#1 Specialization

Launch Your Career in Data Science. A nine-course introduction to data science, developed and taught by leading professors.

Johns Hopkins University

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.

- Collect detailed information using R profiler
- Configure statistical programming software
- Make use of R loop functions and debugging tools
- Understand critical programming language concepts

R ProgrammingData AnalysisDebuggingGithub

Section

This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story. ...

28 videos (Total 129 min), 9 readings, 8 quizzes

Installing R on Windows3m

Installing R Studio (Mac)1m

Writing Code / Setting Your Working Directory (Windows)7m

Writing Code / Setting Your Working Directory (Mac)7m

Introduction1m

Overview and History of R16m

Getting Help13m

R Console Input and Evaluation4m

Data Types - R Objects and Attributes4m

Data Types - Vectors and Lists6m

Data Types - Matrices3m

Data Types - Factors4m

Data Types - Missing Values2m

Data Types - Data Frames2m

Data Types - Names Attribute1m

Data Types - Summary0m

Reading Tabular Data5m

Reading Large Tables7m

Textual Data Formats4m

Connections: Interfaces to the Outside World4m

Subsetting - Basics4m

Subsetting - Lists4m

Subsetting - Matrices2m

Subsetting - Partial Matching1m

Subsetting - Removing Missing Values3m

Vectorized Operations3m

Introduction to swirl1m

Welcome to R Programming10m

About the Instructor10m

Pre-Course Survey10m

Syllabus10m

Course Textbook10m

Course Supplement: The Art of Data Science10m

Data Science Podcast: Not So Standard Deviations10m

Getting Started and R Nuts and Bolts10m

Practical R Exercises in swirl Part 110m

Week 1 Quiz40m

Section

Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week....

13 videos (Total 91 min), 3 readings, 5 quizzes

Control Structures - If-else1m

Control Structures - For loops4m

Control Structures - While loops3m

Control Structures - Repeat, Next, Break4m

Your First R Function10m

Functions (part 1)9m

Functions (part 2)7m

Scoping Rules - Symbol Binding10m

Scoping Rules - R Scoping Rules8m

Scoping Rules - Optimization Example (OPTIONAL)9m

Coding Standards8m

Dates and Times10m

Week 2: Programming with R10m

Practical R Exercises in swirl Part 210m

Programming Assignment 1 INSTRUCTIONS: Air Pollution10m

Week 2 Quiz20m

Programming Assignment 1: Quiz20m

Section

We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice....

8 videos (Total 61 min), 2 readings, 4 quizzes

Loop Functions - apply7m

Loop Functions - mapply4m

Loop Functions - tapply3m

Loop Functions - split9m

Debugging Tools - Diagnosing the Problem12m

Debugging Tools - Basic Tools6m

Debugging Tools - Using the Tools8m

Week 3: Loop Functions and Debugging10m

Practical R Exercises in swirl Part 310m

Week 3 Quiz10m

Section

This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R....

6 videos (Total 42 min), 4 readings, 5 quizzes

Simulation - Generating Random Numbers7m

Simulation - Simulating a Linear Model4m

Simulation - Random Sampling2m

R Profiler (part 1)10m

R Profiler (part 2)10m

Week 4: Simulation & Profiling10m

Practical R Exercises in swirl Part 410m

Programming Assignment 3 INSTRUCTIONS: Hospital Quality10m

Post-Course Survey10m

Week 4 Quiz20m

Programming Assignment 3: Quiz20m

4.6

started a new career after completing these courses

got a tangible career benefit from this course

got a pay increase or promotion

By WH•Feb 3rd 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 EJ•Jul 12th 2016

Excellent course! I already knew a lot about R - but this class helped me solidify what I already knew, taught me lots of new tricks, and now I have a certificate that says I know `something' about R!

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....

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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.

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