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Data Science with R
About this Course
In this course you will learn how to program in R and how to use R for effective data analysis. 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. This course comprises of:
- Introduction to R for Data Science [15 hours]
- Programming in R for Data Science [25 hours]
- Data Science Professional Project [10 hours]
Duration: 50 hours
I. Introduction to R for Data Science
Module 1: Introduction to Basics
Take your first steps with R. Discover the basic data types in R and assign your first variable.
Module 2: Vectors
Analyze gambling behavior using vectors. Create, name and select elements from vectors.
Module 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.
Module 4: Factors
R stores categorical data in factors. Learn how to create, subset and compare categorical data.
Module 5: Lists
Lists allow you to store components of different types. Learn how to work with lists.
Module 6: 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.
Module 7: Basic Graphics
Discover R’s packages to do graphics and create your own data visualizations
II. Programming in R for Data Science
Module 1: Introduction
Module 2: Functions and Data Structures
Module 3: Loops and Flow Control
Module 4: Working with Vectors and Matrices
Module 5: Reading in Data
Module 6: Writing Data to Text Files
Module 7: Reading Data from SQL Databases
Module 8: Working with Data
Module 9: Manipulating Data
Module 10: Simulation
Module 11: Linear Models
Module 12: Graphics
III. Data Science Professional Project