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. This course is a comprehensive hands-on look at the common scenarios encountered in analysis and presents practical solutions. In this course, special attention is paid to data science theory including AI grouping theory. A discussion of using R with AI libraries like Madlib are included.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

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)

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
10

  • Data Visualization

  • Powerful Data through Visualization- Communicating the Message
    Techniques in Data Visualization
    Data Visualization Tools
    Examples
11

  • 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).
12

  • 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
13

  • Business Rule Systems

  • Rule Systems in the Enterprise
    Enterprise Service Busses
    Drools & Using R with Drools
14

  • R with AWS

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

Audience

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.

Language

English

Prerequisites

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. Follow On Courses: Our core R and Python programming, data science, analytics, AI and machine learning training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty. Please inquire for next step recommendations based on your goals

$1,995

Length: 3.0 days (24 hours)

Level:

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29
Nov
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10:00 AM ET -
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