The course material was excellent , quizzes makes this course more efficient and handy, all the lectures are explained well , the most important part of this course providing notebooks of each week for self practicing and to judge our-self . Discussion forums are provided asking queries, Overall the course was excellent both for beginners and intermediate.

No Doubt COURSERA is always best AND MNC like IBM,Google courses associated with coursera are MIND-BLOWING.

The Instructors are so great at Explanation Part that hardly anyone won't Understand All the Topics

I would love to thank all the INSTRUCTORS who created such a Awesome Content for us.

My Personal Ratings For All the Instructors: 100 / 100

Excellent Course, i've learned a lot, i can analyze any data and give a conclusion from it. It's great course with a very clear explanation. If you are not understanding from the videos you can have a full understanding of the course from the Lab Notebook. The best is giving you a chance to access on IBM Cloud, creating new dynamic projects. Thank you

Initial part of course was easy, but the labs proved more and more useful. As I learned the course, I applied the charting skills directly to work, and was able to use Pandas to combine data from 3 databases, evaluate and report on the data to my company. It is already making a difference in our ability to make better data driven decisions every day.

Great Course! I got a great insight into multiple steps involved in data analysis using python starting from an initial data set to pre-processing it, exploratory analysis, doing multiple operations to create possible models and ways to evaluate the models. I hope to be able to use them to solve some sample data sets and come up with possible models

Did this after Andrew Ng's Machine Learning to learn to do the same things in Python. Great course for people somewhat familiar with Python basics (I used datacamp to get a feel for Python and methods etc. first). Labs were really good for reinforcing knowledge from quizzes and videos. Overall, very nice course - will recommend to others!

This is my first course that i completed, and i am very glad to do this .

thanking you for giving me this opportunity to enrolled this course

i learned a lot of new things from this course this was very fruitful for me.

the slides was nicely represented and the way of teaching was so amazing

i am very very thankful to all the Coursera Team

This course is very use for regression model end to end scratch of evaluation and easily understand the coding theory explanation but ridge regression is somewhat improvement is needed.

Finally, I suggested to this course for learning data analysis with python.

Thanks for wonder full opportunity to learn this course in course-era team...

This course is probably the most concise and well explained course I have ever taken on the subject. Materials are explained very well, and in a concise manner. The only downside is that the assessment for this course is based on quizzes, which are way too easy. Nevertheless, the course contains ungraded labs which are really useful.

Good course for beginners, kindly remove the project at the end of the course, make two projects one after completion of 50% of the course and the remaining after 75% of the course.

increase the questions that are mostly related to project, practical work, interview with more focus on the conceptual understanding, than on the syntax.

This is the most complex course in the IBM Data Specialization Curriculum until now. There is a lot to cover and I would advise the students to go through the notebooks for practice 2 times to make sure to remember everything. One thing that is a bit confusing are functions for creating plots as we did not cover them in details yet.

Awesome course on gaining Python skills for performing structured data analyses. If you are already attending the IBM Data Science certification, this course is a "step up" from the initial courses to bring a lot of things together. I would highly recommend doing it in the recommended order, else the learning curve may be too steep.

Wow! Excellent course that provides a great skills-focused overview on how to do data analysis with Python. The videos are first-rate, high quality and summarize the essential points nicely. The data set is real and it is used throughout the course and that helps understand the different features of data analysis taught by pandas.

Very good, learn what I needed to. Make sure to do the python basics first or if you have pre-existing knowledge of the basics, that's fine too. Having some pre-existing knowledge of statistics would be very useful and I don't think the explanations are sufficient to fully understand the concepts, but are sufficient to use them.

The course content is pitched at the right level. I suggest that the presenters provide complete threads of code for the earlier videos since some learners may not have the background skills.

I had challenges with electricity so I had to keep restarting the labs and that aected my performance.

Overall a very empowering experience

Not that the course was over-challenging. A few ideas were better explained (Ridge Regression, Polynomial Models & pipelines). Would have preferred to have had access to slides rathan than the textual notes. Thought that the discussion forum was extremely helpful and the staff who responded earned their rightl. Congrats.

Great introduction for using Python for data analysis. I found the segments on using Pandas, scikitlearn, and Matplotlib, particularly useful. Also, the labs' use of Jupyter notebooks, were excellent, because of the ability to introduce new variables or other data, and to see how it affects the outcome. Thank you very much!

This course gave me very good understanding on basic concepts in Data Science and how we can make use of python. I would recommend this course to people who are searching for basics of data science. If you are from programmer then you will be able to correlate software development life cycle and Data Science Development.

It was really helpful for me. Now i can clearly explain what is data. How we can explore data from a big data-set, How we can analyze different type of data-set. I am so much happy with this course. Now i will try to use this technique in my next steps. Special thanks Coursera community for creating this opportunity.

This is a good course with good instruction and labs that helped solidify my understanding of using Python and Jupyter Notebook to analyze data. I recommend it to others. Please note that there are a few areas where the libraries are out of date, but a quick google search should help you figure out how to fix it.

This course is mind-blowing.

The structure and curriculum were topnotch. I enjoyed every bit of the process.

Although it was challenging at some points, the patter of tutorship made it easy to navigate.

I am very appreciative of the entire team that put up this course.

Thanks a lot for this great opportunity.

Regards.

Very useful analytical techniques were learned such as cleaning the data, multiple linear regression, and working with test and training data. This course gave me a good foundation on the approach to analyze large databases. I also feel this will help in learning R because I now know the analytical process.

It's a great course to learn the basic concepts of Data analysis with Python and thanks a lot to COURSERA .

there is a personal suggestion to coursera regarding to increase the syllabus in the modules if possible.

feeling glad for completing this course and eagerly waiting to do more like these, Thank You.

Trust me! This is best course for beginners, This is how it should be taught. No previous coding experience needed, even mathematics used is explained clearly. Making notes will help you in future while writing codes. After this course, you can confidently move to other courses of IBM Data specialization.

Hello. Very cool course, designed not for beginners in python. Due to difficulties in translation, I had to additionally search for information on the Internet + take notes in Google documents. Sometimes you get very immersed in the ongoing process and forget about the time. Thanks to course developers.