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
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 .
Feb 12, 2019
Not nearly enough reference content in lectures. It needs to be made clear students coming from the Python for Everybody course (other Umich course) has a book which I was used to referencing for all of my questions (the class was pretty well self contained and did not require much looking up of concepts). I tried to learn this class the same way I did for the previous one and that totally did not work - I spent wayyyy too much time on my first pandas assignment thinking all of the answers were in lecture/notes. The lecture and notes were very very scant and not well explanative about data structures that are very complicated. Please either write a book or make it more clear how students should learn. Yes, the teacher tells us about stackover flow but I didn't know he was implying for us to use those resources. He should say something like "we don't offer a book with this course so use online resources" and not tip toe around the topic because people paid money to learn so take responsibility and make these changes please. I passed but it was very frustrating at first.
von Kelam G•
Jan 17, 2019
It was informative but i felt the assignment part needed more clarification. I faced the problem that even though my solutions were right the autograder gave me lesser marks. I figured out that we must not print to the console. If that was clearly mentioned life would be easier.
von Trish P•
Apr 29, 2019
Solid course. I definitely would not recommend it to someone who doesn't have advanced beginner to intermediate python knowledge, though - while it does a good job at a review level for the necessary python, it really moves through the code details quite quickly.
von Marcel K•
Apr 19, 2019
It would be nice if Coursera could update the Python environment used for the exercises and assignments to something recent. The version they're using (0.19) is fairly old. Every single assignment that I had running against 0.24 had to be altered in some way to work for 0.19.
von Michael P R•
Mar 21, 2019
Good course overall, but more material is required to be learned outside of this class for the required assignments than what is actually taught in the class by a very wide margin
von Henry C•
Apr 05, 2017
Dreadful course. Instructors saw no value in presenting elements of course that would help learners complete the assignments; rather you are sent off to teach yourself about uncovered techniques needed to complete the assignments. From some of the posts from previous students on GitHub, they resorted to deriving the answer from another means (Excel?) and simply providing the answer as a constant value, in order to receive credit for particular questions. Not exactly sterling knowledge transfer, from instructor to student! This course should be presented as a challenge course to people that have already learned Python Pandas from some other venue. (BTW, Pandas documentation is also dreadful, as of this writing.) This is definitely not the way to learn Python for Data Science if you are a busy professional software engineer. (Wish I had a good recommendation as an alternative.)
The only positive aspect of this course is the challenge to work with defined datasets, to complete specific tasks, during week 3. (This was as much time as I could afford to allocate to this course.)
From a 40+ year software engineer, with doctorate in CS, a part-time instructor at a private university, with a very challenging technology job in a multi-national corporation.
von Bart T C•
Aug 19, 2018
This course provides very little instruction. I really like learning by trial and error, and I think that is how coding is typically learned. Learning python from stack_exchange, however, is how I was already learning it, and I was doing fine. The whole problem of learning from stack exchange is that you don't know if you are doing things in the best possible way, which can be important for big datasets. There was no discussion of the best practices for complete an assignment, after it was turned in, and, in general, may functions were required to pass the course that were never discussed in the course. The entire weeks lecture could also be watched in about 30 minutes, which seems low to me. Most courses I have taken have at least three hours a week of lecture. I have friends who have taken this same course, and had a similar assessment.
von Carl G•
Apr 10, 2018
Not my style of course. Lectures is a mostly just a list of code snippets without any slides. Instead there is a background of 2 people just staring at their screens the whole time. Does not inspire one to enjoy Data Science as a field. Prefer a narrative explaining why and how with practical tips thrown in. Learning to code is more than just syntax. Good examples are the first chapter in Think Stats by Allen Downey and Andrew Ng's Machine Learning course. In this course the assignments took quite a bit of time to complete since lecture code snippets not very useful. Had to self-learn from web to complete assignments. Also took extra time by some trial and error to get right format of results. A more productive approach was assignments in A
Feb 10, 2020
Topics covered are interesting as next steps when you have some basic programming skills in Python. However, the introduction and explanation of new concepts feel very rushed; a one minute video on map(), then lambda with a quick exercise without further explanation, followed by list comprehension at the same pace. I often found myself stopping the videos and googling for further explanation to understand what is really going on. If instructors feel that such concepts should be familiar to someone participating in the course, then I'd recommend not covering them at all, rather than rapidly rushing through.
