An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

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Self-Paced

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Learn the R statistical programming language, the lingua franca of data science. R is rapidly becoming the leading language in data science and statistics. Today, the R programming language is the tool of choice for data scientists in every industry and field. Whether you are a full-time number cruncher, or just the occasional data analyst, R will suit your needs.

This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R.

Starting from variables and basic operations, you will learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.

What makes this R programming course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform.

Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.

Enjoy!

**What you'll learn:**

- R language fundamentals and basic syntax

- What R is and how it’s used to perform data analysis

- You will become familiar with the major R data structures

- You will make your own visualizations using R

**Section 1: Introduction to Basics**

Take your first steps with R. Discover the basic data types in R and assign your first variable.

**Section 2: Vectors**

Analyze gambling behaviour using vectors. Create, name and select elements from vectors.

**Section 3: Matrices**

Learn how to work with matrices in R. Do basic computations with them and demonstrate your knowledge by analyzing the Star Wars box office figures.

**Section 4: Factors**

R stores categorical data in factors. Learn how to create, subset and compare categorical data.

**Section 5: Data Frames**

When working R, you’ll probably deal with Data Frames all the time. Therefore, you need to know how to create one, select the most interesting parts of it, and order them.

**Section 6: Lists**

Lists allow you to store components of different types. Section 6 will show you how to deal with lists.

**Section 7: Basic Graphics**

Discover R’s packages to do graphics and create your own data visualizations.