BM
Although good to learn the know-how of basic data analysis techniques, the quizzes are predictable and you don't end up coding as much as you should. A good starter course to wet your feet in DA!

Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.

BM
Although good to learn the know-how of basic data analysis techniques, the quizzes are predictable and you don't end up coding as much as you should. A good starter course to wet your feet in DA!
MW
Good Course. Very good overview of Python libs -Pandas, Numpy, Matplotlib, Scipy, Scikitlearn and Seaborn. I really enjoyed learning about them and seeing the usage. Highly recommended course.
AA
Most of what you'll learn in this package are fundamentals to other knowledge areas. So, practice both in and out of the course. I appreciate the coordinators in making it possible. Thank you.
AM
Thanks for course! I met some errors, described them in your forms. I liked every models, but the final assignment was not interesting. I think it can be done better, with decisions and conclusions.
ND
Totally overwhelmed with the course contents and easyness in teaching. The course will make you familiarize the fundamentals in a way that you will never forget when you used in a real world.
TG
Wonderful course, explained everything in the most easiest way possible. BUt hte module 4 and 5 ffeels kinda hard for a person who is not familiar with data visualization and machine learning.
CD
Great Introduction to Data Analysis, the concepts going from the basic to deep in data-testing and data-training, as well as several applications linear and polynomial regression to data analyze.
SC
This was a comprehensive course which dives deep into data analysis. The visualization part could still be improved, but nothing to take away from the course. It is one of the best out there.
CT
Good course, sometimes moves a bit fast in the final modules and the labs are quite tough but great course and would recommend to broaden your knowledge of coding, data analysis and visualisation
AB
Great introduction to data manipulation and analysis for common problems that arise in data science. Also allows you to gain a further understanding of Python syntax, specifically the pandas library.
BD
Really interesting course, if one wants learn programming language. Well designed and structured. Only suggestion is, if the small videos contains example that be really great to understand it well
VS
This is totally one of the hardest course I've ever taken on Coursera. It's packed with knowledge I did not know before. Definitely recommended for people who want to learn data analysis with Python.
Showing: 20 of 3,121
Very low quality of the course. The structure of the course is illogical. Also it takes too little effort to accomplish the course. In the beginning of th course labs contain all the code so a pupil doesn't have to do his/her best to solve tasks. I can just constantly press ctrl+enter and get my certificates. It is not what I expected from the course. Also quizes never contain coding practice, so to accomplish I just need to show the understanding of the basic aspects of the topic, not the coding skills. The, at the end of the course (after I have lost all the motivation during the first weeks you give us difficult function, including custom functions, never explaining them at all). Have a huge doubt about buying the subscribe for the next month.
So many mistakes in videos and labs, including spelling errors, misnaming functions and code that causes errors.
These have been listed extensively in the course discussion forums, with some complaints from over 6 months ago, and have not been addressed
Many typos and other errors. My favorite was the video where they said "150 - 50 = 50"
Honestly, I'm not sure why this course has such a high rating. I feel like it can't possibly be a reflection of what actual students felt about the course. Reading the other reviews, it's clear some of the issues people had with the course were not the course-designers fault. But, there were some tissues that are simply inexcusable. For example, typos in the lectures (especially towards the final week) show little to no proofreading was done. A lot of the labs involved "Warnings" that the instructors didn't explain to students (and so obviously some students got confused by them, even though they were inconsequential). And the final peer-graded assignment was a complete mess. The first few questions are numbered Question 1, Question 2, Question 3, etc. But the last 4-5 questions are not numbered making it very annoying to upload screenshots for each question. The directions in the assignment were simply wrong. For example, one of the questions didn't even have a prompt, just an empty text box. Someone asked about it in the "Discussions" and a staff member replied but it was never fixed in the assignment.
Trying to put the typos and logistical confusion aside, the course material was oddly organized and students were never really given an explanation as to why the concepts taught were being taught in this way. My least enjoyed course of the whole specialization.
I am working through the IBM Data Science Certificate courses (in order) and this is easily the best one I have taken so far. Once again, the labs provide a variety of hands-on exercises that help to cement the topics introduced in the lectures (which, to be fair, are very fast-paced). Everything taught is practical and relevant. One request would be to fix the pacing of the videos and lecture quizzes, which often appear to test students' comprehension mere seconds after the topic was discussed! I did also notice a few errors in the labs, but they did not stop me from learning the material. Overall, great course.