von Aaron B•
Mar 20, 2019
Really appreciate this course. Got me started in Python, Pandas, and Jupyter. First week felt like magic. I am giving it a low score because the assignment questions were so ambiguous that it required constant resubmits an scouring the forums. The ratio of learning of course content to required Stack Overflow internet research was way off balance.
I learned a lot but was extremely frustrated and burned a lot of time it what I felt was all the wrong places.
Still grateful for this opportunity. I think the questions can be better explained and tightened up.
May 25, 2020
The assignments are fine, they are pretty tedious at times, but it is this kind of situations that forces me to self taught myself. Something really bad about this course is the lectures. They assume we know everything, I wouldn't be able to follow if i haven't done python in data analysis before, g, so they go fast and doesn't explain how everything/every function works. But if they assume we know everything, there is no need for the lecture videos. Just give us the assignments and just ask us to look at stackoverflow. The videos are 90% useless.
von Daniel A•
Aug 20, 2018
This is not really a course. 2h of lectures in total. I have been in longer one-day university lectures. You have to attend other courses in order to be able to complete the assignments because 90% of what they ask is not in the lectures. This is a compilation of exercises, not a course.
On the other hand, the assignments and exercises are OK, that's why I gave it 2 stars.
von Marc B•
Jan 11, 2019
The assignments are good practice, but the course teaches you nearly nothing. You have to do your own research to figure out how to do them.
There are some very useful Mentors on the forums to help the assignments, and if it were not for them, this course would be unbearably frustrating and useless.
von Michael B•
Mar 03, 2020
Video lessons go way too fast and don't actually try to teach you anything. If you're already a wiz at using Python to do data analysis, then you could certainly keep up, but then you wouldn't need the course in the first place. Very poorly paced.
von Mahmoud A F A•
Mar 04, 2020
the course speed is very highand assuming high level of knoweldg
von Muhammad A•
Apr 19, 2020
I would not recommend this course at all. This is for a number of reasons.
The lectures are not really lectures, they are more of a narration of someone else writing code on screen, the intructor just whizzes through what's happening without giving any proper explanation (I cannot stress this enough). The limited explanation provided is just on what's happening on the screen rather than why we're doing it this way compared to any other way. There is also not enough guidance given in the lectures but told to just figure it out and go post on Stack Overflow. Anyone familiar with Stack Overflow should know, they *really* do not like beginners posting repetitive questions - so I find that advice from the instructor really odd.
The courses makes use of Numpy, but gives zero explanation on what Numpy is and why we use it. It just dives into it by using Numpy arrays and expects you to either magically understand it or go learn what/why Numpy, from someone else.
Speaking about assignments, a lot of the excercises require you to do something which hasn't been covered in the sessions at all. I understand giving a challene in assignments, but I would much rather prefer those challenges be related to things taught or from resources given / pointed to. But, unfortunately, you have to figure a lot out on your own and the videos are of no help.
It also doesn't help that the assignment feedback is very lacking. The grader also does not tell you what answer it expects, so you have no way of knowing how far off your answer is.
This is further not helped by the out-dated version of Pandas running (0.19.2). It has a 4 year old version. I tried to do the assignments locally, but then coming onto Coursera to find the methods I've used aren't supported. This causes further frustation with the "go learn on your own" approach, as every resource you'll find will be using methods/functions from the latest versions. You then have to spend hours more finding legacy methods for what you're trying to do (which, in practice, will be useless as you will always be working on updated packages)
In my opinion, this course is not worth the money. I would highly recommend you trial its contents before deciding whether to pay for it or not.
von Amir M O•
Jun 10, 2019
Wish I could give it zero star.
