Introduction to R

£395.00

What Will You Learn?

  • How to write queries that involve multiple tables and summary queries
  • How to modify data and understand the logical programming aspects of T-SQL

Category:

DESCRIPTION

During this R programming virtual training course, you will learn how to use R for data analysis. We’ll start with a discussion of the R language and how to install the R language interpreter and RStudio, the popular R integrated development environment. You’ll learn how R can be extended with modules, called packages, and discuss some of the popular packages in use. We’ll delve into the unique features of R that make it so suitable for data science. The core R data structures; vectors, matrices, arrays, and data frames, will be discussed with examples. Before moving into predictive modeling, we’ll do a quick overview of key concepts in statistics. From there we will walk through the training, testing, and evaluation of a predictive model. Since many people will be using data from relational databases, we will explain how to use the RODBC package to read in data using a SQL Server table as an example. The course wraps up by discussing related topics such as scaling R programs, creating R based web applications, and creating dynamic presentations with R Markdown.

INSTRUCTOR

BRYAN CAFFERKYBRYAN CAFFERKY

BI Consultant and Trainer
Bryan Cafferky is a Microsoft Technical Solutions Professional focused on helping customers understand and implement Data Analysis, Machine Learning, and AI solutions in the health care industry. He is a Microsoft 2017 Data Platform MVP and a 2016 Cloud and Data Center Management MVP. Bryan is the author of Pro PowerShell for Database Developers by Apress, available on Amazon. He leads The RI Microsoft BI User Group, and The Greater Boston Area Data Science, Machine Learning, and AI Group. He has been working with the SQL Server stack since 1997 and implemented projects in the banking, insurance, e-commerce, utility, and health care industries. He holds a bachelor of science in computer information systems and a Master’s degree in Business Administration.

 

WHAT TO KNOW BEFORE THE CLASS

A basic understanding of data structures would be helpful but not required.


Duration: 8:32:19
During this R programming virtual training course, you will learn how to use R for data analysis. We’ll start with a discussion of the R language and how to install the R language interpreter and RStudio, the popular R integrated development environment. We’ll learn how R can be extended with modules called packages and discuss some of the popular packages in use. We’ll delve into the unique features of R that make it so suitable for data science. The core R data structures; vectors, matrices, arrays, and data frames, will be discussed with examples. Before moving into predictive modeling, we’ll do a quick overview of key concepts in statistics. From there we will walk through the training, testing, and evaluation of a predictive model. Since many people will be using data from relational databases, we will explain how to use the RODBC package to read in data using a SQL Server table as an example. The course wraps up by discussing related topics such as scaling R programs, creating R based web applications, and creating dynamic presentations with R Markdown.
Introduction to R - What you need to get started
Module 01 - Getting Started with R
10:23
Module 02 - An Introduction to RStudio
22:53
Module 03 - Extending R with Packages
33:31
Module 04A - Vector Fundamentals (Understanding Vectors)
20:08
Module 04B - Vector Fundamentals (Leveraging Vectors)
22:02
Module 05 - Matrices Fundamentals
23:36
Module 06 - Array Fundamentals
20:55
Module 07 - List Fundamentals
33:32
Module 08 - Using Data Frames
33:01
Module 09 - Statistics (What you need to know)
41:28
Module 10 - Data Quality and Missing Data
22:59
Module 11 - Evaluating Data Relationships
21:25
Module 12 - Overview of Data Science
18:06
Module 13 - Creating a Predictive Model
33:07
Module 14 - Comparing Models
16:49
Module 15 - Choosing a Predictive Model
13:54
Module 16 - More On File Formats
40:58
Module 17 - Reading Data from Relational Databases
28:34
Module 18 - More Topics on R
35:06
Module 19 - Wrapping Up
19:52
Class Survey

SYSTEM REQUIREMENTS