!!!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.

This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.

This should not be a course. It's just introduction and should be condensed to one video and a handout with pointers to those who need more specific and detailed instructions for setting up their computers. This is not a learning course. All they teach you how to install git, R and Rstudio. And they take money for this (if you want to say you "completed" the course)!!! Highly unethical practice in my opinion.

The surveys are stupid too. They don't distinguish between those who completed (but not earned any credentials) and those who didn't.

In my opinion this is a very dumbed down version of an introduction to a data science course (and not a cours in itself). Definitely not worth 4 weeks!

A much more valuable course is Stanford's "Machine Learning" where you can actually learn something (and don't force you out of evaluation if you don't pay).

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.

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.

Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.

It was really insightful, coming from knowing almost nothing about statistics or experimental design, it was easy to understand while not feeling shallow. Just the right amount of information density.

Good to set you up for advance courses.

I'm a web developer who is interested in a career in the Data field. That means I know version control and I have more than basic computer knowledge.

I read 1 star reviews' and found people saying lots of things, for example:

1) "Having a course that teaches you how to install programs is very basic and lazy"

The Data Science field sounds challenging to me. Even though I know how to install programs, this easy approach makes the course seems achievable for me. I'm thankful for this, because I guarantee this course will suit people with different academic backgrounds and since it's not really complicated or difficult, it is in fact a very welcoming introduction for a field that might scare people.

Even though I thought this course was going to dig deeper into Data Science topics, the title of the course states it clearly: Data Scientist's Tool Box.

2) "The robotic voice is so boring and distracting I couldn't finish the course"

It wasn't for me. In fact, it was better. I found it less distracting than watching a human being talking.

Also, I think the way they explained why they make the course videos this way is genius. It makes you want to learn Data Science, which I think is one of the most important things a teacher should do: get you excited about the subject.

This course is intended to be completed in 4 week, I finished it in a week.

I do think it's a good starting point and I didn't find it "too basic" at all.

Give it a try if you've never been a tech dude or dudette.

As a business student from Bangladesh who is aspiring to be a data analyst in near future, I love this course very much. The quizzes and assessments were the places to check how much I exactly learnt.

Good overview of the ideas/concepts in data science and the set of courses coming up, but mostly seems to be a place for people to work out any issues getting Git, R, and RStudio set up before they head into the R programming intro. Very light on useful content outside of that. Definitely not 4 weeks worth of course material - can do the whole thing in a couple hours or less.

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

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

More clarity on creation of .md file should be included in lectures

Great Introduction to Data Science and R Programming

This was a great introduction to Data Science and R Programming. I learned about what data science is; how to install R, R Studio, and GitHub; how to install and use R packages; proper forum etiquette; how to create an R script, save it, stage it, commit it, and push it; how to link an existing project with GitHub; and R Markdown. I am starting to appreciate the power of R and cannot wait to dig deeper! I learned so much!

One recommendation I have for the course is having more variety in the quiz questions available. I noticed that the questions from the weekly quizzes were just repeats of questions from the practice quizzes.

Doesn't qualify as a course really, it's a fair introduction that's really helpful when it comes to not being overwhelmed with installing programs & setting your self up for the material coming up next.

It's an easy 100, & you should be able to finish the whole thing in a week or 2 max. Don't linger too much on it, & move forward with the specialization as things get more interesting in "Programming in R".

Non the less, great first step - just don't linger too much on it.

Great intro

I missed the entire course deadline just because I stuck in one of week 4 projects - pushing a file into GitHub. When I went to the forum, there are so many people in the same situation. The main reason is that the week 4 video did not cover the project content well. Overall, compared to other programming courses I took on Coursera, this course is not well organized at all! Instructors basically just read words in slides, no interesting examples and no hands-on practices, especially week 4 slides that look like a syntax reference document. So the entire instruction is very boring, making me wonder why I need to sit here and listen to word reading rather than buying a textbook. Also, people coming here to learn it are new learners. When the instruction skipped much and made the content not logical and complete, it is very difficult for us to understand what the video is talking about. That's why in forum there are so many people expressing their confusions. This entire specialization is the one that I've taken and feel the worst (I got lost in the R course too). I strongly recommend the instructors of this course watch and learn how the instructor of the specialization "Python for Everyone" organizes and teaches the content. He makes programming become very interesting and easy to understand. He also provides many hand-on practices to help learners understand abstract concepts. Unfortunately this specialization instruction is just about reading words in the boring slides.....

I did not like this course for many reasons, the primary one being the automated videos. By switching to this type of video, the course misses the mark on the whole point of taking courses versus reading a book -- one of the big parts of learning is the interaction/relationship you form with the instructor and their experiences and expertise. These types of videos feel very cold and detached and are psychologically not effective or engaging. Additionally, there are many mistakes in the section quizzes, and it didn't really feel like the things taught in this course were worthy of an entire course -- they resembled more of a brief intro to the subject and a bunch of reference appendices.

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.