Less interesting

hard assignments

Too fast paced

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.

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.

Well, first thing I am going to say this is not going to be looking good, however correct me if I got anything wrong or being unfair.

To be honest, I am very frustrated with this course! It is a very good topic that I am very interested and that's the reason I am enrolled , but the support and the structure of it is very disappointing!

Example like this(https://www.coursera.org/learn/python-data-analysis/discussions/weeks/3/threads/0kwIpLJDEeawPhIF4bjuNg), question is not even specified and waste me so much time looking for the requirement and post discussion board and wait hopefully somebody is going to reply!

Same for the ScimEn file , which again the file name was not specified in the question!

From last secession, assignment two have lots of confusing around what exactly the question is asking about and again lots of time being wasted just to figure out the question!

The support is poor as well, not like other course , I would usually get a answer within the same day , but this one is really when you are lucky! Plus the staff rarely response!

In short, I hope the staff of the course would see this. This is a good topic but the course are poorly designed with very limited support!I mean if you are truly love the topic , you should pass on the passion to your students and design the course that students not only learn the material in the course but also can know how to ask questions and find out the questions themselves ,but first like learning any skills students needs to ask lots of questions ! I am not going to mention that ,coz even the question in the course is full of error !!

Unless, the whole purpose is to make some money , then it make sense , however if it is that case , I am not going to enrol in this course any more.

After having done quite a few other courses from UMICH (mostly taught by prof. Charles), I had high expectations from this one. I must sadly say I am extremely dissapointed.

1) The course itself doesn't teach anything. The videos basically say: "Here's pandas. It has functions. Good luck". If you want to learn what to do, you are encouraged and have to search it online, because such materials are not provided in the course.

In my opinion it cannot be called a course, if all it does it says "Hey, there exists this thing called Python and Pandas. If you want to learn about it, go do it somewhere else. You're welcome! "

A responsible course would provided well strucured materials for students to study. Videos showing a proff. reading off slides are not particularly useful.

2) Assignments are messy and it seems not much thought has been put into them. Whilst I do like the challenge itself, spending 3 hours figuring out why the autograder gave me no points is completely useless and frustrating: you get no feedback, no hints, no nothing. Eventually you may be able to find some hints on the forum, but seriously....those 3 hours could be way better spent on some actual materials, rather then trying to figure out a formatting issue.

I could go into more detail, but you get the idea. I was really excited about this specialization, but I will not continue with it. Again - I've nothing against a challenge - so a more difficult curricula - but I cannot work with a completely lack of curricula and structure , as well as materials.

This is not my first time with the University of Michigan, I have completed both the "Python for everybody" and "Python 3 programming" specialisations; and i must say , this is an assignment course, the material is rushed (if you are just talking about 10 pandas methods in an 8 minutes video,; you need to review your pedagogy). Paul resnick , dr.chuck and steve oney are really good teachers, they go into the details.But i don't know if he was forced to , but Christopher brooks doesn't seem to bother with explanations (you should learn everything on stackoverflow; well guess what ...i came to coursera for the material not the assignments).He had already done that in the 5th course of the python3 programming specialisation(sometimes explaining code without showing it),it took me 5 days to complete that assignment(notebooks crashing for no reason i ended up using my own but i guess this has to do with the platform ) .

some more pedagogy and slower ,deeper explanations are required for this course.Not worth the time nor the attention.Just learn on Youtube and Stackoverflow or some other ressources(like the many books provided in this course) then once you are ready, pass all the assignments(which are great ,if the material needed was covered this course would be perfect)

This class is an absolutely horrible experience for those of us new to programming and data science. For a few of the assignments, you are asked to return a dataset based on the merging of multiple data sets. A better approach would have been to have a checkpoint at each step to ensure the resulting data frame met the requirements. For example, if the data set needed to be ordered in a certain way with the header formatted a certain way, then let's have a separate checkpoint for the order of the values and yet a different checkpoint for the header values.

The staff needs to understand that having the correct answer at each step of the process is not a bad way to help the student know if his/her code is correct. After all, the staff can easily modify the dataset read in by the student's code after submission to ensure that the student did not use any hardcoded values.

Despite the frustration with the Coursera platform, I can honestly say this is the most fun subject I've had in a long time. But the format selected is absolutely horrific and not conducive to learning and understanding the material.

