many mistakes throughout

Honestly, I'm not sure why this course has such a high rating. I feel like it can't possibly be a reflection of what actual students felt about the course. Reading the other reviews, it's clear some of the issues people had with the course were not the course-designers fault. But, there were some tissues that are simply inexcusable. For example, typos in the lectures (especially towards the final week) show little to no proofreading was done. A lot of the labs involved "Warnings" that the instructors didn't explain to students (and so obviously some students got confused by them, even though they were inconsequential). And the final peer-graded assignment was a complete mess. The first few questions are numbered Question 1, Question 2, Question 3, etc. But the last 4-5 questions are not numbered making it very annoying to upload screenshots for each question. The directions in the assignment were simply wrong. For example, one of the questions didn't even have a prompt, just an empty text box. Someone asked about it in the "Discussions" and a staff member replied but it was never fixed in the assignment.

Trying to put the typos and logistical confusion aside, the course material was oddly organized and students were never really given an explanation as to why the concepts taught were being taught in this way. My least enjoyed course of the whole specialization.

I'm sure a number of people put loads of time into this, so, thank you!

This must be the absolute worst online course I've ever come across ever! First of all, there are so many typos and mistakes in the course material, which is totally unacceptable! Then, there is no logical continuity in the subjects presented. The course is supposed to be intended for beginners, however, there is no background information or reasoning behind what is presented. Many times, one may only copy and paste python commands without knowing why or what it means at all! Unfortunately, This course is a disaster, and hence, NOT RECOMMENDED AT ALL!

Ton of errors, both minor and major, in the videos and the quizzes. For example, saying the a difference between two variables is significant because p > 0.05. I report them all and I've stopped counting.

Not professional at all.

HOW IS THIS COURSE BEFORE DATA VISUALIZATION??????????!!!!!!!!!!!!!!!!!!!!!!!???????????

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.

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.

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.

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.

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.

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...

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

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.

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.

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.

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 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.

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.

The first "week" was way too simple. I believe things like "what a file path is" should belong to another course. The last 4 "weeks" gave a good picture of where to start with data analysis. The whole course can be completed after 5-10 hours (depends how long you play with the dev tool).