The Data Scientist’s Toolbox

Johns Hopkins University via Coursera

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

17 hours 59 minutes
Sessions

On-Demand
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Korean, Thai, Indonesian, Kazakh, Hindi, Swedish, Greek, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish, Hungarian, Bengali, Pashto, Urdu, Azerbaijani, Farsi

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Overview

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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.

Syllabus

Data Science Fundamentals

In this module, we'll introduce and define data science and data itself. We'll also go over some of the resources that data scientists use to get help when they're stuck.

R and RStudio

In this module, we'll help you get up and running with both R and RStudio. Along the way, you'll learn some basics about both and why data scientists use them.

Version Control and GitHub

During this module, you'll learn about version control and why it's so important to data scientists. You'll also learn how to use Git and GitHub to manage version control in data science projects.

R Markdown, Scientific Thinking, and Big Data

During this final module, you'll learn to use R Markdown and get an introduction to three concepts that are incredibly important to every successful data scientist: asking good questions, experimental design, and big data.

Taught by

Jeff Leek

Reviews

3.3 rating, based on 166 Class Central reviews

4.6 rating at Coursera based on 33933 ratings

Start your review of The Data Scientist’s Toolbox

Rui Ma @ruima

This 4-week course introduces data analysis using R, together with other tools like Git, Github. But the course is so short to cover a deeper understanding of each tool. Actually, week 3 is about several concepts about data and week 4 is about the project of this course.
Brandt Pence

This is the first course in the Data Science specialization, offered by Johns Hopkins through Coursera. The official schedule lists the time commitment as 4 weeks of study with 1-4 hours/week of work. In reality, this course can be easily completed…

This is the first course in the Data Science specialization, offered by Johns Hopkins through Coursera. The official schedule lists the time commitment as 4 weeks of study with 1-4 hours/week of work. In reality, this course can be easily completed in a few hours by anyone with a reasonable background in computers. Most of the course is dedicated to installing programs like RStudio, which is less-than-helpful for most users. However, for those unfamiliar with Git and GitHub, the introductory lectures on those topics are reasonably valuable, although there are better introductions available elsewhere.

Overall, this course is probably not even worth the $29 currently charged, but it is a required part of the specialization and thus necessary for those planning to complete it. For others, it may be a better use of time to simply download Git, R, and RStudio and to set up a GitHub account, then to move directly to R Programming. For those on the specialization track, this course can be easily completed in one night, and there should be no problem starting R Programming concurrently with this course.

The final project asks you to submit a screenshot to prove you can install and open RStudio, and to do a few simple tasks in GitHub (create a repo, create and push a markdown file to that repo, and fork someone else’s repo). The assignment is peer-graded, which historically has been a cause for concern in many of the Data Science track courses, but the questions are yes/no and straightforward enough that they leave little to a fellow student’s (sometimes rather skewed) interpretation. For example, a forum post early in the course asked if it would be preferable to reserve perfect scores on the peer-based assignment for students who had “exceeded the requirements” for the final project. Fortunately, this idea was shot down by subsequent posters (and it’s not realistically possible for this assignment anyway), but it displays a mentality that may cause good students to lose points unfairly in later classes. Reviews from other former students available online suggest that this is an ongoing problem.

Overall, two stars, mostly for the Git and GitHub material to which I had not been previously exposed.
Life is Study

The Data Scientist’s Toolbox is essentially just an overview of the data science specialization track. It introduces the very basics of R and R studio, Git and Github and a few other things that will be used in the data science specialization track. It is basically a bunch of introductory and supplementary material that shouldn't be a standalone course. You can complete all the lecture videos in the entire course in about 2 hours. It's almost embarrassing that John Hopkins has a paid verified certificate option for this course and it is required to complete the data science specialization track.
Anonymous

The whole "course" is doable in about 2 hours. It's laughably easy − the main point is to get you to install RStudio. Granted, if you've never used Github it'll be a bit bewildering, but I think it's wildly excessive to have a Signature Track certificate with a $49 fee to basically prove you're capable of signing up to a website.
Adelyne Chan @adelyne

A very simple introduction to the tools used in data science, mainly to get students acquainted with using GitHub repositories and RStudio. I took this because it was recommended for the other courses in the specialisation, but if you are comfortable with using virtual tools it is probably possible to learn on the job with the subsequent courses. This course could feasibly be completed in a single sitting.

