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学生对 密歇根大学 提供的 Introduction to Data Science in Python 的评价和反馈

25,899 个评分


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....



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.


May 9, 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


5551 - Introduction to Data Science in Python 的 5575 个评论(共 5,719 个)

创建者 Shivani P

Jul 9, 2019

The lectures were not enough for the assignments provided

创建者 Alejandro P A

Dec 22, 2017

Good content but too fast pace and confusing assigments.

创建者 Camilo E A P

Aug 27, 2019

Jupyter notebook for assignments do not work properly.

创建者 Sayali B

Jun 20, 2018

The questions are very hard and not covered in training

创建者 Deepalakshmi K

May 23, 2019

Dint teach anything used in the assignments properly

创建者 Joao V O C d B

Jul 27, 2020

The problem is the lack of practical exercises

创建者 Christalin D

Jun 21, 2020

It's asking for money to continue the course

创建者 Hari S S

Jul 30, 2020

A bit more motivation needed in this course

创建者 W N

Nov 27, 2016

Good material, let down by instructors.

创建者 laxmi n r j

Sep 3, 2017

Its too fast paced and less elaborated

创建者 Heike M H

Mar 10, 2022

Not suitable for beginners in Python

创建者 Alexander K

Apr 14, 2020

Nothing new. I recalled what I knew

创建者 Stefano M

May 27, 2019

The lessons were too fast and dense

创建者 Daniele

Mar 1, 2018

Theory is not related to exercises

创建者 김민섭

Apr 27, 2020

Good materials, annoying grading.

创建者 Nachiketa N

Aug 21, 2020

Should have been more detailed


Sep 11, 2020

Better reading a pandas book.

创建者 Yaron L

May 25, 2021


创建者 zhangzhongquan

Nov 12, 2017

it's not very good

创建者 V

Sep 11, 2017

Not much of a use.


Sep 2, 2020

Less interesting


May 5, 2020

hard assignments

创建者 Arjunsiva S

May 9, 2020

Too fast paced

创建者 Nathaniel R

Jun 12, 2020

This course was a travesty. 1. The version of Pandas being taught is not the current version.... so good luck applying this anywhere OR searching for help. 2. The lecture material was wiffle bat level then the assignments were mack truck level 3. I am a professional developer, I know how to use stack overflow and pandas documentation to solve problems. I was looking for a fundamental grounding of the materials. 4. I do feel I came away with a basic understanding of using pandas and python, but that's because I spent about 100 hours looking up answers to every question on here. 5. The lecture is so superficial that you'd learn a python way to do something, then a pandas way to do the same thing, then another pandas way to do something, then that would be the starting block for the assignment that would use advanced concepts. As a result I know 9 ways to do something simple with no recommended best pattern or understanding of when to use one or the other--and they all kind of muddle together now, but then spent dozens of hours researching the actual answers to the questions. "This is the way I like to do xyz, because of this. There are 3 other ways you may see and I'll briefly show you them" would be great. 6. For how important it is, the distinction between methods that mutate data and methods that don't was pretty minimal. 7. The online exercise thing is worthless. It uses an old version of pandas AND there are certain code breaking idiosyncracies in the tool AND it considers a pandas INT wrong if it's looking for an INT but there's no requirement in the question and no discussion of how to transform these or if there's any reason to do so other than to make the autograder happy. Look in the forum, there's straight answers like "an upgrade broke this, so it is not expected to work" which is a bad experience if you spend a few hours trying to debug code before looking up the answer. IT shakes your faith in all the exercieses. 8. This may be a coursera thing, but I'm learning this for WORK, I need to be able to get stuff working on my local PC. I see the autograder makes things easier, but it's basically a similar but different API. I literally spent 1 hour converting my code so the online grader would run for every 2.5 hours of local coding I did. It's debilitating. 9. This is probably a coursera problem, but it's really difficult to find the question you asked in the forum. Since you can't get through most of this course without forum assistance, that hurts. 10. I feel like I got gas lighted. You cannot do this class without already knowing python. This is mentioned in one of the lectures after you've already signed up. He recommends the Python for Everybody course, but it is very unclear from the Course Description before you pay money. Here are quotes from it, tell me if you would expect this to teach you python: *"This course will introduce the learner to the basics of the python programming environment", *"including fundamental python programming techniques such as lambdas", *"SKILLS YOU WILL GAIN: Python Programming". Then the forum is peppered with answers that say "This is not a python programming class". So... SUMMARY: This was my first coursera experience, I was very much looking forward to it and it really shook my confidence in the site. I did learn how to work pandas, but would have done just as well with a list of problems and a google. The "16 hours to complete" took me over 2 weeks of full time work--roughly 100 hours--due to both this disconnect between the lecture and the assignments and to the difficulties transforming working local code in a modern version to a buggy online grading system working on an old version of python but with some patches that also render legacy forums only 80% useful as well. The lectures manage to be both superficial and confusing (because they take a superficial topic then jam 4 ways to do the same thing into 30 minutes). And despite the course description you do not learn an intro to python here, just to data science. I will be trying one more coursera course, basically because all the other reviews on here say this is an abnormally poorly run one, but if they're all like this I will return to pluralsight soon.

创建者 Victor U

Apr 4, 2017

The course is a great course in terms of the knowledge and experience of the instructor and the helpfulness of the staff. I gave it 2 stars for two reasons.

1) The videos are deceptively short. In MOOC instructional design, you normally design short videos because the attention span of an online learner is tends to be much shorter than an in-person university student. However, in this course, even though the video is short, it is really 5-10 times longer because they speed over the equations and teaching so fast that you have to pause and replay and rewind and replay several times while trying it yourself. So the timings are not truly accurate. If you were actually teaching it in a classroom with actual students you would go much more slowly. In this case, I wish they were more honest with the times by actually typing the code in real time while teaching. I would have preferred a lecture to make it more digestible.

2) The hardest thing for me about the course was the fact that instead of practicing computational thinking within data science (decomposition, algorithmic thinking, etc.), I was really just searching on stack overflow for how to put it into python. It's poor instructional design to only teach somethings and expect students to complete assignments without giving them all the tools they will use in the assignment. It would be ok if it were an accidental mistake, but this seems to be purposeful. This happened not just in course assignments but sometimes even in mid-roll video-overlaid quizzes where the answer was something not explained or taught or shown. This was really strange to me and caused a huge amount of time to be spent searching online or trying mid-lecture problems to no avail. It cause all the timings of the course to be off (#1 caused the video timings to be severely off and #2 meant that the course assignment estimates were HUGELY miscalculated). Good instructional design would mean that the professor should show all the tools one could use to faithfully complete and achieve the assignment. For some reason that was avoided again and again in this course.

Great material though. I loved learning. I just wish it were better structured and supported and that I learned more about the work rather than just searching online for how to write something.