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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



Apr 15, 2020

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.


Sep 08, 2017

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: 3776 - 3800 / 5,467 レビュー

by chad

Sep 08, 2018

I was already familiar with the stuff taught for the most part. I think they should do more tutorials on how to install and do things though for those not experiecned enough in it.

by James C

Aug 12, 2016

Good introductory course pointing to background reading materials and providing context for following courses as well as supporting the installation of the required software tools.

by John E M

Jul 09, 2018

Seemed simple, but had to figure out some things that weren't explicit in the videos for the exams. Helped in my learning, but frustrating for a very pure beginner in programming.

by Pinko W

Sep 08, 2017

Professors could extend the videos by more examples and illustration for deeper understandings of the students. The tests could cover more items and deeper contents of the course.

by Subhankar J

Jul 24, 2017

A little more could've been explained about Github, overall the course was highly comprehensive with regular quizzes and assignments helping me to identify my mistakes and on them

by Jonathan R

Jan 31, 2018

I would like this course to be longer. To have more practices and to have at least one lab with data for running some simple functions. Other than that I think is a good course.

by Gavin D

Apr 28, 2016

Practical to do if you are continuing with specialization. Ensures all students have the correct setup, which is important. I'm not sure how useful it would be on its own though.

by John C Z J

Feb 29, 2016

Gives a great foundational overview of what Data Science is, as well as getting your technical environment set up for the future classes in the Data Science Specialization path.

by Parimi R S S

Aug 30, 2019

I have expected that this course would have given me the basics needed for my data science specialization I strongly recommend a new optional course which gives all the basics.

by Ahmed S

Dec 30, 2017

Very good introduction to Data Science and I found it highly useful and insightful.

The main issue I faced is that problem statements are not always clear for programming tasks

by Juan M F M

Jun 01, 2017

El curso me gustó, considero que fue una buena aproximación hacia el mundo de los datos y sus herramientas. Espero seguir aprendiendo en el transcurso de los siguientes cursos.

by Ruben D W

Mar 09, 2016

Good introductory course, but could offer a bit more of a challenge, with actual (basic) problems. Learned about R, although not programming, and more about GitBash and GitHub.

by LI D

Dec 29, 2017

It's fairly good! Concise intro to DS. However, you will need to spend more time in CLI and GitHub by looking for information in the forum or googling step by step procedures.

by Rachel K

Dec 28, 2017

I was able to set all the programs up that I will need to program in R, but I still feel like I don't really know the basic overview of what I will be doing in this specialty.

by Philip W

Oct 18, 2016

Useful introduction to the world. Could have been a bit longer and gone into more detail about a variety of other factors is my only comment, but overall pretty happy with it.

by ramanathan l

Apr 07, 2016

This is a fantastic course, being a person who already works with loads of data I can surely stand by the statement that "This is a fantastic base line to all your data needs"

by Vishal M

May 22, 2018

Could provide live demo of creating markdown file & pushing into the remote repository.

Overall the learning was awesome. Was a good guide to go forward to subsequent courses.

by Greg S

Nov 15, 2017

Straightforward (although I am already familiar with markdown and git, so that helps). Lots of good groundwork here though, setting the scene for the next round of material

by Shaik M U

Feb 20, 2017

Coursera has changed the way of learning,Yes anyone can learn anything and everything at the comfort of their own time and place......

Thanks Team COURSERA....Keep Going....

by Brandon B

May 22, 2016

It's a good introduction to data science. However, without prior knowledge of statistics, programming, and research methods the course would be fairly difficult to follow.

by Willie C

Jan 22, 2020

This course is fairly rudimentary, but it does a really nice job of getting you all set up with the tools you'll need to tackle the rest of the Data Science specialization

by Hannah B

Jun 04, 2019

The part where GitHub, Git and R are connected was very interesting. However, as a fairly experienced R user and scientist, I wish I could have skipped some of the parts.

by Luis E R R

Apr 30, 2020

I found this course to be quite useful, albeit introductory. I kind of was hoping for more material. Nevertheless, for a beginner, I believe it is completely appropiate.

by Neha S

Oct 10, 2016

Good start for the beginners especially for people new to the data science. Topic are not covered in depth but as its just a introductory course so its totally fine..

by Alex B

Aug 26, 2019

Well explained, step by step instructions on how to install and use the tools

Potential to improve: use more visuals in de instructions (many times now it is only text)