1- The lectures are extremely poor (read the most helpful reviews and you see that a lot of people share this opinion).
2- Assignments are super difficult and not related to the lectures.
3- Assuming that you manage to solve the questions, now you have to deal with their defective auto grader which is royal pain.
4- They insist on using Jupyter (in my opinion a really messy environment). I used PyCharm which is the default IDE for python nowadays but their auto grader caused me so much headache.
Overall, this course requires significant changes and more respect towards the students who spend a lot of time on it. For me personally, it killed my motivation for pursuing Data Science and taking more courses from this instructor.
von Rahul R•
Feb 02, 2020
This course is very difficult. This is first of all not a introductory course. The instructor teaches basic stuff but the assignments were look like mountain. It is quite impossible for a beginner to solve this type of assignment problems without having a very good background in python programming and data structure handling.
I should recommend, the instructor should revise the course content. Please bring balance between what you are teaching and what you are expecting from student.
After taking this course, I personally demotivated from taking further courses in this specialization.
*********** I will recommend going for IBM data science specialization.********
von Marshall J V•
Feb 25, 2018
Would give this class a half star if I could. The material is covered way too fast and the assignments require knowledge of items not even mentioned in the class (let alone discussed). If you know the material well enough to get through this class, you don't need the class. The prof and TA refer to using Stack Overflow to figure it out early and often! Found this class to be a waste of time and money. I wanted to learn the material, but had to drop the class because I had no clue how to do the assignment after watching the lectures multiple times.
von Kyung H K•
Feb 25, 2018
I have no idea who rated this class five stars. The lectures do not prepare you for the assignments and the auto-grader will grade your answer as incorrect if you return a 17 dtype='float64' and they were expecting a 17 dtype='float'. Also, there's absolutely no feedback on your work except from the auto-grader, so there's no opportunity to go back and see a more elegant way of writing your code. I managed to get 90%+ for every assignment, but it was only because I spent over 10+ on the homework assignments for the last two weeks.
von Deleted A•
Nov 20, 2016
The jupyter notebook made this a horrid experience. Plus Coursera really doesn't want you to bother them with your silly questions, relying on peer-forums. If you scroll through the week's discussion forums, many student posts go ignored.
You can't drop the course past the second (I guess) week so the system will keep on keeping on long after you've given up on trying to figure out the janky notebook thing.
Will not return to Coursera for any reason. Breathtakingly bad experience.
von Thileepan P•
Apr 03, 2018
This is definitely not an introductory course. This is more of an intermediate level course. The teachers explain complex techniques in one or two sentences. The notebook demonstration in the video lectures are also very fast.
There is a huge gap between the contents in the lectures and the assignment questions. These points should be kept in mind while choosing this course. I think, I will not take other courses in this specialization.
von David S•
Dec 21, 2019
This course is poorly organized, the instructor doesn't clear the most important basic concepts and pitfalls, instead just gives a brief through what can be done. The assignments are terrible, cannot state the problem clearly, didn't say anything about text files issues which causes submission problems, waist a lot of time on it.
von Daniel D•
May 15, 2018
This was the WORST course I have ever taken on Coursera. The final exercise questions were not specific enough and the autograder SUCKED ASS. I couldn't even refer to a column in my dataframe after I closed the browser 3x and rebooted my machine and it still did not work. This course is a WASTE OF TIME. MOVE ON!!!
von Saulet Y•
Dec 13, 2019
Very disappointed! The assignments are unclear. To complete the assignments, you need to google on each question especially in Week 2 and 3. If you go to "Forum" page, you could see that there are more than 1400 threads in Week 2,3, which means a lot of students ask questions. The course is really really bad!