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Kursteilnehmer-Bewertung und -Feedback für Introduction to Data Science in Python von University of Michigan

4.5
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19,363 Bewertungen
4,373 Bewertungen

Über den Kurs

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-Bewertungen

PK

May 10, 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

SI

Mar 16, 2018

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

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76 - 100 von 4,310 Bewertungen für Introduction to Data Science in Python

von rodania

May 08, 2017

One of the worst course I ever take in coursera. The instructor just writes codes on front of us without explanation.

von Amin s

Dec 04, 2019

terrible course please improve teaching efficiency and give a proper realistic assignments

von RobertD

Mar 06, 2020

The autograder wars made this a bad experience

von Jeffrey D R

May 07, 2018

Like many others, I give this course a high rating while lodging a minor complaint that there wasn't much instruction provided. The lectures were excellent, if brief; it's hard to imagine anyone having objections to the instructor. But in terms of teaching the material, it was a bit of a drive-by. The lectures show a few examples, while not explaining the syntax or the various parameters. You have to draw that out of web sites and cheat sheets. If you're not adept at doing that, proceed with caution here. In the end, I was worn out from the effort, but felt that I had gained a lot.

The assignments were challenging for me because this was my first hands-on experience with Python, much less with Pandas. I did not find Stack Overflow as helpful as the instructor suggested. Nor did I find much help in the forums, but that's not quite my style.

My bottom line is that the course was time well-spent, but it could easily have been a six-week course with a more deliberate pace through the various pandas mechanisms such as merging and grouping.

FWIW: My recommendation is to get to know Jupyter Notebook early and follow along with the lectures by opening the Week[x] files in the course download folder. You can pause the lecture while you go play with the code to make sure you understand it. Also, I recommend working with a local version of Jupyter and keep your files local. Otherwise, Jupyter loses connection to the kernel, and stops being able to save your work. The messages are disconcerting, and if you've worked yourself into a frenzy, they can cause panic and confusion. So do all the work on your machine and then upload the whole assignment when you are finished. You upload on the "Create a Submission" screen; it takes only a sec. You won't even have to worry about details like file paths; they'll be the same either way. Once you get the hang of Jupyter, you can settle into a work routine. Learn some of the keyboard shortcuts.

von Florian M

Feb 03, 2019

I did this course as a 2nd year CS student with limited exposure to Python before the course. I had a basic understanding of syntax and knew basic structures like Dicts., Lists, Tuples. It took me 30h to fully complete the course - I did it in 2 weeks. I would recommend the book 'Python for Data Analysis 2nd' as supplementary literature. The course material is very very limited, which is by no means a bad thing. It just requires you to find answers by yourself. I really enjoyed it personally and would recommend this course for anyone who is interested in Data Science! Just make sure you know your Python basics beforehand.

von zqin

Mar 27, 2019

Honestly, I didn't want to rate the 5 star while I was learning the course, because the assignments of this course was challenging and the course videos didn't talk too much about the coursework. But after I finished the course, I found I have already learned almost all of the knowledge of the book "Python for Data Analysis" by Wes McKinney, which is also the recommended book in the course. And I can do data analysis work with python right now. You might think why do I have to register a course and then learn by myself, but what if this is a good chance to push you out of the comfort zone?

von Sourav S

Jun 04, 2019

The quality of the assignments is really good but the instructions for assignments is really poor.

I had to do read through the discussions to solve almost each and every problem. The assignments are really time consuming and challenging.

Also, I had to refer to stackoverflow for countless number of times to derive the logic.

The instructor has only touched upon the material but additional videos should be included by the TAs for the assignments.

Thanks,

Sourav

von Jens L

Aug 12, 2018

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!

von Hamdy M E T

Mar 16, 2020

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!

von Sean C

Jul 29, 2019

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

von Pravesh G

Mar 02, 2020

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

von Ofir R

Jul 25, 2019

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 !

von Krishna M S

May 12, 2019

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

von Skywalker

Mar 10, 2020

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

von Sumit K B

Mar 05, 2019

Great course to bulild strong base on Pandas.

von John R

Aug 14, 2018

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.

von Stephen L

Jun 17, 2020

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.

von Nattawat B

Apr 02, 2019

This course is very tough. For those who have just learned how to code python will take up to 8 hours for each assignment. The auto-grader required an exactly solution for the answer and sometimes the answer is corrected but you it give you wrong and you have no idea why it is wring just because the type of return value are different!

