The Data Scientist’s Toolbox

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6/10 stars
based on  29 reviews
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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4723 reviews

Course Description

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.
The Data Scientist’s Toolbox course image
Reviews 6/10 stars
29 Reviews for The Data Scientist’s Toolbox

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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Greg Hamel profile image
Greg Hamel profile image
3/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 7 months ago
The Data Scientist’s Toolbox is essentially just an overview of the data science specialization track offered by John Hopkins University through Coursera. 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 other things that will be used in the data science specialization. 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; what's worse, it is required to complete their data science specialization track. I suspect this will be a major turnoff for students interested in the track.
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Student

10/10 stars
3 years, 2 months ago
Content and instructors are excellent, and I am grateful that this course and the entire JHU data science series is available on Coursera. However, I am disappointed that I must pay for access to graded content since it is either automated or done by peer review. The mismatch between what universities have provided and what industry says they need and can't find is apparent here. Not sure why I or any other citizen should pay for that after investing significantly in college and graduate education. Perhaps industry and academe should be sharing the cost of correcting this problem?
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10/10 starsCompleted
  • 9 reviews
  • 9 completed
3 years, 5 months ago
This course is skippable if you are familiar with basics of data science and GitHub. Nonetheless, you can complete the entire course in a matter of hours if you are not familiar with it. I think it's a good way to see what you're getting yourself into and is an extremely soft introduction to data science. I do recommend that you go through this course, but again, it is not quite that necessary.
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Nirav Desai profile image
Nirav Desai profile image
10/10 starsCompleted
  • 9 reviews
  • 9 completed
3 years, 7 months ago
This is the first course in the Data Science Specialization series by Johns Hopkins University taught on Coursera. It introduces all the tools necessary for subsequent courses on data science and gives a driving motivation for the specialization.
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8/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 7 months ago
Although there seems to be alot of people annoyed by the "absolute beginner" nature of the course, as an absolute beginner I can tell you that it is incredibly illustrative. I didn't know what Git or GitHub was, I didn't know how to use the Windows cmd API or how to use a programming language. All of these things that many people take for granted and already know are NOT trivial. Coming from an economics student that had no prior programming knowledge I can tell you: this course is rewarding.
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Damien Theophano profile image
Damien Theophano profile image
4/10 starsTaking Now
  • 1 review
  • 0 completed
3 years, 10 months ago
Overall I found this course very underwhelming. Although there is some good content, it is poorly presented and taught. It is dumped out without clear explanations. The lecturer is not a good communicator. Nearly every sentence begins with "so" or "and so". It quickly became extremely bothersome to try to listen to. The sections about Git and command lines are some of the worst examples. I found much better explanations and how to's on the web. It made me wonder why take the course if there is much better resources to be found for free and on one's own. I hope the rest of the specialization is better because I am very disappointed so far and not motivated to continue
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Hang Yuan profile image
Hang Yuan profile image
6/10 starsCompleted
  • 1 review
  • 1 completed
3 years, 11 months ago
Even though personally speaking, I didn't gain much from taking this course, but for the people who have never used git or RStudio before, they might benefit more from this course. In order to do the capstone project, you will need to pay for this course to get the certificate, and for the price you are paying for this course, I don't think it has a good investment/return ratio. But no matter what the materials covered, need to mastered by anyone who is serious about doing data science.
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Student

6/10 starsCompleted
4 years, 2 months ago
This is the first course required for the data scientist specialization. The goal is to get an overview of what is to follow, and to set up software and online accounts that will be needed for the rest of the courses in the specialization. This course will quickly weed out those who aren't technologically savvy, and will bore the pants off of those who are. I think this course is overpriced.
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Michael Farsan profile image
Michael Farsan profile image
8/10 starsCompleted
  • 7 reviews
  • 6 completed
4 years, 3 months ago
Entire coursework can be completed within 4-5 hours. This course is designed to set up a user account on github and set up a programming environment for R (R studio). This course only sets up the environment for the following courses of this specialization and gives you an overview of the specialization.
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Shankar C profile image
Shankar C profile image
4/10 starsCompleted
  • 2 reviews
  • 2 completed
4 years, 3 months ago
It should not be designed as a separate course. Should be included along with R Programming. The course content is very short - an introduction to git , github and installing R , R studio which you can learn by seeing some documentation and that wont take time.
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Student

10/10 starsCompleted
4 years, 3 months ago
This course is a great kick-off for the Data Science specialisation and getting to know the available tools that will safe lots of time for many upcoming data scientists.
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Student

