R Programming Language Online Course
R Programming Language for Statistical Computing and Graphical Representation
Updated on Sep, 2023
Language - English
Course Description
This course is intended for data miners, statisticians, and software developers who are interested in creating statistical software using the R programming language. This lesson will offer you a thorough overview of practically all of the R programming language's ideas, which will enable you to advance to greater levels of proficiency if you are just learning the language.
R Programming Language Online Course
R is a powerful programming language that is used for statistical computing, data analysis, and data visualization. It is a free and open-source software environment that is used by data scientists, statisticians, and researchers around the world. This course is designed to teach you the basics of R programming from the ground up.
You should have a basic understanding of computer programming jargon before continuing with this course. You will understand the R programming ideas and go quickly through the learning process if you have a basic familiarity with any of the computer languages.
Who this course is for:
All graduates and pursuing students.
Those who wish to excel in Data Analytics
Goals
What will you learn in this course:
R Programming Language for Statistical Computing and Graphical Representation
Learn how to use R documentation
Understand different data types and structures in R
Learn how to install R packages
Prerequisites
What are the prerequisites for this course?
Basic computer knowledge
Basic programming knowledge
Curriculum
Check out the detailed breakdown of what’s inside the course
R Programming Language
82 Lectures
-
Introduction to R Programming
20:05
20:05
-
R Installation & Setting R Environment
50:16
50:16
-
Variables, Operators & Data types
53:10
53:10
-
Structures
47:08
47:08
-
Vectors
01:04:04
01:04:04
-
Vector Manipulation & Sub-Setting
01:06:03
01:06:03
-
Constants
41:38
41:38
-
RStudio Installation & Lists Part 1
01:02:20
01:02:20
-
Lists Part 2
47:44
47:44
-
List Manipulation, Sub-Setting & Merging
45:01
45:01
-
List to Vector & Matrix Part 1
49:52
49:52
-
Matrix Part 2
44:02
44:02
-
Matrix Accessing
48:26
48:26
-
Matrix Manipulation, rep function & Data Frame
56:08
56:08
-
Data Frame Accessing
54:01
54:01
-
Column Bind & Row Bind
50:32
50:32
-
Merging Data Frames Part 1
50:04
50:04
-
Merging Data Frames Part 2
54:26
54:26
-
Melting & Casting
52:55
52:55
-
Arrays
43:50
43:50
-
Factors
50:53
50:53
-
Functions & Control Flow Statements
40:27
40:27
-
Strings & String Manipulation with Base Package
53:22
53:22
-
String Manipulation with Stringi Package Part 1
58:33
58:33
-
String Manipulation with Stringi Package Part 2 & Date and Time Part 1
48:13
48:13
-
Date and Time Part 2
53:19
53:19
-
Data Extraction from CSV File
42:02
42:02
-
Data Extraction from EXCEL File
50:40
50:40
-
Data Extraction from CLIPBOARD, URL, XML & JSON Files
50:04
50:04
-
Introduction to DBMS
50:22
50:22
-
Structured Query Language, MySQL Installation & Normalization
41:35
41:35
-
Data Definition Language Commands
01:02:24
01:02:24
-
Data Manipulation Language Commands
47:29
47:29
-
Sub Queries & Constraints
16:07
16:07
-
Aggregate Functions, Clauses & Views
07:21
07:21
-
Data Extraction from Databases Part 1
52:31
52:31
-
Data Extraction from Databases Part 2 & DPlyr Package Part 1
52:39
52:39
-
DPlyr Package Part 2
51:36
51:36
-
DPlyr Functions on Air Quality Data Set
57:01
57:01
-
Plyr Package for Data Analysis
46:51
46:51
-
Tidyr Package with Functions
50:48
50:48
-
Factor Analysis
57:11
57:11
-
Prob.Table & CrossTable
50:22
50:22
-
Statistical Observations Part 1
51:48
51:48
-
Statistical Observations Part 2
40:35
40:35
-
Statistical Analysis on Credit Data set
01:00:29
01:00:29
-
Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
59:20
59:20
-
Box Plots
54:38
54:38
-
Histograms & Line Graphs
45:26
45:26
-
Scatter Plots & Scatter plot Matrices
01:03:47
01:03:47
-
Low Level Plotting
56:01
56:01
-
Bar Plot & Density Plot
46:31
46:31
-
Combining Plots
35:37
35:37
-
Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot
51:07
51:07
-
Mat Plot, ECDF & Box Plot with IRIS Data set
01:02:55
01:02:55
-
Additional Box Plot Style Parameters
01:01:41
01:01:41
-
Set.Seed Function & Preparing Data for Plotting
01:09:42
01:09:42
-
QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
59:26
59:26
-
ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal
54:42
54:42
-
Data Exploration and Visualization
51:00
51:00
-
Machine Learning, Types of ML with Algorithms
01:04:53
01:04:53
-
How Machine Solve Real Time Problems
43:33
43:33
-
Nearest Neighbor(KNN) Classification
01:07:45
01:07:45
-
KNN Classification with Cancer Data set Part 1
01:03:15
01:03:15
-
KNN Classification with Cancer Data set Part 2
43:12
43:12
-
Navie Bayes Classification
43:53
43:53
-
Navie Bayes Classification with SMS Spam Data set & Text Mining
58:43
58:43
-
WordCloud & Document Term Matrix
56:39
56:39
-
Train & Evaluate a Model using Navie Bayes
01:11:40
01:11:40
-
MarkDown using Knitr Package
01:02:15
01:02:15
-
Decision Trees
57:16
57:16
-
Decision Trees with Credit Data set Part 1
47:03
47:03
-
Decision Trees with Credit Data set Part 2
45:11
45:11
-
Support Vector Machine, Neural Networks & Random Forest
46:50
46:50
-
Regression & Linear Regression
44:04
44:04
-
Multiple Regression
48:24
48:24
-
Generalized Linear Regression, Non Linear Regression & Logistic Regression
35:37
35:37
-
Clustering
29:04
29:04
-
K-Means Clustering with SNS Data Analysis
01:06:18
01:06:18
-
Association Rules (Market Basket Analysis)
39:33
39:33
-
Market Basket Analysis using Association Rules with Groceries Data set
56:19
56:19
-
Python Libraries for Data Science
22:32
22:32
Instructor Details
DATAhill Solutions Srinivas Reddy
Data ScientistMr. Srinivas Reddy is Founder & MD of DATAhill Solutions
He is Research Scholar (Ph.D) on Artificial Intelligence & Machine Learning
He Received Masters of Technology in Computer Science & Engineering from JNTU, MICROSOFT Certified Professional, IBM Certified Professional & Certified from IIT Kanpur & IIT Ropar.
Having 10+ Years of Experience in Software & Training.
His Experience includes Managing, Data Processing, Data Cleaning, Predicting and Analyzing of Large volume of Business Data.
Expertise in Data Science, Data Analytics, Machine Learning, Deep Learning, Artificial Intelligence, Python, R, Weka, Data Management & BI Technologies.
Having Patents and Publications in Various Fields such as Artificial Intelligence, Machine Learning and Data Science Technologies.
Professionally, He is Data Science Management Consultant with over 7+ years of Experience in Finance, Retail, Transport and other Industries.
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