Apart from those things, you will learned and accomplished alot from this course.

von Willber d S N

Mar 21, 2019

Great Course!! You learn alot about Python for data analytics. It is very hard for someone that is beginning to programming. But there are a lot of recourses on internet that can help you. I recomend this course for all that need learning data manipulation with python.

von WASEEM A

May 05, 2019

The course is good but it gets challenging in doing assignments since you have to a lot of learning at your own , video lectures cover a limited domain of weekly projects. over all this course will help you learn new stuff.

von vinod k

Apr 06, 2019

Assignment questions were not clear. I made lot of assumptions and went through forums to get clear picture. It would be good if the question is explained in more descriptive manner

von Randy M

Aug 12, 2018

I have taken my Pandas skills to a new level as a result of this course.

von Lance E S

Jul 18, 2018

Assignment 3, question 1: The autograder would mark this answer correct even when the data in the DataFrame was wrong. I discovered this after I answered the question, was told it was correct, but I produced wrong answers for subsequent questions that depended on the first one. Messages from fellow students in the forum helped me track down the problem.

Assn. 3, question 2: This was worded very awkwardly and the Venn diagram seemed to contradict the question rather than clarify it.

Assn. 4, question 1 ("get_list_of_university_towns"): The function template provided has a long comment block that seemed to be complete instructions for what the function should do. However, there are two other different versions of the instructions for this assignment in the Coursera course resources section and Google Drive. If the function template includes instructions in the comments, they should be complete. Otherwise, don't show them at all and let the student get the instructions from the other document. Also, the course's "Resources" section doesn't seem like the correct place for these instructions. They should be under the "Instructions" tab of the assignment submission page.

The instructor, teaching staff, mentors, etc. are almost completely unhelpful or extremely slow to answer questions. With regards to my forum postings for assn. 3, a staff member replied only recently, about two weeks after I asked the question. Since then, I've completed that assignment and the one following it!

The course videos are difficult to watch. Whenever Mr. Brooks shows how some code works in Jupyter Notebook, he uses a full-screen view of his browser. On my laptop with a 15-inch screen, his font is a little too small to read easily. I need to concentrate so much more on deciphering the screen that I can't easily keep up with what he is saying. Sometimes I wanted to view the course video on my phone or mobile device. At those times, it was impossible to read the screen being shown. I recommend these alternate ways of showing the code:

Use slides. Students usually don't need to see the instructor typing in real-time. Show a slide with the code and the result.

Use a large font. If showing real-time input and results is important for a specific question, use a large font or zoom in the display as much as possible.

There were some small mistakes made in the videos and assignments that make me think all the materials need some proofreading and updates.

Overall, I'm glad I took the course. I wish several things were better, though. I'm looking forward to the next course of the specialization (data visualization), which is the one I was most interested in taking. I took this course because I would need it for the final certificate and I wanted to be sure I didn't miss any information that would be helpful in the second course. I thought maybe the first course wouldn't be interesting to me, since I have many years of Python programming experience. However, I was pleased to find that the course covered a lot of pandas features and some of the mathematics and statistics techniques that I haven't used in many years, so those contributed to making the course challenging. I would prefer to have done without the additional challenges related to autograder technical shortcomings, though.

von Aman j

May 07, 2019

Concepts could have been taught with more explanation. I prefer learning from books. On trying this video course, it seems VERY tough & so time-consuming to learn. Elaborate explanations could have been provided.

Or at least if I could say, I already knew basic Python but learned Pandas for the first time. Advanced Pandas should be explained with more videos, more steps.

I needed to replay video parts countless times because of only higher level explanation in videos

von Benjamin L

Jan 03, 2019

Almost every course everyone complain about assignments being hard..... but this one is EXCEPTIONALLY hard. Last question of assignment 4 is compulsory to pass the course and trust me it will bring to you trauma and pain like you have never imagined before.

Otherwise the lecturer is actually pretty good, and the other assignments are great for learning!!! I really think they overkilled it with assignment 4 though