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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: 5101 - 5125 / 6,789 レビュー

by Cathryn S


Fine as a general introduction, but not a lot to it. Good explanations of how to use github and get set up.

by Abhinay R


A very good intro course but must be priced lesser when compared to the other courses in the specialization.

by Seung J L


Some of the material could be updated even more, but in general a good intro to a data scientist's tookbox.

by mohamed y


Great introductory to the data science tool box and its usage and benefits __ data science 0 course really

by Miryam A M C


Algunas partes del curso deberían tener mas ejercicios para practicar lo visto y reforzar mas lo aprendido

by Dushyant B


A good course to set up your software tool kit before you start your dive into Data Science Specialization

by Ihab A


This course is perfect for really newbie. However, it also answers the question of "What is Data Science?"

by harish s


Good and clear explanation. if more assignments were given for practice, to would have been more helpful.

by Meagan V


Easy to follow for the most part. A couple of things were outdated so I had to figure them out on my own.

by Chen S


the course omits some important things like how to download MikTex and how to use it to open an rmd file

by Anku N M


Course was good, but would have been nice to have some TA/professor oversight of the discussion forums...

by Adam S


So far so good, Git was a bit difficult to get going but I'm excited to continue with the specialization.

by Michael M


A good course for identifying the field of data science and introducing the computing tools of the trade.

by Bernard O


A simple introduction to R and data science. You have given me the appetite to know more and learn more.

by Guillermo C P


Sometimes beginners MAC's users get a bit lost due to many explanations are only for Windows environment

by Thomas Z Y


Straightforward, and I know what to expect for the next few chapters with John H University. Thank you!!

by Filipe M


Good introduction to data science, however it should have more details and practical examples about Git.

by Eileen S


Super basic introduction. I liked the last section reviewing data science methods and conceptual ideas.

by Donghee L


I think more detail statements are needed on PDF.

Because I'm not good at listening, so I studied by PDF.

by Shriya I


Extremely informative & interesting, Instructions can be followed with ease. Worth the time and effort.

by John E F


Excelente curso, porem diferentemente da descrição, existe legenda em português, apenas para um modulo.

by Satya P


Structure, content and step by step practice was good. Interface to browse the courses could be better.

by Jay G


Good start, looking to dig in more, as I already have a background in stats data analysis and data viz.

by Vicent G


Quite simple. It could be more engaging.

When downloading PDF slides, links in them should be clickable.

by Cristiano L


Good and objective course. Gives the basics about data science and how to get started with R software.