Name: A Crash Course in Data Science
Brand: Johns Hopkins University
Availability: OnlineOnly
Rating: 4.5 (1530 reviews)

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

4.5

stars

8,371 ratings

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

Jul 12, 2018

Great introduction. What I found interesting is being able to reflect on projects I have been on relating to data analysis and seeing the steps, benefits and pitfalls explained for me.

Jun 20, 2016

fairly general concepts of data science, but may be quite helpful l for people totally new to the field of data (e.g. not having any idea), or as material to review the big picture of data science

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