Learner Reviews & Feedback for Python for Data Science, AI & Development by IBM
About the Course
Top reviews
PJ
Dec 1, 2020
It is a good course and teaches with the basic of Python so that anyone can understand it very well. Videos are good and can easily be understandable to anyone who is new to Python and Data Science.
EH
Jun 11, 2021
It is a very valuable course that I have learned for the Python skillset. It contains some advanced methods. It helps me to build more confidence in using Python and understand the concept in general.
6951 - 6975 of 7,453 Reviews for Python for Data Science, AI & Development
By Ipsit B
?Dec 26, 2018
it is good
By Saurabh K
?Mar 11, 2025
Excellent
By Fabio B
?Mar 15, 2019
Too basic
By Leonard C
?Jul 31, 2021
too easy
By Yuhao L
?Jun 26, 2020
Too easy
By taha r
?Jun 3, 2024
OKKJUGH
By mostafaabuzeid
?Jan 8, 2020
It's Ok
By Γε?ργιο? Κ
?Aug 24, 2019
Shallow
By Waqas A
?Feb 3, 2025
its ok
By PENDLI M K
?Nov 8, 2022
hosome
By Wei w
?Nov 23, 2021
爬虫部分很烂
By Nour e h L
?Apr 26, 2025
goodf
By CHERRISH K
?Sep 20, 2024
ok ok
By MD S H
?Feb 13, 2021
great
By Amir R
?May 26, 2025
Good
By MANTRI L K
?Feb 14, 2025
nice
By Omkar D
?Jan 17, 2025
good
By KOMALLAPALLI K S
?Nov 25, 2024
Good
By Ahamed M S
?Sep 2, 2024
good
By Sweeti S
?Jan 27, 2023
good
By Farai
?Jun 11, 2022
good
By Chakradhar K
?Jun 5, 2020
cool
By Karumanchi N
?May 21, 2020
good
By Sumit C
?Jul 28, 2024
ok
By Anastasios B
?Jul 6, 2021
I was very excited for this course, but felt underwhelmed overall. I have some programming background, so expected the course to seem a bit slow having been introduced to topics such as basic data types in Python before. Week 1 material, for example, was simple enough to go through in less than a day. As the course progressed to more complex and interesting topics (e.g. APIs, Webscraping, etc.), however, the course seemed incredibly rushed. Instead of understanding the set-up, it felt as though the lectures simple flashed multi-lines of code. The labs similarly seemed to grow in complexity, but shrink in explanations. The labs often incorporated libraries or methods/functions that had not been referenced in the lectures, often with little added explanation/introduction. Similarly, the incorporation of the Watson engine seemed like unnecessary advertising. It was not necessary to understand Python for data science and AI as part of the exercises. So much time was spent explaining how to add arrays, while it took 4 minutes to go through summarizing dealing with csv, JSON, and XML files. It also seems that later lectures had more typos in slides, again giving the impression that the later sections were rushed. Even the final lab randomly referenced a library for processing image files, which seemed to come out of left field. The course could've stopped at Week 4 or maybe split the focus between pandas and numpy between Weeks 4 and 5. Or more focus on exception handling or classes and objects might've been useful. Use of Jupyter notebook is helpful (it really adds to nice-looking labs), but I think it would've been better to have files/resources that could be downloaded and saved locally. That way, any files written/read could also be inspected more closely. Given the title of the course (and IBM as the provider), I would've expected more. With the exception of the weak Week 5, the rest of the topics seemed more like an intro to programming (using Python as the language of choice), rather than a focus of Python for Data Science, AI & Development.