Good to set you up for advance courses.

Great course, really helps get you into the right mindset for becoming a data scientist.

Great intro

Not met what offered. I really don't know why but Instructor was in a hurry and like, he was in the position of instructor by obligation.

Maybe, He has knowledge of the subject, but definitely does not have even basic skills of teaching.

Because of this course, I am not planning to follow other courses on this specialization.

execellent i am very to gland get this certificate .. it is so valueable for me. the first one of data science track

A great intro to the course. I am not the biggest fan of the automated voice, but it gets the job done. I do like the secondary lessons written out with bulleted lists and close-ups of the slides. That is like a helpful review.

Basic introduction for the specialization, principles of data science, and installing stuff, it's fine to get started but could get hands dirty with R more quickly. Overall the plethora of 4-5 star reviews for this specialization seem generous. You will learn a good deal but there is heavy focus on R and academics of data science (Rmarkdown, Knitr, shiny apps etc), only 3 courses (6,7,8) get into meat of statistics/regression models and ML; the capstone project is interesting but doesn't use much of this stuff, it gets bogged down in technical work with new R libraries for text processing. The material is a few years old and not being maintained, discussion forums and interest/participation feels stale. Take some time to look at syllabus and compare to other courses for what you want to learn before committing many months to this specialization.

Providing feedback means giving students an explanation of what they are doing correctly AND incorrectly. However, the focus of the feedback should be based essentially on what the students is doing right. It is most productive to a student’s learning when they are provided with an explanation and example as to what is accurate and inaccurate about their work.

Use the concept of a “feedback sandwich” to guide your feedback: Compliment, Correct, Compliment.

Coursera has given new life to me

Good but need more explanations for topics.

This course was in many ways the first day of lectures, get your syllabus, buy your books, install your tools, etc. I would give it 5 stars but the lectures inclusion of internet addresses that aren't links and aren't included in the transcript led to a lot of time paused and typing out long addresses.

Would be great to have more reading materials

!!!STOP DON'T TAKE THIS COURSE!!!

%100 pure advertising. There is a moment I felt like I learned some thing, but rest of the course I played with x2.0, of there was more I would have get it.

Putting this into Specialization requirements is smart from your perspective, you are basically saying if you want to reach Capstone pay me $50 more, but at least fix the typos you made during video, just a little respect to your subscribers. But right now, I highly doubt that Capstone Project will be something serious that I want to mention in my Linkedn. There is also downside of what you do. But since you are in between the top rated courses either nobody uses Coursera anymore or people are silent enough and patient enough.

You are all Scientists like me, I'm also biostatistician but I would never ever post a course like this to any platform. I'd rather use Google or Facebook ads to lead people here.

If somebody wise enough to get Data Science Course, he should be skillful enough to download R, click next and install it, and R has help for it, shows you step by step. GitHub is free platform, anyone who can signup for Coursera can signup for GitHub, too.

I know there is no requirements for this course or specialization course, it is 0 to Scientist but seriously you are talking about R codes, arrays, loops, regression, model fit but signing up for GitHub.

Your target group in Coursera is either Data Scientist or becoming one, so they know what the Data Scientist job posts requires.

It requires coding blind folded R/Python/Java/one of C family at least 2 of them, hopefully all of them.

It requires SQL, MySQL, NoSQL, any kind of SQL or database solution mankind ever used.

It requires Math, Statistics, Analytics, Algebra, Finance, Economics + all kinds of computational sciences

It requires management, social relations, advertising, psychology, anthropology + rest of the social sciences.

+++++ it requires LOGIC and NON-ARTIFICIAL HUMAN INTELLIGENCE

so we are trying to be that guy, no need to show installing R or GitHub, I'm sure you will do it again doing rest of the Specialization.

SIMPLESMENTE SENSACIONAL !

The course is clear and good to learn,

A bit basic, but a great start for beginners.

Teaching of lessons are simply amazing.

I really don't know much about this stuff, I think the jury's still out on whether the last four weeks will be helpful in the future. We'll see how much I think I've learned at the end of the course

Too basic

I enjoyed this course

Amazing course. Most importantly it kills your fear for Data Science

A great introduction to data science and also to the R Studio environment. R Studio is going to be very used through this specialization, so it is very useful to be familiar with it.

Nice and helpful to start with R.

Overall an interesting program is offered. Just started, an update is expected towards the end of the course. So far found an issue w/ quiz #1 (incorrect grading due a broken internal logic (?) where 2 different 'correct' answers are offered during subsequent quiz sessions). Also, I would say that the intro videos are too short to be useful. Anticipated scope is well aligned w/ modern trends that are re-branded from the underlying concepts known for a long time; such concepts were always being in the arsenal of any serious practicing engineer or scientist. Modern packages though are a nice compact up-to-date tools collection.

Great background information on best-practice R usage.