Too many new functions with no option to practice. New and new and new library each week, in the end it's a total mess in the head.
This course should contains real excersises, lots of, just pass the test doesn't mean that you become familiar with the material or remember smth.
Great introduction to data manipulation and analysis for common problems that arise in data science. Also allows you to gain a further understanding of Python syntax, specifically the pandas library.
Thoroughly appreciate the effort to put this course together, however there are several problems (I think this is the worst quality course I've seen on Coursera so far .. or maybe all other ones have just been great!) -- a) the instructional videos contain many errors in both code syntax, and, worse, in logic; b) questions on Forums take a long time to be answered, and staff member who responds to most of them appears to be a bot/only provides vague general info; c) course material has ups and downs, for example Inferential Statistics are blazed through within 15-20 minutes, and there is very little discussion of, say, how to identify the distribution of your data, how to decide on parametric/non-parametric tests and so on.
I'm sure a number of people put loads of time into this, so, thank you!
This must be the absolute worst online course I've ever come across ever! First of all, there are so many typos and mistakes in the course material, which is totally unacceptable! Then, there is no logical continuity in the subjects presented. The course is supposed to be intended for beginners, however, there is no background information or reasoning behind what is presented. Many times, one may only copy and paste python commands without knowing why or what it means at all! Unfortunately, This course is a disaster, and hence, NOT RECOMMENDED AT ALL!
perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.
i was following nicely until week 4 but halfway there it got really difficult. To a point in week 5 when all i could do was copying code and adjusting it. I have no idea what i was doing, i totally lost the bigger picture. I'm sure i could never replicate any of it outside the course or explain what i learned.
I didn't like it at all
although its the most important course in the whole specialization but it's really bad i didn't understand a thing
I felt the course isn't designed well as it takes you little fast than expected and doesn't explain all the terms! May be one has to be very good at math or revise all the topics before taking this course.
AN excellent course. Hands-on training on the cloud makes an individual really involved. So far the best online course I have ever taken, and I have learned Python programming a lot from this course.
This needs to go much more in depth on the options for analysis, and provide more examples.
In addition, the labs and final exams were not fully completed/corrected/reviewed, so there were many erroneous issues, including assumptions made that was not clear to us students.
The general course content was okay. Unfortunately I didn't learn too much about Python and Data Analysis for Data Scientists. This was due to the following reasons:
1) a lot of interaction with not working IBM infrastructure. It took me around 3x as much time to get required things working on IBM cloud and IBM Watson compared to the time spent for actual assessments. It is annoying if it's getting that obvious that IBM wants to use the course to promote own products. This is sad as we all already pay for the course...
2) There occurred quiet some arrows in the labs which even after months (according to the discussion) have to been corrected.
3) The amount of hands-on training in the notebooks/labs was really small. It was not a lot one had to program on their own and the parts which had to be programmed were only an exact copy of what was already done before. Even the final assessment did not really contain a real task.
4) Many concepts weren't explained in depth. The explanations just stayed very superficial. Some concepts like fit()/fit_transform() which appeared in the labs weren't explained at all in the videos or in the labs. This led to a lot of confusion as could be seen in the discussion threads.
As we all pay for this course please increase the amount of actually explaining concepts in depth and the amount of real in depth hands-on training and reduce the parts on IBM Watson and other such stuff. Thanks a lot!
Avoid this class. Unfortunately, the labs have been broken for several weeks and there has been no helpful response from anyone other than stating that the labs are being migrated to a new platform. The migration has been woefully unsuccessful and the labs continue to be unavailable or non-responsive. I am very disappointed in this class with several weeks wasted trying get through the course.
There was 0 statistics, 0 intuition. This course looks good on paper but teaches very little of substance.
The course had plenty of errors in the videos, Labs and quizzes. The explanations were rushed at times and quite a bit was not easy to follow. The worst course so far!
Low quality.
Do not recommend this course at all.
Boring teaching method.
Full of errors.
No IT support for problems.