• R Programming Courses
• R Programming from the Ground Up Training
• Data Analytics with R Training
• MOC 20773 - Analyzing Big Data with Microsoft R
• Data Engineering Bootcamp
• More Courses

• R Programming Courses
• R Programming from the Ground Up Training
• Data Analytics with R Training
• MOC 20773 - Analyzing Big Data with Microsoft R
• Data Engineering Bootcamp
• More Courses

Data Analytics with R Training

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Location

Public Classes: Delivered live online via WebEx and guaranteed to run . Join from anywhere!

Private Classes: Delivered at your offices , or any other location of your choice.

Course Topics

1. Learn the language basic of R.
2. Work with loops and conditionals.
3. Work with built-in datasets.
4. Work with visualization.
5. Work with statstical modeling with R.
6. Work with clustering and classification.
7. Learn about R and big data.

Outline

1. Day One: Language Basics
   1. Course Introduction
   2. About Data Science
      1. Data Science Definition
      2. Process of Doing Data Science
   3. Introducing R Language
   4. Variables and Types
   5. Control Structures (Loops / Conditionals)
   6. R Scalars, Vectors, and Matrices
      1. Defining R Vectors
      2. Matricies
   7. String and Text Manipulation
      1. Character Data Type
      2. File IO
   8. Lists
   9. Functions
      1. Introducing Functions
      2. Closures
      3. lapply/sapply Functions
   10. DataFrames
   11. Labs for All Sections
2. Day Two: Intermediate R Programming
   1. DataFrames and File I/O
   2. Reading Data from Files
   3. Data Preparation
   4. Built-in Datasets
   5. Visualization
      1. Graphics Package
      2. plot() / barplot() / hist() / boxplot() / scatter plot
      3. Heat Map
      4. ggplot2 Package ( qplot(), ggplot())
   6. Exploration with Dplyr
   7. Labs for All Sections
3. Day 3: Advanced Programming With R
   1. Statistical Modeling With R
      1. Statistical Functions
      2. Dealing with NA
      3. Distributions (Binomial, Poisson, Normal)
   2. Regression
      1. Introducing Linear Regressions
   3. Recommendations
   4. Text Processing (tm package / Wordclouds)
   5. Clustering
      1. Introduction to Clustering
      2. KMeans
   6. Classification
      1. Introduction to Classification
      2. Naive Bayes
      3. Decision Trees
      4. Training Using Caret Package
   7. Evaluating Algorithms
   8. R and Big Data
      1. Hadoop
      2. Big Data Ecosystem
      3. RHadoop
   9. Labs for All Sections

Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this R Programming class:

• Basic programming background.

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Training for your Team

Length: 3 Days

• Private Class for your Team
• Online or On-location
• Customizable
• Expert Instructors

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