I started this course without any knowledge on Data Analysis with Python, and by the end of the course I was able to understand the basics of Data Analysis, usage of different libraries and functions.
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.
By Sreelatha V•
Very detailed and guided course that provides an overview of data analysis in Python with short assignments after each video and interesting lab courses.
By Guilherme V•
insufficient statistic, as the name of the course is Data Analysis, i would expect more classes about the different distributions of data, pdf and pmf..
By Katarina S•
One of the best courses in the IBM Data Science Specialisation.
I would like to have more quiz questions and opportunities to practise what was covered.
I would have given it 5 stars but they barely went over polynomial regressions and pipelines and it was a major portion of the end of class assignment.
By Wenyu X•
pros: well organized, clearly explained each step, useful
cons: frequent errors in both videos and the lab, especially on the questions part in the lab
By Maksym S•
Final exam was too complicated. I have 2 masters degree and for me it was clear, but for other it is too complicated.
P.S. it is my personal opinion
By BINAY K•
Course is good, but in this short course it is covering lot of thing thatswhy lot of topics are just touched intead of going little bit deep into it.
By Sergio F C C•
You learn a lot, good intro to data science with python. Labs have typos and can be confusing at times though, the only thing that could be improved.
By Aurangazeeb A K•
A very interesting and easy course. Anyone can catch up with big concepts with little effort. Thank you Coursera and IBM for this wonderful course.
Course is nicely designed and pare explained well.
I would have liked to see the steps along with the final answer to the peer assignment questions.
By zara c•
Very good course. I wish there were more hands on exercises. We only had a chance to practice in one lab per module; otherwise, I learned a lot.
By Ponciano R•
Great course to start learning python applied to analysis, but after this, I prefer to use R. Less complicated and can obtain the same results.
By Sifat S•
I find this course useful. But some of the contents are little advanced all of a sudden and feels some important explanations are not covered.
By Venkatesh E•
Through out the course i have learned alot like data visualisation mainly.I think i have completed successfully basics for machine learning.
By Randall G•
I feel like this section needs some more hands on labs. Great topic over view and application. Not to much in the way of math unfortunately.
By Saurabh A•
Good course for beginners. Can introduce little more concepts such as multi-collinearity, model accuracy etc to make it even more complete.
By Shreyas S•
It was a good course overall. Would prefer explanations at a slower pace and more examples for each of the techniques explained.
Content : 5/5
Labs : 5/5
Final Assignment : 3/5 (It was quite easy to complete as there we instructions and code already written for you).
By Prasad T•
need better practise questions preferably to write program instead of multiple choice answers plus needed more theory of the topics given
By Jonathan B•
Great material. Very comprehensive. The only knock is sometimes the slides, notebooks, and quizes have typos or are not super-organized.
By Aurelio L G•
Una visión muy amplia con acercamiento a una amplia variedad de herramientas. Faltan más ejemplos de uso, ejercicios y casos prácticos.
By Subhasish D•
The learning are too basic, trust me in real world things are much critical. Probably coursera can help us with that kind of knowledge
By William O•
One the greatest course of Data Analysis. The info given about statistics is very important and accurate.
Thank you to the instructors.
By Matthew A•
The more advanced portions of the course felt a bit rushed without enough examples and hands-on work to really reinforce the concepts.