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.

It is a really well-planned and informative course. The labs provided after every chapter are indeed a lot helpful to understand, recollect and visualize what we learnt during theory lectures. I would recommend this course to all as I found it helpful to improve my Data Analysis & Visualization skills.

I always fascinated towards programming languages from university days as it makes the complicated work easier. as having basic cleared about python helps me to sail out through this course. all levels are thoroughly covered with proper scripts which make better understanding even for newcomers also.

Great course with helpful excersise. The only thing that needs to be looked into is peer review for projects. One of the peers rated my project 8 out of 18 and i was asked to re do but because i knew it was correct i just resubmitted without changing anything and 3 peers who reviewed gave me 17/18

It was a very interesting and correctly paced course for learning Data Analysis with Python. The course content and the assignments were very helpful in understanding the course well. Will recommend this course to all who want to do a well paced introductory course on Data Analytics using Python

This course is outstanding valuable for the beginners who wants to build their career as data analysist. I have learned a lots of valuable statistical and progrmming for data analysis. Thanks to all instructor to give us such a opportunity to learn such kind of code and method for data analysis.

This was a great introductory course to statistical modeling with Python. I learned a lot of the basic methods to perform linear regression models and to describe statistical variables. The final assignment was slightly challenging, but doable if you follow the labs. All and all a great course!

Awesome. A must take course very handy at giving the foundation of data analysis with python and what a nice introduction to linear regression with the library sklearn. For more it looks more like an in-depth course in linear regression. Kudos, the explanations of concepts were well approached.

This course starts with Importing the dataset in Jupyter Notebook, followed by Data Wrangling, Exploratory Data Analysis, Model Development and Model Evaluation, and end with the final assessment applying all the concepts learned.

Thanks to IBM and Coursera for this great learning experience.