學生對 密歇根大学 提供的 Introduction to Data Science in Python 的評價和反饋

Positive aspects:

Readings were relevant and interesting.

I learned a lot.

I was challenged.

Negative aspects:

Programming assignments took SIGNIFICANTLY longer than suggested (I have 20+ years of programming experience and am fluent in 3 programming languages, but I'm new to Python).

Course did not provide enough support to learners. You're on your own to learn the material and you won't get timely responses (if any) from the staff. I asked clarifying questions about lecture material on the Discussion Forums and got no response (twice).

Several concepts were not taught with appropriate detail / explanation. For example, there was no instruction on how to read error messages in Python.

This course is a mess:

-Not well structured

-They don't properly guide you in the learning process : better courses from better universities, as Stanford ones, describe all you need to complete an assignment, or give you a good and deep introduction to the libraries/framework you have to use... here they just explain part, non including many info you need in the assignment, so, you have to spend a lot of time trying to find proper documentation, reading other external tutorials, checking people with same issues in stack overflow -some even with the same datasets, I am not sure if they were from the same course

A lot of stuff to improve, I recommend to you to look for another course

I have learn a lot by taking this course not from the instructor or TA but through stackoverflow. There is clearly a gap between the video and the assignments. The instructor are knowledgable but less of ability to instruct. When watching the video, I need to pause to clearly read what instructor just typed. I feel like the instructor is reading the screen without clarifying the contents. There are a lot of times I want the quit the course but I finally make it, so proud of myself. Working on the assignment with the help of stackoverflow, I learn a lot.

In sum, I really think the instructor needs to improve the way he teaches and the assignments are useful. Thanks.

One thing I dislike a lot about this Specialization is that it forces you to work through their structured weekly materials rigidly. It does not allow you from working on Week 2 and Week 3 courseworks when you have finished Week 1 materials which I think it is just a way for the course owner to make more money from you. Their excuse is that they allow 'other students' to mark your work to promote interactions between students but I think this makes no sense as not people who takes these courses have a uniformed fixed number of hours each week. The whole point of studying an online course is to be flexible with the hours you put in, which this isnt.

The aim of the course and general guidelines are fine.

BUT!!!

It s aweful how the explanation s given.

Main idea of all videos is: here s a function, here s another one, also you can use this function.

Now you 'go girl' try to glue all the puzzles together by yourself.

It feels like professor was just rushing to record videos and get back to his work which gives money.

Never-the-less i did learn A LOT from this course which i couldnt do by myself since as i said GENERALLY GUIDELINES are OK.

PS. The WEEK4 last assignment was the main problem since i had to watch about 6-8 hours of statistics to truly feel and understand VAR, STD, TTEST.

I would recommend the coursera online training session need more improvement. Please look at this youtube video. this will give you some fair idea what do i mean.

https://www.youtube.com/watch?v=V0AWyzVMf54

During the session speaker need to be visible on top corner and they need to present with more examples during learning this will help and learn fast.

I struggled a bit following your videos going back-and-forth with the given materials.

I also struggled solving the assignment due to confusing questions. Not much clear what is required to solve the problem. it would be helpful to provide some hints for each assignment.

The content of the course is really good but the outdated version of Anaconda they are using is a real pain, because you can't use many features available and causing serious problems when trying to submit the assignment. For a course like this you should consider using at least a recent version of the tools.

Also the speed in which the instructor is talking is like a machine gun, without making almost any pause, and thus making really difficult to keep up. I don't know if they modified the speed of the videos or it is the way the instructor usually talk. I strongly suggest to review this aspect of the course.

I decided to take this course after having a great experience with "Python 3 Programming Specialization". I was expecting the same kind of experience from this course but the lectures are pretty fast. The instructor seems to rush through the lectures and keeps on recommending using StackOverflow for any questions so I finally decided to unenroll myself from this course. I recommend you to install Jupyter notebook, buy "Python for data analysis" by Wes McKinney and follow along with all the book exercises. You will have a way better experience learning data analysis than this course.

Assignements are awfuly messy written. You spend way more time on trying to figure out what the author of question had in mind than on working on your Python coding skills. The instructions are unclear and You have to dig trough the forum to understand what You have to do. What's more, even teachers that help on the forum agree, that instructions are unclear.

It's even more frustrating if You realize, that if someone from University of Michigan spent a few hours on making the instructions clearer, then thousands of students woudn't waste millions of hours inefficiently.

The professor sounds like he is reading script. He uses so many hand gestures, his phrasing, his monotone voice, his hand movements, and pace of explanation makes it hard to focus on what he's saying. The woman was even more monotone. I'm not one to make a fuss about teaching styles given that I was a math major and had professors that barely spoke english, but this course is taught terribly. On top of all of this many of the programming questions could have been better worded. Many of the questions seemed unclear. This course could have been much better designed.

Though the syllabus for this course was well designed, I found most of the concepts being taught very fast or without explaining it clearly. I had to go and refer a lot of other YouTube videos to understand the course videos much better. In fact, the YouTube channel called Corey Schafer helped me more compared to the course videos to complete the course. I would suggest to slow the pace of the videos and explain the necessary concepts even more clearly.

