the course content is excellent but some Technical issues occurred in doing the lab exercises

The lesson need more explanations on Polynomial Regression, Pipeline, Ridge Regression.

too much content all at once

Begins relatively clear. The practice labs were coherent and straightforward.

Around Week 4, things started to get convoluted. Small things, things that you don't notice at first.

Week 5 was where it really started to fall apart. You could tell whoever made this course lost interest or just did not have the capacity to teach the information effectively.

A great example of the lack of understanding or knowledge of how Coursera works is something you can view yourself.

Week 6 is the Final Project

Week 7 is one statement about your certificate.

Usually in most courses, the final project will be in end of the final week. That week having multiple modules that you have to complete leading up to the final. It was worrying for me as I thought the approach to this was on accident, but it seems likely that it was just due to ignorance.

Just as well, the Final Project was botched, the software and questions were depreciated and even written wrong by the creator. And when you would upload your pictures in the end to show you had worked out the problem, one of the upload buttons was missing in lieu of the letter "Y"....

Y indeed. Y was the ending of this course so terrible? A little more investment in the people you are teaching would go a long way. Very disappointed.

Course lacks thorough rigor or genuine assessment.

Labs are training on copy/paste and using the Shift+Enter command in the Jupyter notebook.

Assessments are multiple choice. No assessments on ability to write scripts to undertake data analysis to seek solutions.

Again, the use of the IBM cloud is a useless buffering of site traffic for your own products and does not provide anything for the course. Little to no 'challenge' questions that push the student to go beyond the hand held procedure of the labs.

The course makes you aware of some Data Analysis techniques, but you learn very little. The explanations are very superficial. And since nearly all the code is are already there, you are not forced to think about the concepts and methods.

The course was quite good until Week 3 but after that it was poorly structured. A lot of concepts were randomly introduced without proper explanation in Week 4 and Week 5, thereby killing the fun of learning.

This course ranks the least of the wonderful courses I have taken with coursera. There is definitely room for improvement in the delivery of materials.

I'll be honest this course for a beginner is difficult and incomprehensible as thereare many new things introduced which are not explained properly

yOU DEFINITELY NEED SOME BASIC PROGRAMMING BACKGROUND. i FOUND IT TOUGH

many mistakes throughout

The topic is very interesting, but the execution was poor. Code and numbers were just being read at me, instead of focusing the recorded lectures on teaching concepts and troubleshooting, and leave the code to be read by myself in the labs. Also, the quizzes along the way were nearly useless: only two questions, a "pass with at least 50%", and the questions asked were very superficial. This is the most poorly executed course I have taken on Coursera so far.

The course structure and videos are nice, but THERE ARE SO MANY ERRORS in the videos. I spent so much time to google and fix these errors. It is really terrible and I dont understand how people gave the high grade. I stopped watching videos after Week 3, because I fed up correcting their errors. Although people have mentioned it long time ago, but nothing has changed. Really shame on Coursera and IBM that have such quality!!!

Not well designed at all.

explanations are lacking

I read the book 'An Introduction to statistical analysis using R'. To reach to the concept of ridge regression it took about 3 months (as i can only spend an hours a day study hour) and page number > 200 for me to understand the statistical concepts of ridge regression, cross validation etc. And still I was tentative in R. Now, based on this video course and labs, the learning concepts and python implementation could just be done in 2 weeks time (spending 4 hrs on weekends). A lot of effort has been put in this course to make it sound simple. Thank you authors. Wish you continued motivation to design such courses.

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.