The Data Scientist’s Toolbox

4.5
14,604 ratings
3,085 reviews

Course 1 of 10 in the Data Science Specialization

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.
Globe

100% online course

Start instantly and learn at your own schedule.
Clock

Approx. 8 hours to complete

Suggested: 1-4 hours/week
Comment Dots

English

Subtitles: English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

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

GithubData ScienceR ProgrammingData Analysis
Globe

100% online course

Start instantly and learn at your own schedule.
Clock

Approx. 8 hours to complete

Suggested: 1-4 hours/week
Comment Dots

English

Subtitles: English, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew

Syllabus - What you will learn from this course

1

Section
Clock
2 hours to complete

Week 1

During Week 1, you'll learn about the goals and objectives of the Data Science Specialization and each of its components. You'll also get an overview of the field as well as instructions on how to install R....
Reading
16 videos (Total 51 min), 5 readings, 1 quiz
Video16 videos
The Data Scientist's Toolbox5m
Getting Help8m
Finding Answers4m
R Programming Overview2m
Getting Data Overview1m
Exploratory Data Analysis Overview1m
Reproducible Research Overview1m
Statistical Inference Overview1m
Regression Models Overview1m
Practical Machine Learning Overview1m
Building Data Products Overview1m
Installing R on Windows {Roger Peng}3m
Install R on a Mac {Roger Peng}2m
Installing Rstudio {Roger Peng}1m
Installing Outside Software on Mac (OS X Mavericks)1m
Reading5 readings
Welcome to the Data Scientist's Toolbox10m
Pre-Course Survey10m
Syllabus10m
Specialization Textbooks10m
The Elements of Data Analytic Style10m
Quiz1 practice exercises
Week 1 Quiz10m

2

Section
Clock
1 hour to complete

Week 2: Installing the Toolbox

This is the most lecture-intensive week of the course. The primary goal is to get you set up with R, Rstudio, Github, and the other tools we will use throughout the Data Science Specialization and your ongoing work as a data scientist. ...
Reading
9 videos (Total 51 min), 1 quiz
Video9 videos
Command Line Interface16m
Introduction to Git4m
Introduction to Github3m
Creating a Github Repository5m
Basic Git Commands5m
Basic Markdown2m
Installing R Packages5m
Installing Rtools2m
Quiz1 practice exercises
Week 2 Quiz10m

3

Section
Clock
1 hour to complete

Week 3: Conceptual Issues

The Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists. ...
Reading
4 videos (Total 35 min), 1 quiz
Video4 videos
What is Data?5m
What About Big Data?4m
Experimental Design15m
Quiz1 practice exercises
Week 3 Quiz10m

4

Section
Clock
2 hours to complete

Week 4: Course Project Submission & Evaluation

In Week 4, we'll focus on the Course Project. This is your opportunity to install the tools and set up the accounts that you'll need for the rest of the specialization and for work in data science....
Reading
1 reading, 1 quiz
Reading1 readings
Post-Course Survey10m
4.5
Direction Signs

36%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

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 AIApr 24th 2018

This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.

Instructors

Avatar

Jeff Leek, PhD

Associate Professor, Biostatistics
Avatar

Roger D. Peng, PhD

Associate Professor, Biostatistics
Avatar

Brian Caffo, PhD

Professor, Biostatistics

About Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

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.

  • If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.

  • Yes! Coursera provides financial aid to learners who would like to complete a course but cannot afford the course fee. To apply for aid, select "Learn more and apply" in the Financial Aid section below the "Enroll" button. You'll be prompted to complete a simple application; no other paperwork is required.

More questions? Visit the Learner Help Center