GithubMachine LearningR ProgrammingRegression AnalysisData ScienceRstudioData AnalysisDebuggingData ManipulationRegular Expression (REGEX)Data CleansingCluster Analysis
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Offered By
#1 Specialization
What you will learn
Use R to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.
Use GitHub to manage data science projects.
Perform regression analysis, least squares and inference using regression models.
Skills you will gain
About this Specialization
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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.
Learner Career Outcomes
43%
Started a new career after completing this specialization.
19%
Got a pay increase or promotion.
Learner Career Outcomes
43%
Started a new career after completing this specialization.
19%
Got a pay increase or promotion.
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
You should have beginner level experience in Python. Familarity with regression is recommended.
Approx. 8 months to complete
Suggested 6 hours/week
English
Subtitles: English, Korean, Arabic, French, Chinese (Simplified), Portuguese (Brazilian), Vietnamese, Spanish, Japanese, Russian
How the Specialization Works
Take Courses
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Hands-on Project
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
There are 10 Courses in this Specialization
The Data Scientist’s Toolbox
22,483 ratings
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.
R Programming
16,177 ratings
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Getting and Cleaning Data
6,765 ratings
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Exploratory Data Analysis
5,150 ratings
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
Offered by
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.
Reviews
TOP REVIEWS FROM DATA SCIENCE
by WHFeb 2, 2016
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
by AKJul 13, 2016
It's a very introductory course and in a sense I don't feel like I learnt something useful, except the part that shows how to install all the tools that are needed for the rest of the Specialization.
by GOJul 25, 2016
Consistent yet very basic course. I would only recommend this course if you are willing to complete the whole Data Science specialization or if you have troubles with the basic functioning of GitHub.
by SSMar 28, 2017
Wow i finally managed to finish the specialization!! definitely learned a lot and also found out difficulties in building predictors by trying to balancing speed, accuracy and memory constraints!!!
by WNAug 19, 2018
A very short (could be completed in a day) course to get you started on the rest of the Specialization. You will learn very basics of installing R and playing around with GitHub. That's it really.
by JCMay 4, 2016
Compared to the other classes in the JHU Data Science specialization, this one is pretty laid back. It's useful information, and teaches a few nice tricks on how to present data analysis results.
by ASAug 26, 2017
A very easy course, can be completed in 3-4 days at max. Having done R programming course first, this was a breeze taking a couple days. Good for people starting off with the specialization.
by MPJan 12, 2016
The course is primarily aimed to get you primed and set-up for the other courses in the Data Science Specialization. This is a fairly lightweight course and can be completed pretty quickly.
Frequently Asked Questions
What is the refund policy?
Can I just enroll in a single course?
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Is financial aid available?
Can I take the course for free?
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
How long does it take to complete the Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.
How often is each course in the Specialization offered?
Each course in the Specialization is offered monthly.
What background knowledge is necessary?
Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).
Do I need to take the courses in a specific order?
Begin by taking The Data Scientist's Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.
Will I earn university credit for completing the Specialization?
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Specialization?
You’ll have a foundational understanding of the field and be prepared to continue studying data science.
Can I sign up for the course without paying or applying for financial aid?
Yes, you can access the course for free via www.coursera.org/jhu. This will allow you to explore the course, watch lectures, and participate in discussions for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify for financial aid.
More questions? Visit the Learner Help Center.