2/10 starsCompleted
4 years, 4 months ago
My first Coursera course and I'm not impressed compared with courses I've taken on edX. The sound volume of the lectures is too low. Content is really thin for 4 weeks. Worst of all, I had a bug (couldn't see a screenshot) while doing the required evaluation of another student's work and no help...the flag button led to an oops page and then lands on some general FAQ. All for $29? Moving on the 15.071x The Analytics Edge instead of continuing here.
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Michael Devereux profile image
Michael Devereux profile image
6/10 starsCompleted
  • 5 reviews
  • 4 completed
4 years, 6 months ago
So this was the second course I did after having completed Andrew Ng's Machine Learning course and wanted to go down a bit more into the rabbit hole....this is the first course of the "Data Science" path for John Hopkins and I would think of it as just a very gentle introduction to Github - which allows people to work on a project, collaborate with others and have a very precise version timeline on the project, which frankly people doing shared excel or powerpoints could learn a lot from. The course is quite easy but don't stop there - this is just the tip!
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Priscila Guimarães profile image
Priscila Guimarães profile image
7/10 starsCompleted
  • 11 reviews
  • 10 completed
4 years, 10 months ago
I liked the course and learnt much. I understand that some people thinks that this course was unnecessary but I think it depends on your level of knowledge. When I took it I was finishing my first year in University and it was my first contact with GitHub. I do believe that what probably was responsable for this bad impression was week 1. They would only speak about the other courses offered in this specialization, which is not really fair, I mean, they should provide this information for free, and not inside one specific course. To end up my review, I think it's necessary to point that this course is not useless, however it should not be obligatory in the specialization because i don't think a graduate could take much from it.
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6/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 7 months ago
Not everyone who enrolls in Data Science courses is an experienced data person. Many are trying this for the first time, and quite a few are people who are a little uneasy with English. The git and gitHub applications are command-line oriented, as is R; RStudio is not exactly intuitive either. This course needs to be required for most of the 10 to 20 thousand newbies who enroll for the first time. If you are confident, take the R programming course at the same time. Learning computer applications and languages is largely a matter of banging one's head until it works. I was a programmer for about 35 years and cried over new software frequently. I started on IBM and Univac mainframes and was there for the great PC revolution, including Linux. So I am going to do CTA for this course in April. I hope to help newbies become familiar with the material, and to give them confidence to proceed.
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Jerry Jacobs profile image
Jerry Jacobs profile image
2/10 starsCompleted
  • 1 review
  • 1 completed
4 years, 9 months ago
This really shouldn't be a course. All you do is review the different tools that are out there for doing data science work, while not actually learning any thing about data science. Really easy class if you are just looking to put a nice buzz word on your linkedin profile. This is just an intro for the rest of the classes they offer on their data scientist track.
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Jackie Werner profile image
Jackie Werner profile image
1/10 starsCompleted
  • 3 reviews
  • 3 completed
4 years, 12 months ago
There is absolutely no point to taking this course unless you're going for the Data Scientist Specialization - the only content is installing R and R Studio.
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Rohit Kshirsagar profile image
Rohit Kshirsagar profile image
6/10 starsTaking Now
  • 1 review
  • 0 completed
5 years ago
To be filled in later
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Anna Desiatnik profile image
Anna Desiatnik profile image
7/10 starsTaking Now
  • 1 review
  • 0 completed
5 years, 3 months ago
I expected to learn some basic data science tools, I think I would like to have it deeper discussed and presented.
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Jeff Winchell profile image
Jeff Winchell profile image
4/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 9 months ago
This was perhaps the easiest MOOC I've ever taken and I learned almost nothing from it. But since it was the prerequisite of all the other 8 MOOCs in Johns Hopkins Data Scientist series, I felt obliged to take it.
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Hamideh Iraj profile image
Hamideh Iraj profile image
6/10 starsCompleted
  • 70 reviews
  • 60 completed
5 years, 7 months ago
I generally agree with Greg. It is a very light weight course : an absolute beginner guide to R .Having passed "computing for data analysis" and "data analysis" on coursera , I already knew almost all the material and completed all in less than two or three hours. I think the mission of this course is to assimilate students who want to take other courses such as R Programming. Before the data science specialization announcement, computing for data analysis (Now R Programming) was the course for beginners and many were complaining that it was not easy. I think this course plus Getting and Cleaning Data acts as a warm-up to make R Programming easier. To wrap up, Although being easy I consider the course necessary for the entire specialization program. But it does not worth 50 dollars.
Was this review helpful? Yes8
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Mbwana Samatta profile image
Mbwana Samatta profile image
2/10 starsCompleted
  • 5 reviews
  • 5 completed
5 years, 5 months ago
As much as the MOOC revolution has redefined the way courses can be structured, this one is but a shameless dash for cash. With its 2 weeks tops of lectures (first week does not count being essentially an advert for the "Specialization") it can hardly be called a course. I do understand it may be intimidating to "install a programming language" for the first time, or to use github, but it can and should be covered in a short text instruction with maximum two accompanying videos. Just as it was in "Molecular evolution course". These materials could have easily been merged into "R programming" course. Glad I haven't paid for it.