Here, I would also like to appreciate the Assignment questions. They were very well set and challenging.

The class was a helpful intro to pandas. But it was not as much a class as it was a series of homework assignments and the student painstakingly looking things up on stackoverflow. In the end, i am positive I got the correct answers using a horrible coding method and will never see the correct solutions. There should be a little bit more handholding in order for the student to learn the concepts. Otherwise, I might just throw a class up on coursera, give the link to docs.python.org, tell the students to read it and then they will be experts at python.

"Course" is very self-directed. There are very few exercises, and there is minimal feedback on those exercises. No attempt is made to reinforce learned knowledge over time (e.g. spaced repetition) nor is there any definitive list for what knowledge should be gleaned from this course. Emphasis is placed on the student's teaching themselves and learning through experience with no attempt to actually understand the learning process or rest the course design on a scientific basis. Standard filler course for an empty education system.

The course instructions are very limited comparing to some of the other Coursera courses I've taken. The video lectures and code examples are extremely high level and general, while the assignments are significantly more challenging than the content demonstrated in the lectures. I spent a significant amount of time reading the Discussion Forum and googling for the right codes to use, rather than actually applying any knowledge learned from the lectures. If I wanted this much self-learning, I wouldn't have taken a Coursera class.

I have to say, as a new data analyst, even though I have started my work. It is still difficult to complete the assignment. The assignment is far away from the lecture video, which makes the assignment painful and you may lose interests in the process. Even the assignments are not written in a understandable way, so you have to go to forum to clarify each question in the assignment. So please make the learning process me more enjoyable and easy for the starters. Data science is a great career and and I believe it is the future.

Overall I have to rate the course sub par, so 2 stars.

The lectures seem disconnected from the assignments. If you're not already proficient at Python you'll spend your time on stackoverflow.com looking for ways to solve the questions. The assignments took me a lot longer than scheduled.

I did learn a lot by doing the assignments (using stackoverflow). I credit the course with "forcing" me to do the assignments.

Sophie (staff) was the saving grace when I really got stuck. Without her I would possibly have thrown in the towel.

i do not recommend this course to those who has just started Python and want to have a glimpse of the data science capability of Python, i do recommend those should have learnt Corey Schafer's Youtube learning channel for pandas and matplotlib and then maybe start this course, esp. with its assignments. The assignments were rather difficult for beginners, since it does not provide any feedback on where you are actually not right or needs improvement. The course is overall challenging to follow and get all assignments done.

Course materials were too brief to work on the assignments. You have to do lots of your own homework to get through the assignments. It is almost one can do assignments without referring to any of the class materials. It is obvious course cannot explain all the material but they could have organize it better by making clear focusing the assignments. Also it would be much useful if they can add comment in the Jupyter codes. Assignment 4 was very poorly explained. It is not tough at all but the explanation made it worse.

Videos are fast-paced, material is limited (no real slides or extensive doc). Exercises are sometimes not clear in their statement. Activity on the forum compensates that. Exercises are however close to challenges you face in real life. Finally, the start date was delayed and there is no clear visibility on when the other modules are started. So, the course could be better given, provide more material and be better coordinated. I followed one Python course at Rice University which was by far better given.

Pretty bad experience, this course let me feel like listening a people read a book. Professor just simply go through the material with relatively high speed, the connection between each part is unorganized, the graduate student even goes through the material with higher speed. Assignment sometimes confusing and autograder add even more barriers on my path. Assistance answer question quickly in discussion form which I'm appreciating, however, can't compensate for the disappointed quality of the course.

The course lectures were subpar and didn't cover the material in the assignments. Googling for answers and checking stackoverflow for code is a great way to get tips, but it doesn't teach you the core material. I would have rather had links to supplemental readings that the materials were based on. I would not recommend this course as an introduction course unless you've had significant experience with Python data structures and know how to work with lists, dataframes, and iterate over those.

Really disappointed with this course to the point I'm quiting -in spite of having paid for the grade-. It reminded me of my days at the university where I found being a great scientist/engineer didn't mean you were a great teacher. And this is the real problem, even things that should be easy to grasp are difficult to understand -just the opposite of prof. Bill Boyd who makes learning a joy-. Because of this I would only recommend this course if you already have an intermediate level in Python.

The professors are very clear and the speed if good enough so that if you have a computer science degree you won't get too bored.

But, ohhhh my good the grading is terribly frustrating. If you've left uni and spend more than a couple of months in the corporate world you will find it terribly frustrating. You had to fiddle around for hours around data format and some other potential issues.

For interesting python problem with a nice layout and helpfull solutions, discovery go to checkio.org.

This course was extremey hard. The pacing was fast, it seemed like the instructor wanted to end the lecture as fast as possible, I had to rewatch a 5-7 min video three times with frequent pausing to just get the idea of what is going on. The assignments were really hard, especially final questions. I would suggest making longer lectures with slower pacing taht would cover all the material you need. I have just finished this course and feel frustrated instead of motivated to learn more.