A massive fall from the previous course (The power of Statistics) both in terms of scaffolding (especially with the Python labs) and accessibility. The key concepts that are explored within the videos, are certainly difficult to grasp but the main issue stems from the lecturer's inability to go over the basics of a concept and link it with a simple scenario to cement any foundational learning.

Terms, explanations, questions found within the practice exams and end-of-week challenges are almost shoe-horned in with very little exposure within the documentation/videos. This course is equivalent to being taught in a lesson by a teacher who passes you a textbook and tells you to read, and do all of the tasks, from a certain page. Finally, the lecturer seems almost like she doesn't want to be there/nervous when all they are doing is reading from a prompt.

I don't enjoy this part of the course as much as other parts. The instructor reads scripts like a robot. Week 4 and 5 are quite abstract. Many concepts just show the definitions without examples.

This is one of the worst course instructor I have ever seen. She's sitting still like a robot throughout the entire course. Completely visible that she's reading a script, looking over the screen. Seems like she dosen't wanted to be here in the first place. I was wondering the entire course, how the videos were approved in the final assesment? How come no one noticed the weirdest possible eye contact? I think she shouldn't conduct any other course ever.

Too many concepts without enough explanation. narration not engaging enough

I would not recommend it. I've finished half of the course and felt the course materials are mostly videos with only limited readings , and the videos are not so informative. In addition to that, the mathematical expressions are not very rigorous and sometimes confusing, with little explanation of the notations.

Disappointed with this module. I understand that there was too much to cover. But the module could have been extended. The practice lab and the project lab were vastly different. It was like learning basic arithmetic operations and being tested on calculus. The EDA steps during the project lab were not explored before, while there were ample opportunities. It is hard for a beginner to follow along. The tools used during the learning labs were not used in the project lab. Whole different tools were used. I did not learn a thing and I have to follow external resources to get my head around the concepts now.

The materials and presentations are excellent, but I am not a fan of the busy work involved in the end of course PACE portfolio work. Professional presentation is certainly important, but you end up diluting the course by including so much of this material. Furthermore, having worked on many projects, I can tell you that the workflow process is so iterative that trying to "shoe horn" the process into a four letter acronym -- which is better than the six letter acronym in the previous specialization -- is both reductionistic and frankly artificial. My suggestions are as follows: remove this excess material and "re-factor" the PACE/portfolio material into one course, perhaps the capstone course. Otherwise, I learned quite a bit.

-Not much reading material

-Speaker is boring

Course 5, 'Regression Analysis: Simplify Complex Data Relationships,' in the Advanced Data Analytics specialization by Google on Coursera has been an incredibly enriching experience. The course provided a comprehensive introduction to complex data relationships, simple linear regression, multiple linear regression, advanced hypothesis testing, and logistic regression. The end-of-course project offered a valuable opportunity to apply the newly acquired knowledge in a practical setting. I appreciate the clear explanations, real-world examples, and interactive exercises that made the learning experience engaging and impactful. This course has significantly enhanced my understanding of regression analysis and its crucial role in simplifying complex data relationships. I highly recommend this course to anyone looking to delve deeper into regression analysis and its applications in data analytics.

The regression modeling project allowed me to appreciate the importance of selecting the right variables and how they can significantly impact the model's performance. It reinforced the value of assessing assumptions and model accuracy. My understanding of hypothesis tests has deepened, as I learned how to use them to evaluate the significance of individual predictors. One interesting aspect I discovered was the potential for multicollinearity, where independent variables can be correlated, leading to unstable coefficient estimates and making interpretation challenging. This project highlighted the need for careful variable selection and assessment of assumptions in regression analysis.

Fantastic course! This is a challenging, but approachable course. It is very thorough, and gives you everything you need to succeed, I found it one of the more real-world applicable lessons that I have taken. I highly recommend that anyone looking to build a strong foundation in advanced analytics, and how they can apply in real situations, take this course! You also wind up with a pretty solid project that you can do a case study on for your career portfolio.

This course in the series proved to be highly intellectually engaging (every minute of the videos are sherds of information and knowledge), making it well worth revisiting. The fundamental concepts of slope and intercept, as well as metrics like AuC, were presented in a manner accessible to anyone. I commend Google for curating this curriculum with the explicit goal of imparting essential knowledge

This was a thorough walkthrough of regression analysis. The topic is heavy and has many details related to it, but the instructors provide great videos explaining the topics with visual examples, excellent follow-along coding walkthroughs that put the concepts into practice, and a project at the end that illustrates an in depth assessment of a data set using multiple regression and EDA.

If you are a beginner and wanna get a gist of key Regression Analysis concepts using python, I think this is the best place to start off as you put all your learning skills at the end of the course's data analytics project , which is a very thoughtful step by google to make their potentials learners equip their resumes with great critical thinking projects. Thanks to Google !!

I found this to be both a really interesting course and a challenging one. It could have been a lot harder but the explanations of the complex terms were really straightforward and there was a lot of time for meaningful practice. I highly recommend this course if you want to gain a good, broad understanding of the topics it covers.

In summary, this course is a game-changer for anyone seeking a deep understanding of regression analysis and predictive modeling. The practical, human-friendly approach, coupled with expert instruction, makes it highly recommended for those looking to enhance their data analysis skills.

Thank You Coursera and Google for this Course, I learnt a lot about Regression Model, from Linear regression Model to Logistics regression Model, assumptions, hypothesis testing and interpretation of results to stakeholders. Thank You NITDA for the Sponsorship.

The course material was prepared exceptionally well. I loved how challenging the end-of-course project lab activity was, one has to be patient and do some research in order to comprehend some steps in order to complete it.

Would highly recommend this course. Great instructor. Well-presented content and very helpful hands-on labs. Week 4's material on ANCOVA/MANOVA/MANCOVA seems to be rarely covered in other analytics/ML classes.

Covers a lot of content, but is well paced and easy to understand. Provides a solid foundation for further learning and exploration in regression analysis.

good instructor, slightly complicated notebooks, too much of new information and formulas at once, regression analysis is very powerful technique

Impressive! It is a highly recommended courses especially for those who want to become data professionals as well as data scientists! Thank you!

For me this course was good. Lots of good information with clear examples and resources to keep digging into.

I recommended for the same reasons.

This course is beneficial and includes concepts that could help improve and develop new skills, very structured and robust information.

This course is extremely exciting and challenging. I am finally starting to understand the power of models for predicting outcomes.