Learning is a process of blending theory and practical in equal portions to provide intellectual inputs to get tangible outputs. This course is a perfect example of it, as it consists of ample hands-on lab sessions for each module, where anyone could practice what they have been taught through the videos. The videos are super explanatory, where even a beginner could learn from scratch with passion and love. I take this opportunity to thank all the instructors, resource providers and contributors, and wish you all the very best to keep your knowledge-sharing efforts with pride and joy.

This course gives me a brief understanding of data analysis based in the use of Python. Since I have already had a foundation of the basic knowledge of coding with other programming language, this course started with introducing several basic packages for data science followed with the use of each package. Also, in week 4 and week 5, the course provided me with the idea of generating statistical models to train our data sets. The thinking method of evaluating a model will help me a lot in my future studies in the field of machine learning and deep learning.

It is an excellent course for beginners in Data Analytics. It teaches you all basic concepts required for data analysis which includes data pre-processing, data wrangling, data formatting, data normalization, data binning, Exploratory data analysis and data modelling. It also teaches you descriptive statistics including, Correlation, ANOVA etc., It also helps you with basic data visualization, Linear regression, prediction, decision making, Model evaluation and refinement using Ridge Regression and Grid Search. I find it very useful for beginners.

To pass this course is really not easy as it doesn't just teach us how to code to fulfill the data analysis but it delivers a lot of relevant knowledge of statistics as well, including linear regression, polynomial regression, ridge regression, MSE, R2, ANOVA, etc. Coding is not difficult but understanding those methods of analysis is hard. so if you have little basis of statistics, you have to work harder. But I feel more confident after the course because I have gained one more skills. Keep on going and embrace the future.

The course nicely gives you a glimpse of the endless possibilities in the area of Analytics. It showcases how data can be easiely and speedily analyzed using Python if you are clear even with the basics of Python programming. It provides a prefect platform to gain skill sets needed to be a great Analyst.

The course is wonderfully desined, the material within seems self-explanatory and you won't have to struggle to grasp the concepts taught. Labs are awesome and so is the team who made the course what it is. Really loved it!

Honestly it is one of the best courses I've attended in Data Science. All the ambiguous concepts that I read in the internet and couldn't understand were clear in this course and I didn't have to struggle to get them. The way the course is structured, the visual materials, labs, quizzes and assignments ensure that you leave the the course with good theoretical and technical understanding. Thanks for making it easy to learn Data Science and python! I would definitely recommend this course if you want to have a good start.

Loved the course overall. Truly amazing! Professors did a really great job in making and structuring this course session by session.

A good course to learn know-how of Data Analysis using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

Become a Trustworthy Data Analyst

This course provides the knowledge and skills that form the foundation for data analysis. Students learn how to use Python Packages and gain experience creating dataframes and manipulating data sets for computation and visualization. Extensive work on building and evaluating models is included with explanatory lectures and hand-on labs to work with real data. Students' data analysis work will be supported by applying proper of model optimizations learned in the course.

A very good course in general, everything is explained in easiest to understand way that can be easily absorbed by students with little or no knowledge about data analysis and machine learning (mainly Linear Regression in this course). Although the exercises provided by this course is considered a lot, and more than enough to cover the exam and assignments of this course itself, but I hope the creator can put in some optional exercises to help us practice more and more and eventually being job ready.

A step-by-step and detailed introduction to data analysis using Python. It covers a 0 to 1 understanding from importing data to evaluating models, and offers hand-on labs to run codes. The content also includes all the packages and libraries necessary and essential to do data analysis. The courses are somehow in detail, if not, hard, but the tests and assignments are easier. I am sure I will always review the codes I have learned in the course in the future when I go deeper into data analysis.

I audited the course. I did not complete the quizzes because my goal was to get a very quick overview of pandas and scikit and pick up on basics. This was at the right level for me and did not go haphazardly. It did not try to convince me that something was simple, hard or not important.

I recommend this as a starting point for most who have little experience with Python but are well-versed in programming otherwise and want to get a look at a little of the ecosystem for ML using Python.

This course is great!!!

I just have a little suggestion/request that is on week 4, Pipeline theme, that they could detached it from polynomial regression and dedicate a couple minutes to detail it a little bit further on code, applications anda variations. (it was spent exact 1 min).

Besides it, it was a very practice course, I learnt a lot and certainly this course expanded my tool box in my portfolio and for sure I'll have to continue my practice to domain it properly!

I will say an excellent class! You will learn a lot essential data analysis methods, and the concepts.

Ok, it's never easy for someone who never learned such knowledges before, now encounters all those statistics concepts along with python code. But still, this class managed to use an easy way to explain all those abstract concepts. The forum also helps a lot to explain some difficulties. You might feel lost in the models, but once you learn it, you feel good.

Really good stuff in terms of outlining what is necessary in order to properly analyze the data. One thing to note is the powerpoint slides are off sometimes. Some of the stuff is not spelled correctly in the code.

Another issue is that x and y axis variables will be assigned, but be on the opposite axes (I.E when x = df['price'] but in the scatterplot it's actually the target variable, and thus on the y-axis.

This is an excellent course to begin with analyzing data in python. However, it would have been even more useful and interesting had it contained some more discussions on the topics like logarithmic transformation of features, when to apply it, how to do bi-variate and mutivariate analysis, exercises on topics like manipulation of dataframes using pivot, melt, crosstab etc.

Fantastic introduction to some of main python libraries and functions used in order to do anything related to data analysis, also a good entry point for machine learning, big data and other data science specialisations - highly recommended for anyone comfortable with high level scripting and basic oops concepts - if you don't then best take a basic course in python first...

This course is useful for statistics students who are not taught any programming languages before. It gives us a quick way to organize data. It is also an excellent online course with lab assignments by the end of the modules to practice the Python. I would say it would definitely benefit my career as data is increasingly available nowadays at any corporations.

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...