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Learner Reviews & Feedback for The Data Scientist’s Toolbox by Johns Hopkins University

4.6
stars
33,829 ratings

About the Course

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....
Highlights
Foundational tools

(243 Reviews)

Introductory course

(1056 Reviews)

Top reviews

LR

Sep 7, 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.

SF

Apr 14, 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.

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6351 - 6375 of 7,122 Reviews for The Data Scientist’s Toolbox

By Ruben K

Nov 28, 2018

cool

By shiva s

Sep 1, 2018

nice

By Anup K M

Aug 22, 2018

good

By Alessandro D B T

Apr 29, 2018

Good

By Tiange X

Jul 13, 2017

good

By dragie

Apr 5, 2017

good

By Rose S

Nov 23, 2016

Good

By Chinmoy D

Nov 5, 2016

Good

By Andika

Apr 30, 2016

Good

By Harsha G

Mar 3, 2016

good

By liyp

Dec 8, 2017

完成了

By MINHAJUL I

Jul 19, 2021

..

By Abdul J B A H

Oct 15, 2020

ok

By Cabes M

Dec 31, 2016

df

By Aditi D

Nov 15, 2022

-

By Myriam G

May 25, 2018

-

By Matthias M

May 20, 2018

V

By Grant S

Apr 14, 2017

J

By Andrew D H W

Feb 15, 2017

G

By Mununur M

Sep 13, 2016

I

By Sudheer K

Jan 29, 2016

G

By Nick B

May 5, 2019

Hi guys. I'm not sure that you are reading the feedback, but instead of saying that it's good or bad I'm going to come up with suggestion. I'm data pipeline architect with 20+ years of experience who decided to take these courses to understand a gap of knowledge that current data scientists have. I think it comes from the very course. The R is kind of out dated for current world of big data, but I think you've already heard about this. Some Data Scientists who show up in our company they are good with theory but very bad in implementation. They don't understand the Big Data, especially distributed data. R is good language to the lessons but it doesn't have any connection with real world. You better include some basic knowledge about Spark (especially Spark ML), distributed computation and finish with R. Most of data science algorithms and libraries implemented (and used by real world) are in Python. Contact with some Cloud Providers like AWS and Google to create accounts for education. You course would be more attractive and, what is most important, would be more useful for people and companies where they start to work.

If you want, you can contact with me about some volontier consultancy. My email is nick.orka@gmail.com

By Matthew W

Aug 10, 2018

Generally good information, but the static powerpoint videos are a bit too vague to be useful. I ran into issues several times when the steps described in the video (sometimes providing incomplete terminal or git commands) didn't coincide with the steps described in site specific tutorial videos recommended in the course forum. So I ended up spending a lot of time 1) figuring out the full commands required, and 2) reconciling conflicting sets of instructions after receiving error messages. This mainly occurred when trying to get my local git work to correspond to GitHub. I suggest more actual demos in the video lessons (i.e., actually type in full commands, show the result, and explain how to interpret those results), or 2) explain overarching concepts and then simply list a set of existing online tutorials that should be followed.

By Tanvi M

May 25, 2019

This course lacks the inter-activeness that holds up a class. Even the material was not worth the money as it just teaches you to install certain programs and exactly what one can do with it. I feel there is enormous scope to improve this course in particular.

Things I will suggest:

1. The installation process is not shown with a depth. I feel increasing video size wont matter as reducing and removing certain important points that students should learn. I hope a better depiction and graphical representation of such an amazing subject can be done.

2. The problem with coding is that though they told how to make certain thing bold or Italics not actually it was shown as to where to put this.

I hope that everyone gain interest in such a subject.

By Louie M

Mar 11, 2018

I noticed that w/in the course video's there were numerous cases of misspelled words and even some incorrect information. Regardless, it didn't prevent me from learning the material, however I would expect more precision from Johns Hopkins. Additionally, the narrator (at times) seemed as if he was getting exhausted/running out of fuel towards the end of each lesson. Some of the instruction isn't exactly clear, i.e. the instructions for installing R, RStudio & Git. Perhaps you all are attempting to make the student engage in some heuristic thinking? When it comes to a class like this, precise and clear instructions are a necessity, especially to novices. Regardless, I look forward to continuing to learn. :-)