This course is a real waste of time! Please avoid!!

The lecturer in general teaches nothing. He explains some basic concepts. You can learn them in a 5 minutes YouTube video. Then, you should answer the detailed/technical coding assignments. The assignments have nothing to deal with the lectures. The lectures have zero to very limited coding explanation. Then, there is an outdated picky auto grader that grades your work. You will spend hours finding out that your code is correct, but the auto grader works with libraries very old versions. I learned nothing from the lectures but I passed the assignments with 90, thanks to StackOverflow and online resources.

I am wondering who gives this course 5 stars. Fake reviews?

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.

Too much content condensed into 4 weeks of course. The videos are very fast with ~1.5 hrs every week and do not cover what is being asked in the assignments at all.

Slamming down some notebooks is not teaching. Despite this shortcut does the lecturer still miss to show and explain the difficult cases of the different concepts.

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

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

I really like Prof. Brooks's way of teaching. He developed a very good introductory level course. Apart from some talks about data science in a whole, he concentrated on the preparatory work in this field -- data cleaning. Instead of delving into theories, he paid most of his attention to how to make things work by using python. I actually have a background in C, and I was a bit reluctant to learn python at first since C is already strong enough to attack most tasks. However, I have fallen in love with python now, and I think it is a much more suitable language for daily use especially when your projects aren't very large. Among its many merits, the best thing about python is of course its numerous libraries like numpy and pandas which free us from tedious low-level programming. I am quite convinced that I will move to python from now on.

In addition to lectures, I truly recommend you go over extra reading materials. Those articles are very thought provoking. For example, the first one "50 Years of Data Science" totally changed my previous view towards this field. It made me realize that data science is not a simple combination of statistics and machine learning, that it is a distinct way of obtaining new knowledge, and that its advancement shall benefit the whole science society.

About the assignments, those taught in the lecture are not enough and you should refer to python documents and stack overflow. I think knowing how to solve problems and where to find help is more important than solving problems itself, and that's why I consider those assignments well designed.

Finally, thanks to all the efforts made by the teaching staff.

If you are looking for in-depth theory, you may be looking at the wrong place. The videos skim through some fundamentals, and sometimes give you some valuable hints.

But if you are looking for a challenging experience that emulates the real world, this course is definitely for you. The assignments will throw you to the wolves very early. You will have to research way beyond the videos to finish them in a elegant manner. It also encourages you to code in a "pandorable" way, which is a valuable skill.

Overall the course is fine. Much of the work is left out to the user, which would be a good thing if the lectures actually spent time discussing a topic. The instructor picks up a topic and shows us one example and is done with it.

I was disappointed with the teaching style. That being said, I did learn a lot in this course. I learnt a lot of stuff, but I wasn't taught much. Some of the topics were really interesting but they are concluded in 5 minutes max.

I really wish the programming walkthrough were more comprehensive and not just "here's how you do this thing, let's move on".

The assignments are challenging, but are poorly worded. Half the time I had to figure out myself what the assignment was asking me to do.

The assignments took too long for me to complete .

great course, but the auto grader needs updating

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.

Was not a fan of this course at all. The first assignment is completely on regex which I understand that it is an important topic, but that's a fairly advanced topic in data science so to have as the first assignment of the first course in this specialization seems a little ridiculous. Not a single question on the assignment was on numpy which we spent the vast majority of the week learning. I did not get much out of the other assignments either. Dr. Brooks is really not the best teacher. Very knowledgeable, but not good at relaying that knowledge to others in a clear manner. If I could go back a month I wouldn't have taken this course.

It would 3* for materials (they are acceptable but not great until Week 4, when there is only one video crafted by staff and the rest are links to external materials including content for which you have to pay). It definitely would be 1* for assignments. The way they are designed and described (especially in Week 3 and Week 4 ) makes it impossible to solve them without referring to the forum where there are many clarifications. Staff member doesn't see any issue with it. My favorite: asking for a ratio and expecting percentage in the answer.

