great lectures and projects. on data analysis topic, IBM has chopped contents into many small courses, which make student confused and hard to find which one to take. IBM should consolidate them into 4 or 5 courses that are focused, heavy weighted, so that students can build rock solid knowledge and skills.

The courses are very good to get familiar with Data Science and what it essentially is. I would have like practise examples with answers after each chapter to practise it in but that is just how I learn. Overall I learned a lot about new resources and how to do certain things in the Data Science world.

I liked the course. Highly recommended for someone who is looking for coding experience for data Analysis using python. Please practice the lab that will make you confident. Only thing which bothered me is the final assignment review. It was not correctly reviewed. I lost 4 marks. Hope this helps!

a few discrepancies here and there, please see the comments in the discussions. Other than that, very good! This course was more difficult than the others, and so i guess this is why employers prefer potential employees hold a PhD, or at least maintain a high algebraic/calculus/statistical aptitude

This is course is very useful to understand the data and analysis. You should do the labs because give us clearn understanding the concepts which see in the video lecture. So concept are difficult to understand like as Regression in the first time but if you watch the video again it will be clear

Actually one of the better courses in the Data Science program. Information is useful, test/quizzes are related to the material, and the final exam is appropriate difficulty to the material and information being applied. This course was challenging yet the tools needed to succeed were available.

The underlying basics of Data Analysis with Python were deeply conveyed. Simple examples and easy to operate commands were greatly described. I would suggest everyone take this course whether or not they know to code. It is always great fun to learn new concepts and Coursera makes it possible.

Some of the Regression-model and Plotting topics that were tested on the Peer-Graded Assignment should have gotten a little more time in the videos. The best solution to this would be to put the "Data Visualization with Python" course BEFORE this one, as it devotes more time to these topics.

Successfully completed the 'IBM Data Analystics with Python' course, gaining proficiency in Python for data analysis and visualization to drive business insights. This certification marks a significant step forward in leveraging data science tools for practical, real-world data challenges.

Contained some simple grammatical errors, as well as some syntax typos in some of the modules. The most relevant thing I would criticize is the lack of depth with describing certain topics ion the modules as they can be very complex. I recommend studying the section notebooks thoroughly.

There is a lot to unpack in this course. If you have a statistics background, this may seem kind of trivial, but for the rest of us it is loaded with ways to view data. My only criticism would be that it sometimes skims across an advanced topic without really giving a general overview.

This course gives a very clear view of the tools used to find the best way to analyze data when looking for the best model to predict target values. The use of Jupyter Notebooks to run code for the data analysis is very useful and enables the student to experiment on his own for options.

I have start this course without knowing any Python code. I made it through but with a lot of rock with all the code. like a For loop or simple Python code. I suggest to study basic Python code then start this course but this course did push me a lot on Python code learning with Youtube

Only 1 thing i miss in this course : some extra reading material because especially the last subjects here are a bit tricky to comprehend in full. Presentation overall is great, the labs are really helpfull as they are packed with excercises AND extra info. So yeah : take this course!

This was a good course, but didn't have as much labwork as I would have liked. There were a lot of labs, but they were mostly already completed by the instructors - more of a read-along than actually doing work yourself. That said, it was a valuable course and don't regret taking it.

This entire course is elucidated through projects and their respective tasks and assignments, allowing for a comprehensive understanding. Skills: Data Analysis, Visualizations, & Predictive Modelling Model Selection Python: Pandas, NumPy, Seaborn, Matplotlib, SciPy, Scikit-Learn

This was a very challenging course. i don’t think I had any business choosing an intermediate level course because I have no experience in Data Analytics so I am a beginner. It was very interesting to see how statistics and math concepts were applied though and I did learn a lot.

Great course to help us understand more about Python libraries. Just marked as 4 stars because I wish we had a better conclusion, showing us how to explain the charts and values to a meaningful insight for decision making. There could also be more dataset examples for training.

This module required a lot of effort and dedication in studies. It was very gratifying to complete it. Particularly, I will have to study the topic of this module a lot. I can say that this module deserves a separate course. Thank you to everyone who helped me along this path.

The course teaches an incredible amount of information in a relatively short time. The downside to this is that users don't get enough practice within the course on the data analysis methods and functions taught. Additionally, there are a lot of typos that need to be fixed.

Course was very useful and helpful. However, there are so much new and complex information being introduced in just one 4-6 minute videos, which can be difficult to understand, and may require to watch the same videos over and over, as well as alot of practice to be done.

Pretty good class. Ridge Regression and GridSearch were new topics for me. Felt the explanation of hyperparameters could have had a little more detail and the Week 5 lab has some details to correct. Lesson on polynomial regression and under/over fitting was well-done.

I think the breadth of content in the course was a bit too wide. More modules, and Python content, focused on exploratory data analysis could've been expounded upon, instead of so quickly moving into predictive analytics. Nonetheless, I did gain value from the course.

This module is loaded with concepts. Even though they are introduced in a logical sequence, it gets a little overbearing and tend to lose the relevance in the context of car price prediction. At least, now I am aware of the techniques, methods and python's capability.

Good introductory course. Even though it is an introduction, the course would benefit a lot from including a bit more of theory, even as optional material. For example, including theory about ridge regression, instead of just mentioning how to implement it in Python.