Although I intend to take all the courses in the specialisation for its content (thus did not pay for a Verified Certificate), I am aware that a number of other people who are taking this for the specialisation did feel that it was not worth the money they paid given the very little content covered.
Matteo Ferrara @matteo

By far the worst course ever taken. I understand they need to find a viable business model, but if I did not take courses on Coursera before it, I would have not bother to open Coursera again. It is even worse since their following course, introduction to R, has a very steep learning curve (let alone it is as well a very bad course compare with the rest of Coursera offer), they could have put all together in one course, one week for this and 7 for R, instead of 4 and 4.
Ricardo Vladimiro

This is a very simple introductory course of Coursera's Data Science Specialisation.

It gives a brief overview of the eight other courses in the specialisation, an overview of what data science is (in the instructors opinion) and an overall crash course on R, RStudio and Github.

The course is a blessing if you don't have any coding or computational statistics background since it is an appropriate introduction. If you have the background are taking the course for the specialisation sake, it is annoying but you can do it in (literally) 4 hours.
Anonymous

Okay...so this course is easy and can the whole project can be completed within a couple hours. Even so, this is a valuable and helpful course for those new to Github, R, and some statistics jargon. I agree with many other reviewers that this cours…

Okay...so this course is easy and can the whole project can be completed within a couple hours. Even so, this is a valuable and helpful course for those new to Github, R, and some statistics jargon.
I agree with many other reviewers that this course definitely isn't worth the $49 fee for the verification services. Even newcomers will quickly realize that this course is ridiculously easy and lacking in fruitful rewards of useful knowledge. I would really like to see this course's verified certificate be offered at a lower price, if not offered for free since the work isn't difficult.

Don't let the reviews of this course serve as the general impression of the rest of the Data Science courses. The later courses involve much more work and expose students to truly rewarding knowledge and skills such as basic statistics and programming.
Anonymous

Your reaction to this course will likely depend on your background. If you already know about integrated programming environments (RStudio), version control (git and github), and mark-up languages (markdown and knitr) you will find it insultingly simple. If you don't know what I'm talking about, you badly need to do this course before attempting the rest of the specialisation. As a once upon a time programmer who has not kept up, I found it fairly easy but also very useful.
Anonymous

This class should be both:
- optional.
- free.
Instead, it's required, and I wasted money paying for it.

Others have covered this class's material in their reviews. My two cents are, this content is insultingly simple if you have a decent amount of experience as a programmer. (I'm in the "danger zone" of their data scientist venn diagram, I guess). It is literally painful to sit through an "introduction to the command line" after many years working in Unix.

It's inappropriate to place course overviews behind a paywall. I would not have paid the (now-reduced) $29 for this course if it weren't a gateway to the later ones.
Anonymous

As of early 2016 there were ZERO code walkthroughs in the *INTRO* R programming course. I got a 98% on the R programming course (2nd in the sequence). I peeked in & didn't notice any change in the curricula. 1) I’m an experienced programmer & hav…

As of early 2016 there were ZERO code walkthroughs in the *INTRO* R programming course. I got a 98% on the R programming course (2nd in the sequence). I peeked in & didn't notice any change in the curricula.

1) I’m an experienced programmer & have taught adult & gradeschool learners where the debugger was our friend.

2) Instructor didn't provide cookbook examples that matched the assignments (and the supplied books / manuals were just references). With the assignments set before us, I found it a challenge to code to the assignment levels.

