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.
About this Course
4.5
18,034 ratings
What you will learn
Create a Github repository
Explain essential study design concepts
Set up R, R-Studio, Github and other useful tools
Understand the data, problems, and tools that data analysts work with
Skills you will gain
Data ScienceGithubR ProgrammingRstudio
Syllabus - What you will learn from this course
Week
12 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.
16 videos (Total 51 min), 5 readings, 1 quiz
16 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
5 readings
Welcome to the Data Scientist's Toolbox10m
Pre-Course Survey10m
Syllabus10m
Specialization Textbooks10m
The Elements of Data Analytic Style10m
1 practice exercise
Week 1 Quiz10m
Week
21 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.
9 videos (Total 51 min), 1 quiz
9 videos
Command Line Interface16m
Introduction to Git4m
Introduction to Github3m
Creating a Github Repository5m
Basic Git Commands5m
Basic Markdown2m
Installing R Packages5m
Installing Rtools2m
1 practice exercise
Week 2 Quiz10m
Week
31 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.
4 videos (Total 35 min), 1 quiz
1 practice exercise
Week 3 Quiz10m
Week
42 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.
1 reading, 1 quiz
1 reading
Post-Course Survey10m
38%
started a new career after completing these courses
35%
got a tangible career benefit from this course
Top Reviews
Highlights
Foundational tools
(243)Introductory course
(1056)By LR•Sep 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 AI•Apr 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
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
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.
About the Data Science Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Frequently Asked Questions
When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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