '2001Q3' and then had to lowercase the 'Q' to 'q' before I received a passing grade which I felt was a large waste of time.

By Erik I

•

Nov 10, 2017

The videos are to-the-point and there is a lot of great exposure to field of data science. The assignments are instructive and very realistic I think. The forums are well monitored and the staff does help.

HOWEVER- there is much to be done with the course grader. The course does not support assignments in the latest versions of pandas, which is a real headache. I'm at the point now where I have done everything except the last question on the last assignment, and I'm just going to move on to other things in my life. Because I have the latest version of pandas and I don't want to spend the time to figure out how to use the one the grader supports. Also, I did well learning the material, but my intuition for a statistical ttest is weak.

By Luiz H S (

•

Oct 14, 2019

the learning environment and the scripts are very nice, they're very easy to read and to comprehend, and have a lot of insights. The same can be said about the assignments. They're a steep curve from the materials covered in the lessons, but are related and can be done. The classes, however, are a (very) weak point. Instead of going through the coding, doing slowly some examples and explaining through the codes, the lecturers are, in practice, just citing the classes notebooks. In this sense, there's no need for the class. Moreover, although they indicated this is not a beginner's class into Python, it is not an intermediate or advanced either, so the lectures should be paced more slowly and more detailed on the coding.

By Mount

•

Nov 26, 2016

A course which has great assignments. However, the video itself is a bit boring. Most of the time, my motivation to learn this course is just doing its assignments.

At the same time, the assignments are somewhat difficult for those who are not familiar with Python, but for me it's just OK. What I want to make complaints about the assignments is that sometimes the Autograder is so rigid that I have to try one question over and over again until the Autograder "feels happy", and for me, sometimes the gap between "correct answer" and "incorrect answer" isn't so large...

And finally, thank you, teacher Christopher Brooks! You are ateacher full of passion, and I actually learnt a lot from you and your course.

By Allan K

•

Nov 26, 2016

Lectures were interesting and well put together, however the assignments and the knowledge required for the assignments were not covered in the lecture material. While I can appreciate that every course will require some elements of self-learning and exploration, this felt a step too far. My sense is that if you have some experience in the actual topics covered by the course, and are looking to verify your knowledge with a certificate, you will be fine. However if you are hoping to actually learn about the topics, you are going to have to work very hard. I'm hoping that the coverage of course material to assignment requirements is a lot better in the subsequent courses in this specialization.

By Kathrin F S

•

Jul 18, 2020

The course definitively succeeded in motivating to struggle with Python, using different tools to search for solutions and trying out different ways to solve certain questions. However, this was mainly achieved through the assignments which appeared not to be 100% in line with the lectures (what was needed was not touched in the lectures or only after the respective assignment).

Whta is additionally challenging is that the Jupiter Notebooks for the weeks and additional trainung notebooks created by the user on the one hand and the assignment notebook are using different versions of Python. This is kind of unintended additional training (trying to identify alternative ways to answer the questions).

By Oli C

•

Feb 26, 2017

The course content and assignments are pretty good at covering the fundamentals of how to manipulate data in python, I did learn a lot that I can see my self applying practically.

Where it falls down though is the assignment auto-grader. It's immensely fussy (as I understand it has to be), but the assignment instructions are often very vague and don't sufficiently explicitly define whats expected. This means you spend more than 50% of your assignment time debugging and recasting objects and getting very frustrated whilst doing so.

I would recommend this course, but I do hope that the course administrators pick up on this feedback and make the assignments clearer in the future.

By Sayaka E

•

Feb 17, 2017

This course would be great if you are experienced in Python or any other programming language. The title ' Beginner Specialization. No prior experience required.' is very misleading and if you do not have any experience, you will struggle a lot especially to complete week 3 & 4 assignments.

It makes you think and requires a lot of self-study, which is a good way to enhance our knowledge and skills, however assignments are way too challenging for those who are 'beginners' or have little real-world experience. I would not recommend if you do not have much confidence in your programming 'experience'.

Having said that, teaching team is great - very knowledgeable and supportive.

By Song J

•

Mar 28, 2017

For me the course's biggest value-adding point is to simply ask us to Google everything on Stack Overflow to complete all the assignments, which has almost nothing to do with the course content. The assignments are also badly designed. In the discussion forum you can see lots of confused / frustrated posts. The TA's and mentors are clearly under-resourced to answer all our questions. The videos unfortunately don't teach us much either. The professor + TA's a simply reading off a teleprompter, and they're rushing through everything. I've done some amazing courses on Python also offered from Uni. of Chicago, but this course has been a big disappointment unfortunately...

By Guo X W

•

May 31, 2020

This course provided a brief introduction to using Python for data science applications, with a strong focus on pandas method. It is well-suited for students with some background in Python programming. The assignments are rather challenging. It would be great if there are more in-video questions to reinforce our learning and prepare us for the assignments. Working with the autograder required tremendous patience. It would have been a better experience if there was test code to help us debug our errors. On the whole, I benefited from this course, but I must point out that successful completion of the assignments required a lot of independent learning and patience.

By Jessica G

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Mar 17, 2017

I learned so much. However, the lack of support when I got stuck made it a frustrating experience. I would not have been able to pass without the discussion forum tips. Even then, it was a lot of mucking in the dark and assignments took longer to complete than they should have. I'm glad I took the course but it was not designed for someone at my skill level. If the discussion forum was organized in a better way, it would have helped -- I had to scroll through pages of text to find the right tips to identify my mistakes. Sometimes the question requirements were oddly or ambiguously phrased and I misunderstood the expectation for the function being tested.

By 7characters

•

Sep 18, 2017

I appreciate why the data cleaning and debugging steps are included - I imagine this is a key component of working with real world data, but I think the time I spent debugging and cleaning could be better spent purely manipulating the data to get the answers to the questions in the assignments.

I don't think the introductory videos on python are necessary - they would not be enough for someone to do the rest of the course. I would replace that with explanations on how to use jupyter notebook and getting more from the course in that way.

In all i enjoyed this course, I particularly enjoyed Week 4's lectures on hypothesis testing.

By Niels W

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Nov 28, 2016

This course is a decent introduction to using the python pandas data science package, but suffers from some problems.

The lectures are very brief and do not prepare you well for the assignments. The assignments are not well described and the autograder is very finicky. As a result, every week I spent several hours on the fora and stackoverflow to figure out what the autograder wants, instead of actually learning pandas. I managed to pass this course with (what I know is) subpar code, but we never get to see proper solutions to the assignments.

The course has potential, but as it is now, I will continue this specialisation.