Was this review helpful? Yes4
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Duncan Murray profile image
Duncan Murray profile image
4/10 starsCompleted
  • 25 reviews
  • 24 completed
5 years, 6 months ago
Seeing people in the Data Science stream will have already done R programming (which covers the installation of R - Studio), then the bulk of this course is a couple of lectures with the final "Assignment" writing a one line MD file, then clicking "Fork" on github. That is a terrible excuse to charge people for this course, when the content should be part of another sections introduction.
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Pascal Labbe profile image
Pascal Labbe profile image
1/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 6 months ago
Ridiculous! The only reason to take the course is that it is mandatory to get the data science track certificate. It is a how-to install git, github, R and RStudio. That's all. It is scandalous to pay for that. However the R language course that follow in the track is not so bad.
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T. A. profile image
T. A. profile image
4/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 5 months ago
The other reviewers' criticisms are valid. It's short, easy, and you can argue its volume of content doesn't amount to a whole course. EXCEPT... I had known about git and github for years and had never used either. Same thing with markdown. The benefit of this course to me was forcing me to use these things, which I didn't appreciate until I had tried them. I found this course much more useful in retrospect, now that I have moved on to subsequent Data Science courses where some of these tools make the course much easier to navigate. As for the minimal volume of time, I suppose for an absolute beginner to R it is a baby step toward digging in to the later courses. I might suggest to the JH profs they include this as a free course and not require it as part of the specialization.
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Marcelo Soares profile image
Marcelo Soares profile image
4/10 starsCompleted
  • 16 reviews
  • 13 completed
5 years, 6 months ago
It's an introductory course, not challenging at all. I completed it in one week - unsurprising, for it comprises little more than the first week of "Data Analysis", the course Leek gave before.
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Tamás Nagy profile image
Tamás Nagy profile image
6/10 starsCompleted
  • 7 reviews
  • 7 completed
5 years, 4 months ago
Although an important course, it had some really weak points. For example IMHO it was completely unnecessary to teach Git Bash (which is for weirdos), if the GIT Hub works just fine for the average users. The lecturer sounded quite bored too.
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Erin K profile image
Erin K profile image
2/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years, 5 months ago
This course did not meet my expectations. The only place for learning was the video lectures, and these were not well done. I left the lectures not having the slightest idea of what I was supposed to learn from them. Additionally, the lectures had links in them *which were not clickable*, so if the instructor used text to define what the link was, that text masked the address and you couldn't get to it. This also meant manually typing in all other links. Guided examples would have also been useful at times, as well as use of the discussion forums. The first week of video lectures even included long introductions to the rest of the courses in John Hopkins' Data Science Coursera Specialization track, which I skipped after realizing they were just advertisements. They would've done a better job making me want to take their other courses if more effort had been put into this one. The first lecture was a very cursory introduction to t... This course did not meet my expectations. The only place for learning was the video lectures, and these were not well done. I left the lectures not having the slightest idea of what I was supposed to learn from them. Additionally, the lectures had links in them *which were not clickable*, so if the instructor used text to define what the link was, that text masked the address and you couldn't get to it. This also meant manually typing in all other links. Guided examples would have also been useful at times, as well as use of the discussion forums. The first week of video lectures even included long introductions to the rest of the courses in John Hopkins' Data Science Coursera Specialization track, which I skipped after realizing they were just advertisements. They would've done a better job making me want to take their other courses if more effort had been put into this one. The first lecture was a very cursory introduction to the utter basics of computer programming, so while this was a tad unnecessary for me, I could see it being useful for other, less experienced people. From this course I learned how to use GitHub and make markdown files, but the course should NOT have been four weeks long, as most of what was taught could've been adequately covered in probably one week. I also could've learned this stuff by just reading the github manual/intro on their website, which is quite good, and probably actually better than this course's introduction to it. This course only taught command line interaction with github, when there exist many programs with user interfaces that simplify this interaction and make it more intuitive. If they wish to improve the course (which is questionable as they asked for no feedback whatsoever), they might explore how to use both the command line and these interfaces, introducing multiple interface options and discussing their benefits. It would've been nice had this course discussed why github is the frequent choice for subversion repositories and also what the other options are and how they differ. Also, this course frequently only addressed how to do things on one operating system, which can be annoying for those not dealing with that system. So while this course did teach me how to use github, it took way too long to do that, did not teach nearly enough in that time, skipped some pretty important areas, and taught what it did include pretty poorly.
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Richard Taylor profile image
Richard Taylor profile image
1/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years, 5 months ago
The whole course can be completed in about 15 minutes. It's quite puzzling why they decided to start a specialization on such a bad way. Not really a course but just a way to ask you to install a couple of programs.
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