About this Course

620,242 recent views

Learner Career Outcomes

39%

started a new career after completing these courses

35%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 8 hours to complete

Suggested: 1-4 hours/week...

English

Subtitles: English, Korean

What you will learn

  • Check

    Create a Github repository

  • Check

    Explain essential study design concepts

  • Check

    Set up R, R-Studio, Github and other useful tools

  • Check

    Understand the data, problems, and tools that data analysts work with

Skills you will gain

Data ScienceGithubR ProgrammingRstudio

Learner Career Outcomes

39%

started a new career after completing these courses

35%

got a tangible career benefit from this course

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 8 hours to complete

Suggested: 1-4 hours/week...

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Content RatingThumbs Up96%(74,878 ratings)Info
Week
1

Week 1

3 hours to complete

Data Science Fundamentals

3 hours to complete
5 videos (Total 40 min), 2 readings, 5 quizzes
5 videos
What is Data Science?9m
What is Data?6m
Getting Help10m
The Data Science Process9m
2 readings
Welcome5m
A Note of Explanation2m
5 practice exercises
What is Data Science?6m
What is Data?6m
Getting Help Quiz6m
Data Science Process8m
Module One Summative Quiz30m
Week
2

Week 2

3 hours to complete

R and RStudio

3 hours to complete
5 videos (Total 34 min)
5 videos
Installing R Studio3m
RStudio Tour7m
R Packages11m
Projects in R5m
6 practice exercises
Installing R8m
Installing R Studio4m
RStudio Tour8m
R Packages10m
Projects in R6m
Module Two Summative Quiz30m
Week
3

Week 3

2 hours to complete

Version Control and GitHub

2 hours to complete
4 videos (Total 28 min)
4 videos
Github and Git8m
Linking Github and R Studio4m
Projects under Version Control4m
5 practice exercises
Version Control6m
GitHub and Git10m
Linking Git/GitHub and RStudio6m
Projects under Version Control8m
Module Three Summative Quiz30m
Week
4

Week 4

5 hours to complete

R Markdown, Scientific Thinking, and Big Data

5 hours to complete
4 videos (Total 34 min)
4 videos
Types of Data Science Questions9m
Experimental Design9m
Big Data6m
5 practice exercises
R Markdown10m
Types of Data Science Questions6m
Experimental Design14m
Big Data6m
Module Four Summative Quiz30m
4.5
4,420 ReviewsChevron Right

Top reviews from The Data Scientist’s Toolbox

Highlights
Foundational tools
(243)
Introductory course
(1056)
By LRSep 8th 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.

By AMJul 22nd 2017

Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.

Instructors

Instructor rating4.45/5 (585 Ratings)Info
Image of instructor, Jeff Leek, PhD

Jeff Leek, PhD 

Associate Professor, Biostatistics
Bloomberg School of Public Health
895,478 Learners
20 Courses
Image of instructor, Roger D. Peng, PhD

Roger D. Peng, PhD 

Associate Professor, Biostatistics
Bloomberg School of Public Health
886,577 Learners
22 Courses
Image of instructor, Brian Caffo, PhD

Brian Caffo, PhD 

Professor, Biostatistics
Bloomberg School of Public Health
901,944 Learners
21 Courses

Offered by

Johns Hopkins University logo

Johns Hopkins University

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.