Mastering R for Data Scientists

COURSE OUTLINE:

Description

R is a functional programming environment for business analysts and data scientists. It's a language that many non-programmers can easily work with, naturally extending a skill set that is common to high-end Excel users. It's the perfect tool for when the analyst has a statistical, numerical, or probabilities-based problem based on real data and they've pushed Excel past its limits.

In this course, you will explore common scenarios that are encountered in analysis, and present practical solutions to those challenges. Throughout the course, special attention is paid to data science theory including AI grouping theory. A discussion of using R with AI libraries like Madlib is also included.

Prerequisites

Experience with statistics and probability, as well as good hands-on working knowledge of Excel, is recommended but not required

Learning Objectives

  • R Environment
  • Going from Excel to R
  • Simple math with R
  • How and when to use and apply vectors
  • Manipulating text
  • Formatting dates; manipulating time and operations
  • How to work with multiple dimensions
  • Working with R with Madlib / AI libraries
  • Techniques in Data Visualization
  • Overview of Hadoop and related technologies, and where R plays a role
  • Rule Systems in the Enterprise
  • Working with Drools

1. From Excel to R

  • Common problems with Excel
  • The R Environment
  • Hello, R

2. R Basics

  • Simple Math with R
  • Working with Vectors
  • Functions
  • Comments and Code Structure
  • Using Packages

3. Vectors

  • Vector Properties
  • Creating, Combining, and Iterating
  • Passing and Returning Vectors in Functions
  • Logical Vectors

4. Reading and Writing

  • Text Manipulation
  • Factors

5. Dates

  • Working with Dates
  • Date Formats and formatting
  • Time Manipulation and Operations

6. Multiple Dimensions

  • Adding a second dimension
  • Indices and named rows and columns in a Matrix
  • Matrix calculation
  • n-Dimensional Arrays
  • Data Frames
  • Lists

7. R in Data Science

  • AI Grouping Theory
  • K-means
  • Linear Regression
  • Logistic Regression
  • Elastic Net

8. R with MadLib

  • Importing and Exporting static Data (CSV, Excel)
  • Using Libraries with CRAN
  • K-means with Madlib
  • Regression with Madlib
  • Other libraries

9. Data Visualization

  • Communicating the Message
  • Techniques in Data Visualization
  • Data Visualization Tools
  • Examples

10. R with Hadoop

  • Overview of Hadoop
  • Overview of Distributed Databases
  • Overview of Pig
  • Overview of Mahout
  • Exploiting Hadoop clusters with R
  • Hadoop, Mahout, and R

11. Business Rule Systems

  • Rule Systems in the Enterprise
  • Enterprise Service Busses
  • Drools
  • Using R with Drools