R Programming for Data Science & Analytics

Learning Objectives

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:

• R Language and Mathematics
• How to work with R Vectors
• How to read and write data from files, and how to categorize data in factors
• How to work with Dates and perform Date math
• How to work with multiple dimensions and DataFrame essentials
• Essential Data Science and how to use R with it
• Visualization in R
• How R can be used in Spark (Optional / Overview)

Audience & Pre-Requisites

This course, geared for Data Analyst and Data Scientists who need to learn the essentials of how to program in R. Incoming students should have prior experience working with Excel or SAS, and should know the basics of SQL. Students should have intermediate-level experience in their field, and prior experience working with programming languages.

1. From Excel or SAS to R (Optional)

• Common challenges with Excel / SAS
• The R Environment
• Hello, R

2. Working with R Studio

• Rshiny
• Rpresentations
• Rmarkdown

3. R Basics

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

4. Vectors

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

5. Reading and Writing

• Text Manipulation
• Factors

6. Dates

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

7. Multiple Dimensions

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

8. R in Data Science

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

9. R with MadLib

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

1. Data Visualization

• Powerful Data through Visualization: Communicating the Message
• Techniques in Data Visualization
• Data Visualization Tools
• Examples

1. Databases, Data lakes & Additional Topics

• Building connections to Databases and Data lakes, for both Python and R (using Hive server)
• Methods to “query” data from database and data lakes, for both Python and R
• Creating and passing macro variables.  Specifically, R sprint, paste, paste0, and paste3 (not sure of the equivalent in Python).

Optional - Time Permitting Topics

2. 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

3. Business Rule Systems

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

4. R with AWS

• Best practices for working with AWS (completely outside of R and Python)

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials