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The Data Scientist’s Toolbox

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The Data Scientist’s Toolbox

Name: The Data Scientist’s Toolbox
Rating: 4.5901091165082715 (34092 reviews)

This course is part of multiple programs.

Instructors: Jeff Leek, PhD

773,816 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

34,092 reviews

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

96%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

34,092 reviews

2 weeks to complete

at 10 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

21 assignments

Taught in English

91%

of learners achieved a positive career outcome

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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
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this 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.

Module details

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

5 videosTotal 40 minutes

Why Automated Videos?5 minutes
What is Data Science?9 minutes
What is Data?7 minutes
Getting Help10 minutes
The Data Science Process9 minutes

2 readingsTotal 7 minutes

Welcome5 minutes
A Note of Explanation2 minutes

5 assignmentsTotal 126 minutes

Module One Summative Quiz30 minutes
What is Data Science?6 minutes
What is Data?30 minutes
Getting Help Quiz30 minutes
Data Science Process30 minutes

5 pluginsTotal 75 minutes

Why Automated Videos?15 minutes
What is data science?15 minutes
What Is Data?15 minutes
Getting Help15 minutes
The Data Science Process15 minutes

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

5 videosTotal 34 minutes

Installing R6 minutes
Installing R Studio3 minutes
RStudio Tour7 minutes
R Packages12 minutes
Projects in R6 minutes

6 assignmentsTotal 180 minutes

Module Two Summative Quiz30 minutes
Installing R30 minutes
Installing R Studio30 minutes
RStudio Tour30 minutes
R Packages30 minutes
Projects in R30 minutes

5 pluginsTotal 75 minutes

Installing R15 minutes
Installing R Studio15 minutes
RStudio Tour15 minutes
R Packages15 minutes
Projects in R15 minutes

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

4 videosTotal 28 minutes

Version Control11 minutes
Github and Git9 minutes
Linking Github and R Studio4 minutes
Projects under Version Control4 minutes

5 assignmentsTotal 150 minutes

Module Three Summative Quiz30 minutes
Version Control30 minutes
GitHub and Git30 minutes
Linking Git/GitHub and RStudio30 minutes
Projects under Version Control30 minutes

4 pluginsTotal 60 minutes

Version Control15 minutes
GitHub and Git15 minutes
Linking GitHub and RStudio15 minutes
Projects under version control15 minutes

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

4 videosTotal 34 minutes

R Markdown8 minutes
Types of Data Science Questions10 minutes
Experimental Design9 minutes
Big Data7 minutes

5 assignmentsTotal 150 minutes

Module Four Summative Quiz30 minutes
R Markdown30 minutes
Types of Data Science Questions30 minutes
Experimental Design30 minutes
Big Data30 minutes

1 peer reviewTotal 60 minutes

Assemble your toolbox60 minutes

4 pluginsTotal 60 minutes

R Markdown15 minutes
Types of Data Science Questions15 minutes
Experimental Design15 minutes
Big Data15 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Instructor ratings

(4,803 ratings)

Jeff Leek, PhD

Johns Hopkins University

32 Courses1,758,861 learners

Offered by

Johns Hopkins University

Learner reviews

5 stars
69.48%
4 stars
23.15%
3 stars
5.25%
2 stars
1.12%
1 star
0.98%

Showing 3 of 34092

Reviewed on Oct 7, 2020

Great course content and very much informative with the different options of learning either through text or video. A good introductory course to the Data Science: Foundations Using R Specialization.

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.

Reviewed on Sep 10, 2019

Very clear and concise and is very easy to follow for those who aren't very experienced with setting up a dev environment or git. A little on the easy side but I'm sure more challenges are to follow!

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