The main focus of the course is the introduction of the Pandas (series and data frames) library, which is very useful in data analysis. The last two assignments are quite challenging and time consuming, if you are not familiar with Pandas. Why the poor review: I'm sure that the intention of the teacher (Prof. Brooks) is for the student to be challenged and obtain familiarity with several "advanced" functionalities of Python. When I had finished the last assignment I felt that way, but not due to the lectures (only ~2.5 hours all in all). The pace of these lectures is too fast (probably because they are scripted). The teacher should slow down a bit and show some more examples (for inspiration watch Prof. Andrew Ng from Stanford lecture on machine learning). I'm not suggesting to show explicit solutions of the assignments, but just a few more examples such that the transition from lecture to problem solving is less "frustrating". Furthermore, the students are paying $79 for this course expecting thorough lectures on the topic. Reading the documentation of the Pandas library can be done for free...

The format of the presented material - essentially a live transcripting of the lecture - was not a very helpful way to present the information. I would rather listen to the words, rather than wasting space having them written out, and have longer to study the code snippets. An explanation of what each of the code snippets is doing would also be enlightening. And the lecture material didnt really relate to the content of the assignments, those had to be solved through self-study. By Week 3 Id given up trying to absorb anything from the lectures and was teaching myself how to solve the assignments using the internet. Consequently, Im not confident that the solutions I implemented are elegant rather than just brute force and ignorance; a model answer or equivalent would be helpful for teaching the idioms of python. I only completed this course because it was a prerequisite for a data science training specialism organised by my company, otherwise I would have abandoned. This course can be summarised as "figure it out for yourself".

Read the reviews carefully before signing up for this course.

I would not recommend this course to anyone. It is branded as an Intro course, but it is anything but an intro course.

The instructor whips right through the material without much explanation as to the how and why of what he is doing. Then when it came to the assignments, the assignments were way harder to the material covered, and I spent hours pulling my hair doing research on StackOverflow and GitHub just to figure out how to get the data sets to work correctly so the auto-grader could pass my problem. I ended up dropping at the fourth week because I knew I couldn't finish the project without referencing other people's work on GitHub and there was very little instruction on how to set up and do the final project effectively.

There have been very few courses in my life that I felt utterly defeated by, and this is unfortunately has been one of them. I am going to pursue other data science courses on Coursera and other resources that are better suited to the beginner.

Dire, absolutely dire. If you like the following; A. Spending longer endlessly searching the forums for answers than anything else and still not necessarily finding them B. Wasting time getting the right answers only for an autograder to decide an answer that hasn't been touched for an hour and was right, is suddenly wrong (not a great advert for a language you want to use to automate this, is it?) C. Reading countless posts voicing a lot of similar frustrations to this D. Lectures so brief you may as well not bother E. Interpreting "assumes some knowledge of other languages" as "you best be great with these other languages because these lectures won't really help you" F. Wasting yet more time on the forums where answers to one post go totally off track so you're left hunting for a needle in a haystack of replies for something that may or may not be of relevance.

If these things are truly your bag then this is the course for you. If not, then do yourself a favour, go elsewhere and find a different course.

I loved intro to python with the other teacher, but not this. Staff is helpful, but the homework is completely different from the course. Assignment four has us using extract with expert level to do the job. My correlation was off . I had .17 , but the answer was .15. Found I missing data or one city San francisco instead of having two. For instance, autograder tells me in question 1 of assignment3 that my columns are named incorrect. I see nothing wrong with them after comparing to the assignment . what does formatting have to do with passing anyhow. I could just cut and paste, rename and submit, but I have to wait a day for the results and my submission is already late. I Getting question 1 of assignment3 is required to get the other questions, so I got sort of sick of it. I learned something certainly, but sick of the frustration. I looked forward to my python and Jquery classes. I don't look forward to data science with python.

This course really needs to be reviewed and updated. It’s very clear that the instructor is an expert and has a commanding knowledge of data science and python. But the approach is far too academic and not really “introductory” at all, therefore the certificate you get doesn’t represent the effort put in.

My biggest critique is there are not enough exercises in between material to solidify the concepts before adding more. Also, most lectures are too concerned with rare exceptions, and the underworking’s of pandas instead of showing the correct and common way of using pandas. Reminds me of all the time wasted at another Big Ten University spent listening to professors too obsessed with the underlying nature of the material to actually train the audience and impart useful knowledge.

Assignments are way too difficult compared to what is being taught in the course lectures. I would have gotten more value had I taught myself the course material. The phraseology used during the lectures is almost designed to confuse you. You do not get any feedback from the assignments and when I got stuck, I posted on the Discussion forums. No helpful feedback came from that.

I wanted to learn Introduction to Data Science in Python while I worked full time. I spent hours and sometimes even days on an assignment because they want very specific answers and the prompts are too vague.

I do not recommend this course, you're better off teaching yourself. I will not be taking the other courses in this specialization.

