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The Data Scientist’s Toolbox に戻る

ジョンズ・ホプキンズ大学(Johns Hopkins University) による The Data Scientist’s Toolbox の受講者のレビューおよびフィードバック



In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio....
Foundational tools
Introductory course



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.


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.


The Data Scientist’s Toolbox: 4726 - 4750 / 6,860 レビュー

by Figo C


Great learning on the basics of Data Science and it's importance in real-world applications. Help to get started with introduction to Python, R Language, Git!

Lectures could perhaps be more engaging and have more visual appeals (instead of having just lots of words on most slides)

by Angella S


Great course. I know it's impossible to show examples of everything, but sometimes having to go to Google to search for how to do something can be frustrating. Would be good if instructor can go over the different file types that can be created with Markdown, Rstudio and GitHub.

by Guilherme B D J


This course is good to get all your programs set up before you start your studies in Data Science.

I think it could offer a little bit deeper knowledge of git and github in order to guarantee it will not be a problem later, since they will not be strictly related to data science.

by Eugenia G


The course content is very useful, but explanations are short and It's unclear how to install R studio for the Windows (I found it at Youtube). Also I had a problem how to install the R packages, and solution was simple: you should run it as administrator (it wasn't in lecture).

by bt19103033 R J


This course is the beginner course , in which you will learn about the basics and get to know the tools you need to develop your career in DATA SCIENCE field. This was the optimal course to get you familiar with what basically is data science. You only need a question ..... :)

by Ximena R


I felt like I was able to keep up with the course material fairly well. My only critique would be when it comes to using git, the commands aren't very intuitive to me. Maybe explaining the commands a bit more would be more helpful, i.e. what the commands are telling git to do.

by Rahul P


Very nice introduction! Unlike a lot of online courses, this course is no fluff or jargon. It is solid stuff with hands on experience. I only wished this course was longer. After completing the 10-week Machine Learning course by Andrew Ng, this course felt a bit too short. :-)

by Colin L


Very basic. A few tweaks are needed in the last quiz's questions - the one pertaining creation of a .md vs. a .rmd file, and how to make sure the "## " prefix is properly given. (There should be a space after, and graders need to look at the raw file, not the presented view.)

by Madhusudhan T


An interesting introduction to data science, Git and GitHub. Hope GitHub is explained in a little more detail. Quite a few people found a couple of problems with the final project. The community is great and there are people who will help. Looking forward to the next course!

by Tina L L


The course is great but there are some serious glitches happening in the Coursera platform that desperately need attention. I just went from showing that I did not pass the peer review section and in the next second was greeted by a big green Course Completion Certificate.

by anjali v


This course is a great introduction to what data science essentially is and all the necessary tools required to start your analysis. However, it would be great if the examples used in the videos were explained a bit more in context rather than being stated plainly.


by Zainul A


A little unclear about the process for using Git & Github. The common functions/code are thought, but I believe a demo or a video review for the last assignment should be shared. Other things in the course provide a good introductory insights to the world of Data Science.

by Tanmay B


It is a really nice course if you plan to complete all the 10 courses in the Data Science Specialization track. As a standalone, It is not that great a course as it basically introduces you to different things and you need to do other courses to actually learn something.

by William B B


This is an excellent basic course. The main problem I had was understanding the computer voice at times. There is also a quiz question or two that refer to commands in Studio that are not up to date, but only a couple that I found. All in all, it's an excellent course.

by Naveen K


Great intro to Data Science Specialization. Hoping to complete the other courses as well. Dispels my myth about Data Science is all geeky stuff. Looking forward to bust more myths.

This course is light, broad and introductory. 4 weeks is a sweet spot. Keeps you engaged.

by Apolline M


Not much to learn, I would have liked a more thorough introduction to data science's principles.

Yet, everything is really presented step by step to make sure that all participants install correctly all tools needed for the further classes included in the specialization.

by Sally L


I did not know how to use R or R studio neither did I know what they entailed. With this course, I am now more aware. Being a solution provider. It is definitely a course you should check out to be conversant with the data scientist's tools especially R that is popular.

by Tony D C


This course is perfect to get an introduction to R and RStudio and the Github. It's easy to follow and pretty fast to complete. Probably the best thing you take home from this is to have a nice setup for the following courses where you can use the tools presented here.

by MHE v A


Very good, basic level course. Only one minor issue when working on the markup section, make sure to install TeX before you start, or R will not be able to generate a pdf. Not a major thing, but something that did frustrate me for 15 minutes while I got it all set up.

by Bernardo M F d S


Although I understand that Data Science involves a lot of self-oriented research, more resources and recommendations for learning git basics would be appreciated. Perhaps some practical exercises before the final assignment would've ensured a better learning process.

by Rok B


It is a good start to data science, you don't need a background in programming. The course is aimed at 1) helping you set up R, RStudio,git and conect it to GitHub and understand it's basic functionality and 2) getting a basic understanding of what data science is.

by Vamshi K P


The course covered most of the necessary tools in the Data Science industry. The content is clear and very easy to retrace the steps. Git version control required a deeper explanation of undoing a commit, branching and merging. The rest of the content is flawless.



The track consists of 9 courses that each last about 4 weeks which are released in batches of 3 courses each month. This course introduces the very basics of R and R studio, Git and Github and a few otherthings that will be used in the data science specialization.

by Brandon T


A little daunting at first but the instruction is simple and the ability to search video transcripts for tidbits basically saves me the step of taking notes. Some of the navigation was difficult in the forum, but I ended up figuring it out and posting something.

by Marcio R


Very helpful and important introduction to the world of Data Science. I do feel an overall lack of more examples and/or small optional projects/exercises to help learn, though. Maybe something like a list of exercises could be given at the end of each Module?