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    Back to Python for Data Science, AI & Development

    Learner Reviews & Feedback for Python for Data Science, AI & Development by IBM

    Filled StarFilled StarFilled StarFilled StarHalf Faded Star
    4.6
    stars
    41,909 ratings

    About the Course

    Kickstart your Python journey with this beginner-friendly, self-paced course taught by an expert. Python is one of the most popular
    programming languages, and the demand for individuals with Python skills continues to grow. This course takes you from zero to
    programming in Python in a matter of hours—no prior programming experience is necessary! You’ll begin with Python basics, including data
    types, expressions, variables, and string operations. You will explore essential data structures such as lists, tuples, dictionaries, and sets,
    learning how to create, access, and manipulate them. Next, you will delve into logic concepts like conditions and branching, l...
    ...

    Top reviews

    EH

    Jun 10, 2021

    Filled StarFilled StarFilled StarFilled StarFilled Star

    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.

    DR

    Sep 27, 2024

    Filled StarFilled StarFilled StarFilled StarFilled Star

    This course was really helpful in make me understand all the topics of Python from scratch, including the slightly advanced topics, of APIs, for my level as a freshman just getting settled in college.

    Filter by:

    7051 - 7075 of 7,558 Reviews for Python for Data Science, AI & Development

    Filled StarFilled StarFilled StarStarStar

    By Fabio B

    •

    Mar 14, 2019

    Too basic

    Filled StarFilled StarFilled StarStarStar

    By Leonard C

    •

    Jul 30, 2021

    too easy

    Filled StarFilled StarFilled StarStarStar

    By Yuhao L

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    Jun 25, 2020

    Too easy

    Filled StarFilled StarFilled StarStarStar

    By taha r

    •

    Jun 2, 2024

    OKKJUGH

    Filled StarFilled StarFilled StarStarStar

    By mostafaabuzeid

    •

    Jan 8, 2020

    It's Ok

    Filled StarFilled StarFilled StarStarStar

    By Γεώργιος Κ

    •

    Aug 23, 2019

    Shallow

    Filled StarFilled StarFilled StarStarStar

    By Waqas A

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    Feb 3, 2025

    its ok

    Filled StarFilled StarFilled StarStarStar

    By PENDLI M K

    •

    Nov 7, 2022

    hosome

    Filled StarFilled StarFilled StarStarStar

    By Wei w

    •

    Nov 23, 2021

    爬虫部分很烂

    Filled StarFilled StarFilled StarStarStar

    By Nour e h L

    •

    Apr 25, 2025

    goodf

    Filled StarFilled StarFilled StarStarStar

    By CHERRISH K

    •

    Sep 20, 2024

    ok ok

    Filled StarFilled StarFilled StarStarStar

    By MD S H

    •

    Feb 12, 2021

    great

    Filled StarFilled StarFilled StarStarStar

    By Amir R

    •

    May 25, 2025

    Good

    Filled StarFilled StarFilled StarStarStar

    By MANTRI L K

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    Feb 14, 2025

    nice

    Filled StarFilled StarFilled StarStarStar

    By Omkar D

    •

    Jan 17, 2025

    good

    Filled StarFilled StarFilled StarStarStar

    By KOMALLAPALLI K S

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    Nov 25, 2024

    Good

    Filled StarFilled StarFilled StarStarStar

    By Ahamed M S

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    Sep 2, 2024

    good

    Filled StarFilled StarFilled StarStarStar

    By Sweeti S

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    Jan 26, 2023

    good

    Filled StarFilled StarFilled StarStarStar

    By Farai

    •

    Jun 10, 2022

    good

    Filled StarFilled StarFilled StarStarStar

    By Chakradhar K

    •

    Jun 5, 2020

    cool

    Filled StarFilled StarFilled StarStarStar

    By Karumanchi N

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    May 21, 2020

    good

    Filled StarFilled StarFilled StarStarStar

    By Sumit C

    •

    Jul 27, 2024

    ok

    Filled StarFilled StarStarStarStar

    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.

    Filled StarFilled StarStarStarStar

    By David T

    •

    Mar 31, 2024

    This is a course useful in getting you onto the ramp to Python and the Python libraries "Pandas" (provides tools for data manipulation and analysis, in particular the "data frame" matrix as known in R) and "NumPy" (provides multi-dimensional arrays and numerical computing). It also explains basic skills in loading and writing files in various formats from and to disk, getting pages from the Internet, how to perform client-server communication in the REST approach and how to scrape websites. Sadly, it feels like a "0.8 version" of a proper course, or a course which has been abandoned. Content is affected by confusing text, typos, omissions of important points, code in "non-pythonic" style, in some cases code that doesn't work, or looks like it deserved a refresh to latest notation (the matrix multiplication with the '@' operator isn't even used). Sorely missing are chapters on "gradual typing" in Python, assertions and unit tests, all of which I consider a minimal must-know. On the other hand, the "coding notebook" practical exercises (based on Pyodide running in your browser) are the right way to go. Drilling into details and looking up documentation on the Internet is unavoidable, indeed it will give you the understanding that you actually need. The course seems also unsure about whom it is addressing. I doubt that aspiring data scientist need extensive explainers about how vector and matrix operations work, those should be assumed known. Still, all of this should be easy to fix by IBM in a fortnight (I left them extensive notes, too). On the other hand, the questions that Coursera throws at you in the "graded exercises" are rather embarrassing, and need serious rework, I want more interesting question than multiple choice questions about syntactic details, word definitions and Python one-liners (which cannot be written in Python btw.). Make it better, guys!

    Filled StarFilled StarStarStarStar

    By Kalin T

    •

    Jul 10, 2020

    Generally, the course is very informative and useful.

    However, the Python for Data Science and AI course is anything but helpful.

    The course requires the students to open a free IBM Cloud account, which is practically impossible in my case for unknown reasons.

    The IBM Cloud service is essential part of the certificate program, as it is used in most of the courses, but, however, impossible to use.

    IBM Watson Studio is a tool developed for Machine Learning and a part of the IBM Cloud service and an essential part of the course, but can not be used without a cloud account...

    I think the above sentence says it all.

    Through threads in the forum I requested a solution for my issue, which many other seemed to have. I received a few answers to my inquiry, suggesting a few workarounds like using a non-generic e-mail or at least a GMail, changing my network settings and so on.

    None of them seemed to work.

    The solution that was suggested at the end was to download the file from a suggested link, run the code locally and upload the result to GitHub...

    Seriously?!... o.0

    A few e-mails sent to IBM Cloud support remained unanswered.

    The saga above, as well as the lack of exercises, non-working code in the lectures and LAB really made me question my choice of certificate...

    The course does not contain much more information than the one you will find in the book by Murtaza Haider, parts of which are included in the reading sections of the course. If you are wondering if it is better to spend $35 each month on Coursera, or to buy the book for $20 and learn the same stuff... Well, I think you have your answer.

    I would not be recommending the course to anyone, as I am not sure If they will cope with the frustration around the process of finishing it.

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