Crash Course R programming from Scratch workout

Crash Course – R programming from Scratch & workout, available at $19.99, has an average rating of 3.95, with 49 lectures, based on 62 reviews, and has 542 subscribers.

You will learn about 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 This course is ideal for individuals who are Those who wants to learn basic R programming with Example or Those who know SAS and know wants to know, how to get the same analysis done in R It is particularly useful for Those who wants to learn basic R programming with Example or Those who know SAS and know wants to know, how to get the same analysis done in R.

Enroll now: Crash Course – R programming from Scratch & workout

Summary

Title: Crash Course – R programming from Scratch & workout

Price: $19.99

Average Rating: 3.95

Number of Lectures: 49

Number of Published Lectures: 49

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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 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

10 Practice case studies

  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 

Section 4 – Practice Case Studies – apply your knowledge to solve business problems

There are 10 workouts included in the course, which will help users to master the concepts of R programming from industrial usage perspective. Students should try these practices on their own before jumping to solution provided.

  Why take this course? 

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

Course Curriculum

Chapter 1: Getting Started with R

Lecture 1: Course Overview

Lecture 2: Welcome Note

Lecture 3: Section Agenda

Lecture 4: Installation of R and R studio / Understand R environment

Lecture 5: Understand Data for Workout

Lecture 6: Download files used in the course

Lecture 7: Section PDF

Chapter 2: Work with Data

Lecture 1: Section Overview

Lecture 2: Import Data in R

Lecture 3: Direct data entry in R

Lecture 4: View Data and Metadata

Lecture 5: Frequency Distribution Analysis

Lecture 6: Numeric Variable Analysis / Univariate Analysis

Lecture 7: Merge Data sets

Lecture 8: Append Data sets

Lecture 9: Derive New Variables

Lecture 10: Arithmetic and Logical Operators

Lecture 11: Section PDF

Chapter 3: Other R procedure

Lecture 1: Section Overview

Lecture 2: Filter data, Keep some fields, drop some fields, sort data and show top n rows

Lecture 3: Cross Tab Analysis

Lecture 4: Regression Analysis

Lecture 5: Using Substring Function

Lecture 6: Section PDF

Chapter 4: Practice Case Studies – apply your knowledge to solve business problems

Lecture 1: Why these practice case studies?

Lecture 2: Q 1: Find lnew in list B (B-A) stuff

Lecture 3: A 1: Find new in list B (B-A) stuff

Lecture 4: Q 2: Variable Substring Challenge

Lecture 5: A 2: Variable Substring Challenge

Lecture 6: Q 3: Investigate linear relationship between variables

Lecture 7: A 3: Investigate linear relationship between variables

Lecture 8: Q 4:Tabular report in presence of 2 class variable & different statistics neede

Lecture 9: A 4:Tabular report in presence of 2 class variable & different statistics neede

Lecture 10: Q 5s: Little help about seasonality and pair T test for next problem

Lecture 11: Q 5: Pair T Test in presence of seasonality

Lecture 12: A 5: Pair T Test in presence of seasonality

Lecture 13: Q 6: Calculate red car percentage for different age group & run chi square test

Lecture 14: A 6: Calculate red car percentage for different age group & run chi square test

Lecture 15: Q 7: Calculate relative variance (Coefficient of Variance)

Lecture 16: Q 7s: What is Coefficient of Variance? Why it is required?

Lecture 17: A 7: Calculate relative variance (Coefficient of Variance)

Lecture 18: Q 8: Work with date and create stacked chart

Lecture 19: A 8: Work with date and create stacked chart

Lecture 20: Q 9: Using SQL within R for agreegate function based complexities

Lecture 21: A 9: Using SQL within R for agreegate function based complexities

Lecture 22: Q 10: Customized complex text for each row (row wise max/min and field name)

Lecture 23: A 10: Customized complex text for each row (row wise max/min and field name)

Chapter 5: Appendix Topics (based on students demand)

Lecture 1: Word Cloud using R

Lecture 2: Closing Words

Instructors

Gopal Prasad Malakar
Trains Industry Practices on data science / machine learning

Rating Distribution

1 stars: 2 votes

2 stars: 4 votes

3 stars: 8 votes

4 stars: 15 votes

5 stars: 33 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!