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

4.6
stars
35,197 ratings

About the Course

Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching. You will use Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with APIs. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for anyone who wants to learn Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps as well as a number of other job roles....

Top reviews

MA

May 16, 2020

The syllabus of the course takes you in a roller-coaster ride.

From basic level to advance level and you won't feel any trouble nor hesitate a bit.

It's easy, it's vast, and it's really usefull.

TM

Nov 17, 2019

it becomes easier wand clearer when one gets to complete the assignments as to how to utilize what has been learned. Practical work is a great way to learn, which was a fundamental part of the course.

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301 - 325 of 6,171 Reviews for Python for Data Science, AI & Development

By Ahmed A

•

Jul 28, 2020

Bad videos.. Many methods and functions in the lab are not explained in the videos

To complete this course I spent most of my time reading documentations and searching to understand what is said as there is no enough explanation or even resources to read from.

(Maybe this method suits you but I didn't like it)

By Eleanor

•

Jan 8, 2022

Classes in IBM Data Analyst track seem to be for marketing purposes. They force you to sign up for IBM products, provide your personal information and credit card number, and then it doesn't even work. You really don't learn much in the actual classes, they just seem to want to advertise IBM products.

By Cem K

•

Jan 8, 2022

Absolutely worthless course. Waste of time. You will only get begginner knowledge after listenin to videos with tedius useless information. I knew python before and i watched in horror and disgust how horrible topics were covered and how unnecesarily complicated they were forced to become.

By Ahmet C

•

Nov 16, 2021

Examples in the videos was not good, at least giving some examples for necessary functions in videos would be good, If you don't want to put new videos than at least give students the documentation link to guide them so they can learn themselves with parallel to labs.

By Ivan G

•

Jan 26, 2023

It all makes sense till about half way into the course then it turns into some kind of gibberish and you get completely lost. The videos are just text to speech and the labs are very bland and not interactive at all. Waste of time and money.

By Rohit R

•

Dec 11, 2023

The entire Data Science Certification course is misdesigned. It is not at all beginner-friendly. It seems like the entire thing is rushing down on covering the concepts within 5-10 minutes of videos with insufficient demonstration.

By William M

•

Oct 25, 2023

You won't walk away with anything more than a superficial understanding of Python. The introduction is cursory, the course breezes very specialized python code for data science and there is very little hands-on practice.

By Lyn L

•

Feb 6, 2023

One of the worst courses/attempts at teaching something I've ever seen. Datacamp Python is a million times better and this random book I bought was way better.

By Mike L

•

Mar 2, 2023

Labs are garbage (only half of them load, most of the info in the lab is not covered by the video, we don't use an interpreter), videos are too fast

By Omer E L

•

Sep 28, 2022

Weeks 1-3 were really good, But the lessons in weeks 4-5 were really were incomprehensible and it was really hard to understand things

By Damen w

•

Dec 26, 2023

Trash. Have someone who knows how to put together a course rewrite this. The ordering is wrong and it skips steps. Very bad.

By Nikol U

•

Dec 29, 2023

It should be much more better explained for a true beginners. Also, there are many corrections needed.

By Zsolt d T

•

Mar 12, 2023

A lot of not-working codes and tasks unable to solve based on the lessons taught before

By Yashvir I

•

Nov 3, 2021

Lab is not working. I am not able to access any of the hands-on lab exercises.

By Ala'a A H A

•

Jun 30, 2023

its very bad, not clear and the videos doesnt explaine any thing.

By katlv z

•

Nov 19, 2023

Lots of ambiguous statements, and sometimes inconsistent content

By AKSHAT R

•

Jun 14, 2023

very poor course fuck off coursera

By Reza M

•

Aug 9, 2022

worst python course ever

By Maruf C

•

May 27, 2023

assignment problems

By Deleted A

•

Sep 26, 2023

IBM's Python for Data Science, AI & Development course on Coursera is an exceptional learning experience. This comprehensive course equips students with the knowledge and skills required to excel in the rapidly evolving fields of data science, artificial intelligence, and development using Python. The course curriculum is thoughtfully structured, beginning with the fundamentals of Python programming and gradually delving into more advanced topics like data analysis, machine learning, and AI. Each module is well-organized, with clear explanations and hands-on assignments that reinforce learning. What sets this course apart is its practicality. Real-world examples and industry-relevant projects allow you to apply what you've learned in a meaningful way. The instructors are knowledgeable, and the peer-graded assessments encourage collaboration and deeper understanding. Furthermore, IBM's reputation in the tech industry adds credibility to the course, making it a valuable addition to your resume. Whether you're a beginner or have some Python experience, this course provides a solid foundation and the confidence to tackle complex data-driven projects. In conclusion, IBM's Python for Data Science, AI & Development course on Coursera is a game-changer for those looking to thrive in the world of data and AI. It's an investment in your future that offers both knowledge and practical skills to advance your career.

