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Learner Reviews & Feedback for A Crash Course in Data Science by Johns Hopkins University

4.5
stars
6,732 ratings
1,278 reviews

About the Course

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT...
Highlights
Basic course
(76 Reviews)
Well taught
(48 Reviews)

Top reviews

MD

Aug 28, 2016

Is really hard to summarize the potential of Data Science and being clear, but I think that the instructors have done their best, so that we can achieve the most from the Course.\n\nGreat Job!

SJ

Sep 10, 2017

This is a great starter course for data science. My learning assessment is usually how well I can teach it to someone else. I know I have a better understanding now, than I did when I started.

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1126 - 1150 of 1,244 Reviews for A Crash Course in Data Science

By Francisco P S

Mar 29, 2017

The course can use more visuals instead of videos of the face of the instructor. It can also use more interactive examples as this is a more executive view instead of having scholar examples.

By Enrique G

Jul 29, 2020

Was actually expecting more.

Some of the lectures seemed just to theoretical and distant.

Several of the linked resources do not exist anymore or are accessible only with paid subscriptions.

By Peter P

Mar 19, 2016

Great course for somebody who does not know anything about data science. When doing this specialisation there should be credit for those that did the other data science specialisation

By Scott K

Oct 11, 2015

Very basic course. If you know about data science, data analysis, or machine learning, you may find this class basic or boring. Good intro course for those who have no prior knowledge

By Sam B

Jun 18, 2017

Interesting course but structure was a bit odd I thought, it was not clear why so much time was devoted at the outset to the difference between Machine Learning and data science.

By Yi P C

Aug 19, 2017

not bad but not good enough for showing examples like data visualization and how to build the mind of data science for several fields (finance, marketing, sales and so on)

By Rajkumar S S

Jul 28, 2020

I felt that the content of this course was too little. It should go one level deeper and explain the challenges that managers may face when handling data science projects.

By Leslie T

Mar 05, 2019

The material and lectures are good but the quizes are not very helpful and somewhat random (in answers). The small number of questions make them very unforgiving.

By Raymond T W

Oct 11, 2018

A bit too lengthy for the points to be learnt. Can get more done in less time and fuss. Too many examples especially if one wishes to cover 100% of the material.

By Marco C

Nov 16, 2015

Good course, with general and not over-detailed explanations of all the relevant topics in data Science. A good, general overview definitively worth working on.

By Jason M

Jul 13, 2018

Pretty high level and quick. Hoping the remaining courses in the specialization give more depth. (Completed this course in an afternoon.)

By Yuhua N

Mar 09, 2016

Lectures were fairly straightforward but not really that exciting and the lecturers sometimes felt a little unprepared or underwhelming.

By Jochen H

Jan 10, 2020

Interesting course and good learning. However for me some of the video lectures were a little bit unstructured and difficult to follow.

By Yousuf A

Aug 09, 2018

A lot of the topic is described in a difficult way using unknown words(for a beginner) and with examples that I did not understand.

By Dave W

Jul 03, 2017

Pretty basic course. Good if you are completely new to the space. There are good references to tools for further investigation.

By Stephanie M D

Dec 23, 2016

Nice overview for those of us unschooled in the language. Syntax of the notes and text is in need of major editing-proofreading.

By AKASH S

Jun 06, 2020

the course must be more explanatory and the professors should not speak so fast. let us understand things. It takes time.

By Walter E K

May 27, 2017

Very preliminary, most of the course don't have a PPT or handout. May be helpful if you know very few about data science.

By Pablo T

Dec 25, 2017

ok introduction if you have no background in DS.

if you have experience, take the quizes first, its pretty basic.

By Deleted A

Nov 29, 2016

Would have been better if quiz access is available for free so that it is easier to reinforce concepts learnt.

By Daniel S

Sep 15, 2020

I know its a crash course, but probably need to be less general management stuff and more on data science

By Karthik S N

Apr 24, 2016

Really basic course. Not needed for people you have already done data science specialization in coursera

By Aarti K

Jan 03, 2018

The teachers spoke really fast with which it became difficult to grasp the words. Overall it was good.

By Hason G

Aug 18, 2017

Would have liked to seen more examples from other industries for those not in health industry

By Peter L

Jul 25, 2018

Added value is highly dependent of your experience with data analysis or data engeneering