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 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.
創建者 SOUVIK B•
Good course if you are beginning data science. You don't need much of python experience but will be better to have if you want to quickly finish the course.
創建者 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.
創建者 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..
創建者 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.
創建者 Shayan K•
There must be a slightly high level of Quiz, assignment and Project and must have to add some more advanced concepts about statistics and probability.
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.
創建者 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
創建者 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
創建者 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.
創建者 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.
創建者 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.
創建者 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.
創建者 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.
創建者 S. 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.
創建者 Venkatesh E•
Through out the course i have learned alot like data visualisation mainly.I think i have completed successfully basics for machine learning.
創建者 Randy 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.
創建者 Saurabh A•
Good course for beginners. Can introduce little more concepts such as multi-collinearity, model accuracy etc to make it even more complete.
創建者 Victor D S C•
A great explanation of the concepts and methodology in data analysis , i wish we couldve gotten more peer reviews like the last excercise
創建者 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).
創建者 Prasad T•
need better practise questions preferably to write program instead of multiple choice answers plus needed more theory of the topics given
創建者 Jonathan B•
Great material. Very comprehensive. The only knock is sometimes the slides, notebooks, and quizes have typos or are not super-organized.
創建者 Aurelio L G•
Una visión muy amplia con acercamiento a una amplia variedad de herramientas. Faltan más ejemplos de uso, ejercicios y casos prácticos.