By Anthony N G

•

Oct 4, 2019

This course was a perfect introduction to python for data science. I already have a B.S. in political science which required a few semesters of statistics. We mainly used Excel and SPSS. I wish I had taken a course like this because I’ll say that I much prefer Python to SPSS and Excel. I find Python more functional but far less user friendly. What helped a lot here was that I have a background in windows and pc hardware. I also have a little experience with Linux and .bash scripting. I’ll admit, this course would have been much more difficult without the computer knowledge I already had.

I’m currently working full-time trouble shooting large 3D printers 40 hours a week. I’ve been pondering what to go to graduate school for. This course has helped with that decision. I’m leaning toward a masters in the applied data science.

I plan on taking the other data science and applied data science courses on Coursera as well. Any and all continued learning I can get will be valuable.

What was most challenging? Learning the syntax and structure of the python language. I’m still learning it and it’s going to take quite a lot of effort to master it. Attention to detail is an absolute must in programming or coding—albeit a short script or manipulating a data set.

Also, I found that the Anaconda suite was the best choice to complete the course. It was a little more user friendly than the bare-bones IDLE/Python combination.

By Owais A

•

Mar 15, 2024

Content Quality: The course should cover a broad range of topics from basic to advanced Python concepts such as data types, data structures, control flow, functions, classes, modules, and popular libraries/frameworks (e.g., NumPy, Pandas, Flask, Django). Clarity and Explanation: The instructor should be able to explain concepts clearly and effectively, catering to learners of different levels. Complex topics should be broken down into understandable chunks with practical examples. Engagement: The course should keep learners engaged through interactive elements like quizzes, coding exercises, and projects. Projects and Exercises: Hands-on projects and exercises are crucial for reinforcing learning. They should be diverse, challenging, and relevant to real-world applications. Community and Support: A supportive community or forum where learners can ask questions, share insights, and collaborate can enhance the learning experience significantly. Updates and Relevance: Python is an evolving language, so the course content should be regularly updated to keep up with the latest language features, best practices, and trends in the Python ecosystem. Reviews and Feedback: Checking reviews and feedback from previous learners can provide insights into the course's strengths and weaknesses.

By Courtney B

•

Dec 4, 2018

I was a complete newbie to Python, and coding in general, and this course made it easy for even a beginner like me to understand. I would honestly love to take an extended version of this course. That said, I have recommendations for improvement:

1) the labs didn't really make you think terribly hard about how to solve the questions, and I would have loved more complex lab work, especially because of the next point...

2) The complexity of the final project basically skyrockets from the rest of the course work. I feel like an extra week or two of going over the additional knowledge necessary to really succeed in the final project without major struggle would help tremendously. Conceptually, it seemed like it should be REALLY easy... if only I had a little more applicable practice work under my belt before hand. (I finished it successfully, but it was a bigger struggle than it perhaps should have been. I think many other people are in the same boat.)

By Crystal Y

•

Aug 24, 2020

This action-pack course is exactly what I am looking for. It's down-to-earth and practical. Instructors explain with videos once, then you get walk through in the labs, like a step by step guide.

The videos are of bitesize length so it's easier to concentrate on the concepts., and followed by quizzes that ask only the essentials. i love the part that i can experiment with the code myself after researching the concepts further on the internet.

It may be pretty demanding for complete beginners because each concept is introduced very succinctly, so if you have no clue with python at all, i think you need to research extra a lot in order to understand the concept. There are also a few minor typos which may affect the understanding, just really minor ones like a becomes b while b becomes a, or some general english typos.

Perfect for those who want to get a taste immediately what data science looks like, like myself.

By M. F M

•

Oct 19, 2023

This one is definitely one of the better courses by IBM. Joseph Santarcangelo and the staff needs to be commended for putting in so much high quality content. The videos are sharp and bite-sized with questions within the videos to keep focused. There is sufficient coding in the lab exercises and the videos are not too theoretical like the low quality courses such as IBM's React and Node.js courses. There are still room for improvement as some of the labs contain errors, the practice quizzes are copy-pasted from the super-easy video quizzes and there is no project to tie everything together. I think a data science project involving APIs would have been an excellent way to end the course instead of the easy multiple choice final exam. Finally, I really think "AI" should be removed from the title as it is a bit misleading. There is no machine learning, deep learning or anything AI related in the course.