Data Analytics with R Training

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

Training for your Team

Length: 3 Days
  • Private Class for your Team
  • Online or On-location
  • Customizable
  • Expert Instructors

What people say about our training

A semester's worth of class in one solid, easy-to-follow, day!
Nathan Woolard
University of Central Oklahoma
I would highly recommend this course.
Brenda Curry
PREEMPT CORP
The InfoPath 2010 online training course was well designed and well led by a knowledgeable instructor. I will be looking to Webucator for our next class.
Charles Scott
Plains All American
Webucator provided a very thorough class. The ability to ask questions, have the instructor view my own screen, and practice tasks made it a very valuable learning experience.
Jeannine Little
P2 Energy Solutions

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GSA schedule pricing

61,469

Students who have taken Instructor-led Training

11,766

Organizations who trust Webucator for their Instructor-led training needs

100%

Satisfaction guarantee and retake option

9.21

Students rated our Data Analytics with R Training trainers 9.21 out of 10 based on 7 reviews

Contact Us or call 1-877-932-8228