Data Analytics with R Training

Customized Onsite Training

3
Days
  • Customized Content
  • For Groups of 5+
  • Online or On-location
  • Expert Instructors
Overview

R is a very popular, open source environment for statistical computing, data analytics and graphics. This Data Analytics with R Training class introduces R programming language to students. It covers language fundamentals, libraries, and advanced concepts and advanced data analytics and graphing with real world data.

Goals
  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.
Preparing for Class

No cancelation for low enrollment

Certified Microsoft Partner

Registered Education Provider (R.E.P.)

GSA schedule pricing

81,519

Students who have taken Live Online Training

15,629

Organizations who trust Webucator for their training needs

100%

Satisfaction guarantee and retake option

9.40

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One of the best classes I've taken. I wish this was my Java class during college!

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