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.

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.

This is not an introductory course! There is a very large assumption that you already know a lot of about the pandas library, as well as extensive knowledge about dataframes and series.

Not sure whether this course is trying to reach data science or Python, but it does a poor job at both.

The class is a light-speed tour through NumPy and Pandas, definitely not for the neophyte Python developer (which I am not). There's 30-40 mins of lecture each week that's basically lightly narrated typing into a Jupyter notebook with only the slightest bit of additional explanation about what the instructor is doing, although the material covered is substantial. There's lot of important details that are glossed over -- forcing the student to pause the lecture and do offline research to understand what just happened.

Similarly, the assignments address and cover beyond the material covered, but the instruction is scarcely sufficient to understand the concepts required to complete them, so lots of Stack Overview and other research is required. And the automated grader, as expected, is completely literal so for complex problems, not much help in validating whether you're on the right track. Assignments take many multiples of the estimated time.

And because even for paying students (such as myself), you never get access to an answer key even after the assignment is due, you have no idea how closely your solution conformed to best practices, even if you arrived at the right answer. For coding, this makes all of the difference, particularly with large datasets that could consume considerable computing resources if not done correctly. I'm told this is because of potential cheating by learners.

How would I change this course? Simple: 3x more lecture material to actually explain what's going on, or down-scope the class so that the existing lecture time becomes adequate for the material.

I recently completed this course on Coursera, and I must say it was quite a disappointing experience. The course material itself was not comprehensive enough, leaving me struggling to grasp the concepts and understand the topics properly. However, what made matters worse were the challenging assignments. The assignments seemed to be disproportionately difficult compared to the provided course material. It felt like there was a significant gap between what was taught and what was expected from the assignments. This left me feeling frustrated and ill-equipped to complete the tasks successfully. I understand that courses should be challenging to facilitate learning and growth. However, there should be a balance between providing comprehensive materials and ensuring the assignments align with the knowledge acquired during the course. This course fell short in that regard. In conclusion, while the course had potential, the mismatch between the course material and the difficulty level of the assignments, coupled with limited support, made it a frustrating learning experience. I hope that the course creators take this feedback into consideration and make the necessary improvements for future learners. he is just a Radio! you can listen to him ! but wont learn from him

I dropped this course. The complexity of the assignments is absurd and the autograder does not give much information about what is supposed to be wrong. Many people have problems with the assigments because the forum is packed with posts about all sorts of difficulties. I managed to complete all the assignments but the last one. In the last one I wasted so much time that I came to realize that there was not point in wasting any more time with this assignment and this course. If the remaining of the specialization is like this, it is going to be a nightmare. I will choose another data analysis course in Coursera, sinze there are a few available.

I find it outrageous that in Week 4 instead of practicing python and statistics I have to spend a lot of time learning very US and sport-specific information which is absolutely useless and I am never going to need it. The same is valid I believe for the rest of the world. The coursera platform was not intended exclusively for US and Canada students? Or for people who do not have full-time jobs and a family in addition to the need to learn python?

Unfortunately, I won't complete the specialization because of this course and I will look for the content elsewhere, because of the following reasons:

1- The videos are not informative and short

2- Assignments only use like 10 to 15% only of the given info from the video

3- No slides, hints or tricks are given to help you in the assignment

4- The lecturer needs real life examples and visualization aid to support his teaching method

Not really an introduction course. The lectures are moving very fast, without really explaining the material. The assignments are much more complicated than the material learned during the lecture. Almost not related at all. I had to learn everything from google. Not for beginners!!! This course will take all your free time and will to live...

very badly designed course, no material support. Assignment required outcome where not covered in the lectures. what do you expect students to learn from outside sources. Very very bad. Waste of money, I am disappointed this course.

The course lectures hardly covered what was asked in the assignment. For someone who has a full-time job scouting through discussion forums is extremely time consuming.