As a beginner,This Course theory is very good and can be understood with a little of Python Basic Programming skills cuz of the 1st week notebook and theory provided.and coming to the other weeks,the theory was good but short and faster explanations ends you up in a dilemma.I personally feel that this course is highly recommended for the purpose of 'THEORY ONLY' != 'Assignments'. If you want to work on assignments you'll end up loosing your mind,and for the assignments I recommend people to just checkout Stack Overflow or the discussion forums or Github to answer them.Definitely You won't be able to grasp everything in here,so I prefer and refer you people to checkout courses on 'PANDAS' to work explicitly.

I was quite disappointed by the almost total inaccessibility of the staff in the discussion forums, the unconquerability of the autograder for most of the assignments (losing points for no discernible reason, with all resources exhausted), the lack of a stats module for the specialization, and the lack of education, really. There is value in asking students to learn on their own, but this course goes much too far with that, giving problem sets that are virtually unsolvable without prior experience in data wrangling in R or some other data language. This leaves serious, hardworking students with little choice but to troll the forums for solutions. Hardly the best way to learn the intricacies of this subject.

I have loved using Coursera to learn Python and have really enjoyed the Python University of Michigan courses I've taken so far. Unfortunately, this is not one of those courses. There is no accompanying textbook or reading, only videos and then practice code. The instructors in the videos don't provide any explanation of the syntax, and simply tell you what the code says which you can already clearly see. Basically they read the course syllabus and the lines of code to you and provide no explanation, i.e. "today we're talking about lambdas, here is code where we're using that." You may as well google articles about each topic in this course and you will probably gain more understanding of these concepts.

This was by far the worst experiance I had on coursera. Assignments were way too hard. Had to go online and do research all the time. Of course it is good to do online research time to time but I felt frusturated cause I couldn't do anything without a research. I felt like lecture videos couldn't explain the topic properly. Didn't find readings usefull as there are too little (2-3 useless non introductory readings only) (- -_ - -) would expect from an introductory course to have HANDY cheat sheats. And also I thing we should have been given easy examples followed by harder ones. I have taken 6-7 courses on this platform and haven't been frusturated more before. Below my expectations, way below

I have a decent knowledge of python, this course tries, initially to introduce Pandas. In my opinion, the way they try to do is bad. The material is not available, so, no way to reproduce on the student side the examples. During the course, there are also some small checkpoints to see if the example were clear enough. Honestly, without data sets, without proposing (many) exercises, it seems all useless. They expect that the students get everything immediately (they claim that you don't need to know the lambda function, but expect you to get in in 2 minutes of a lesson). I discontinued this. I will check for other materials, books, and more to deepen my data science knowledge in python.

This course was extremely bad. Reading a video where someone simply types at you and monotonously reads exactly what they type is excruciating. The text moves too fast to absorb anything remotely complex, the presenter has no personality whatsoever, and the course mostly amounts to a guided tour through StackOverflow. The autograder fails to compile code that works but isn't efficient, and many quiz questions are worded too ambiguously. Then when you get questions wrong due to bad wording, you can't immediately retake the quiz! I've taken several Python MOOCs, and this was not only the worst by a mile... it was the only bad one. I refuse to waste any more of my time on this.

Existe uma falta de teoria, o conteúdo é quase integralmente apresentado por meio de exemplos em tempo real com o professor que da a falsa impressão de ser fácil.

As tarefas da semana 3 e 4 são bem difíceis, dependem muito de você correr atrás do conteúdo e documentação dos comando porque o professor não apresenta alguns (e são esses alguns que vão travar seu progresso por horas e horas). Além de difíceis consomem bastante tempo, o curso diz que o tempo aproximado de conclusão dessas tarefas são de 3 horas, eu demorei pelo menos 12 horas em cada. Para piorar o auto corretor utiliza testes secretos então você provavelmente não saberá qual a origem dos seus erros.

This course is a really bad introduction to Data Science. You do not learn how to code for Data Science, they just give you a list of functions without really teaching you how to use them. Then in the tests you get tested on a lot of things that were not explained and you end up searching how to do most stuff on Stack Overflow.

I came here to learn stuff in an organised way, not to learn function after function. The things tested in the exams should be about what you teach, and not whatever you want. This course asumes you have a background in Python and also a background in using it for Data Science, which basically means it is not an introductory course.

i will give 0 star if i could. first of all, the course itself like a lot of other reviews said, leaking of learning and class material. secondly, the assignment and the course lectruing are like two seperate phases, the course lecturing are super simple while the assignments is super hard. Thirdly, you should be careful with the subscription, i only go for the 'introduction to data science in python' this course only, it has it is own page and enterance. And the course should be opt out once you have finished the course, but it is not. it will tell you that this course is some what one of five course,so the subscription continue, it is so tricky.