學生對 约翰霍普金斯大学 提供的 数据科学家的工具箱（中文版） 的評價和反饋

Good to set you up for advance courses.

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.

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

Great intro

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.

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

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.

!!!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.

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

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.

SIMPLESMENTE SENSACIONAL !

This is a very good course as it tells you some basic and is mostly the introductive course for the entire specialization.

it was simply the best

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

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.

Good but need more explanations for topics.

Would be great to have more reading materials

Teaching of lessons are simply amazing.

The course is clear and good to learn,

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

A bit basic, but a great start for beginners.

The course contents are thorough and clear, but the UI and the platform of the web course could use some thought. I find it a bit tricky to take notes and copy url's and command lines from the video. Perhaps this is just a matter of getting use to the platform. Excited to proceed to the next course!

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.

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.