R programming in 4 hours - Frequently used syntax by example

Analytics / Data Science: Learn to Import, Sort, Merge, Subset, Append, Freq, Univariate, Regression, Derive variable
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  • Lectures 25
  • Length 2 hours
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 12/2014 English

Course Description

What is this course about?

This course helps student learn R syntax for

  • Import / Enter / Viewing data and metadata in R
  • Conduct Frequency Distribution Analysis / Univariate Analysis in R
  • Create derived variables
  • Merge / Append data sets
  • Sort / Subset data sets
  • Learn to substring variables
  • Create cross tab analysis
  • conduct Linear Regression analysis

Terminology associated with the course

  • R syntax
  • Data Mining
  • Analytics
  • Machine Learning

Material for the course

  • 20 HD Videos
  • Excel Data sets
  • PDF of presentation
  • R code

How long the course should take?

Approximately 4 hours to internalize the concepts

How is the course structures

Section 1 - explains how to get R, R Studio, Understand environment and data for workout

Section 2 - explains the R syntax through examples

Section 3 - explains some other syntax needed for working

Why take this course?

This course ensure quick learning in a simplified way. It explains the most important aspects of working on data and conduct analysis through example.

What are the requirements?

  • This is a basic course
  • intermediary computer skills required to install R, R studio and run commands seeing it on video

What am I going to get from this course?

  • Import / Enter / Viewing data and metadata in R
  • Conduct Frequency Distribution Analysis / Univariate Analysis in R
  • Create derived variables
  • Merge / Append data sets
  • Sort / Subset data sets
  • Learn to substring variables
  • Create cross tab analysis
  • conduct Linear Regression analysis

What is the target audience?

  • Those who wants to learn basic R programming with Example
  • Those who know SAS and know wants to know, how to get the same analysis done in R

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Getting Started with R
Course Overview
Preview
02:02
Welcome Note
01:06
Section Agenda
Preview
00:54
Installation of R and R studio / Understand R environment
03:42
01:29

Please download associated excel files. This should be available in right side download section

Download files used in the course
02:39
Section PDF
11 pages
Section 2: Work with Data
Section Overview
Preview
01:46
06:52

Please download the R code using right side download area. You should practice along with the course to master the syntax.

Direct data entry in R
05:43
View Data and Metadata
05:48
Frequency Distribution Analysis
03:20
Numeric Variable Analysis / Univariate Analysis
06:14
Merge Data sets
03:04
Append Data sets
Preview
01:37
Derive New Variables
07:21
Arithmetic and Logical Operators
01:37
Section PDF
27 pages
Section 3: Other R procedure
Section Overview
Preview
01:17
Filter data, Keep some fields, drop some fields, sort data and show top n rows
07:18
Cross Tab Analysis
Preview
01:52
Regression Analysis
01:46
Using Substring Function
02:13
Closing Words
00:50
Section PDF
15 pages

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Instructor Biography

Gopal Prasad Malakar, Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 15+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.

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