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
The Data Scientist’s Toolbox
This course is part of multiple programs.
Instructors: Jeff Leek, PhD
757,335 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.6
(34,005 reviews)
17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
96%
Most learners liked this course
What you'll learn
Set up R, R-Studio, Github and other useful tools
Understand the data, problems, and tools that data analysts use
Explain essential study design concepts
Create a Github repository
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
21 assignments
Taught in English
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This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
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There are 4 modules in this course
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.
What's included
5 videos2 readings5 assignments5 plugins
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.
What's included
5 videos6 assignments5 plugins
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.
What's included
4 videos5 assignments4 plugins
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.
What's included
4 videos5 assignments1 peer review4 plugins
Instructors
Instructor ratings
4.5 (4,780 ratings)Offered by
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Learner reviews
4.6
34,005 reviews
- 5 stars
69.45%
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23.19%
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5.26%
- 2 stars
1.11%
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Showing 3 of 34005
DS
5
Reviewed on Sep 9, 2020
It covers pretty much everything basic required to start your journey in field of Data Science. It is not very difficult to understand, thereby boosting your confidence to give Data Science a try.
NB
5
Reviewed on Jun 2, 2017
Nice Course. Basics are very well taught in this course.Thank you JHU and Coursera for this course. I have decided to donate 10% of my first salary to coursera once I am complete this and get intern.
DG
5
Reviewed on Jun 12, 2017
Pretty easy, and never felt like it was a struggle to find the information that was needed. Basically a setup course for out things you'll need for the likes of R Programming and Data Science work.
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