R programming in 4 hours - Frequently used syntax by example
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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
4.7 (23 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
375 students enrolled
Last updated 6/2015
English
Price: $20
30-Day Money-Back Guarantee
Includes:
  • 1 hour on-demand video
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
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
View Curriculum
Requirements
  • This is a basic course
  • intermediary computer skills required to install R, R studio and run commands seeing it on video
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.

Who 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
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Curriculum For This Course
Expand All 25 Lectures Collapse All 25 Lectures 02:03:30
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Getting Started with R
7 Lectures 11:52

Welcome Note
01:06


Installation of R and R studio / Understand R environment
03:42

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

Preview 01:29

Download files used in the course
02:39

Section PDF
11 pages
+
Work with Data
11 Lectures 43:22

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

Import Data in R
06:52

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


Derive New Variables
07:21

Arithmetic and Logical Operators
01:37

Section PDF
27 pages
+
Other R procedure
7 Lectures 15:16

Filter data, Keep some fields, drop some fields, sort data and show top n rows
07:18


Regression Analysis
01:46

Using Substring Function
02:13

Closing Words
00:50

Section PDF
15 pages
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
800 Reviews
12,395 Students
11 Courses
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.