3) On the fora I saw lots of my cohorts were getting burned the poor pedagogy support. Some had coded before, while others admitted to taking it for the 2nd time. Most of them didn't even know that R Studio had an interactive symbolic debugger b/c the instructor couldn't be bothered to update his slides to reflect the fact.

4) I asked about this on the fora, made a suggestion about code walkthroughs & the assistants down-rated my comment (it's like a ding on your reputation there...).

5) Peer review is a farce. I got dinged on one coding assignment b/c my comments and VARIABLE NAMES were_too_long, b_gratuitously_typed && very_distracting_to_the_coder_with_ADHD.

In my view:

MOOC’s are not the venue for universities to exercise their weeder-outer course mentality, esp. since they’re offering non-credit curricula. Bleeding for the credits does not need to be part of a rubric & code walkthroughs are not cheating.

To see instructors just droning on & on in front of their powerpoint slides, without code walk-throughs — comes across as rigid pedagogy or lazy.

For $50/month?
1) Expect to be annoyed or just expect less;
2) Look for better elsewhere, your time is valuable, lame curricula are not;
3) Breeze through the courseware if the credential matters, but don't run up a big tab slogging through an insincere courseware offering.
Jasmine Mercier

This course provides a useful introduction to Git, GitHub, R, and RStudio, which are all very useful tools you'll need to complete the rest of the specialization as well as in real data science work. It also provides a philosophical overview of just what "data science" is all about, clarifying what kinds of work actual data scientists do.

Unfortunately, all the material can be covered in a single day. The class should really have been a part of the R Programming course - paying $50 for the signature track (if you plan on doing the capstone project for the specialization) seems a bit too much.
Anonymous

Very difficult if you don't have any prior programming and computer science knowledge. This is not a beginners course. Frustrating, challenging, assumes students have enough prior knowledge to fill in the gaps during the lectures. As someone with no prior computer experience of any kind (other than basic Word) I was lost almost immediately.
Mohamed Sameh

The course is very easy, and its material is very shallow, it should have been only a first-week introduction to another course in Coursera's Data specialization track.
Greg Chapman

First, I should note that the time spent per week is actually the total time for completion. This course is a very straightforward introduction to the data science track and the tools that are going to be used in the rest of the track. And that's it, there's really no meat to this course, but if you're not familiar with Git or command-line interfaces it could be really helpful. I suppose if you're paying for the specialization you just have to amortize the cost of this course over the other courses in the track.
Anonymous

If you have never worked with Git, GitHub, R, or RStudio, this is a great (very short) introduction.

I have seen a lot of people in later courses of the specialization complain that they did not know how to upload the peer projects onto github, so I would say it can be a very useful class.
Ryan Bowen

This course essentially is just a walkthrough of the different programs that you will be utilizing throughout the rest of the Data Science Specialization (if you are continuing with the other courses). The fact that they allow you to pay for this course is a joke because you are taught next to nothing except how to install programs. If you plan on finishing the entire Specialization, then perhaps paying for it will benefit you in the long run. Otherwise, the class is easy, does not take much time, and sets you up to continue with the other courses.
Krishna Magar

Great course if you are hearing the words GitHub, R and RStudio for the first time. It will provide you the basics(practically) of these terms and rest of the courses in the Specialization. For people like programmers, it's just a mode of entry(only with the payment) to the rest of the Specialization courses. So, it's painful that this 2 hrs course is expanded to a month plus a fee. But then its a good bargain for the ovearall courses in Specialization. Cheers !
Guest

Taking this course was very annoying due to it's lack of structure(specifically when the quizzes should be taken) & shallow content (mostly overviews). Also if you have an older computer installing to software needed to complete the class maybe a challenge. I may try it again when I need a new computer.
Greg Kent

Overall, the class is pretty simple, and far easier than the other classes in this specialization. Really, the hardest part of this course was finding the complete instructions for the final project. The first part was found easily enough, but the other parts of the project are not obvious.