R Programming Language Online Course
R Programming Language for Statistical Computing and Graphical Representation
Development,Data Science,R Programming
Lectures -83
Resources -84
Duration -68.5 hours
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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
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
Basic computer knowledge
Basic programming knowledge
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Curriculum
Check out the detailed breakdown of what’s inside the course
R Programming Language
82 Lectures
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Introduction to R Programming 20:05 20:05
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R Installation & Setting R Environment 50:16 50:16
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Variables, Operators & Data types 53:10 53:10
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Structures 47:08 47:08
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Vectors 01:04:04 01:04:04
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Vector Manipulation & Sub-Setting 01:06:03 01:06:03
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Constants 41:38 41:38
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RStudio Installation & Lists Part 1 01:02:20 01:02:20
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Lists Part 2 47:44 47:44
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List Manipulation, Sub-Setting & Merging 45:01 45:01
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List to Vector & Matrix Part 1 49:52 49:52
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Matrix Part 2 44:02 44:02
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Matrix Accessing 48:26 48:26
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Matrix Manipulation, rep function & Data Frame 56:08 56:08
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Data Frame Accessing 54:01 54:01
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Column Bind & Row Bind 50:32 50:32
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Merging Data Frames Part 1 50:04 50:04
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Merging Data Frames Part 2 54:26 54:26
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Melting & Casting 52:55 52:55
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Arrays 43:50 43:50
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Factors 50:53 50:53
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Functions & Control Flow Statements 40:27 40:27
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Strings & String Manipulation with Base Package 53:22 53:22
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String Manipulation with Stringi Package Part 1 58:33 58:33
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String Manipulation with Stringi Package Part 2 & Date and Time Part 1 48:13 48:13
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Date and Time Part 2 53:19 53:19
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Data Extraction from CSV File 42:02 42:02
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Data Extraction from EXCEL File 50:40 50:40
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Data Extraction from CLIPBOARD, URL, XML & JSON Files 50:04 50:04
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Introduction to DBMS 50:22 50:22
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Structured Query Language, MySQL Installation & Normalization 41:35 41:35
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Data Definition Language Commands 01:02:24 01:02:24
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Data Manipulation Language Commands 47:29 47:29
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Sub Queries & Constraints 16:07 16:07
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Aggregate Functions, Clauses & Views 07:21 07:21
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Data Extraction from Databases Part 1 52:31 52:31
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Data Extraction from Databases Part 2 & DPlyr Package Part 1 52:39 52:39
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DPlyr Package Part 2 51:36 51:36
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DPlyr Functions on Air Quality Data Set 57:01 57:01
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Plyr Package for Data Analysis 46:51 46:51
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Tidyr Package with Functions 50:48 50:48
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Factor Analysis 57:11 57:11
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Prob.Table & CrossTable 50:22 50:22
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Statistical Observations Part 1 51:48 51:48
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Statistical Observations Part 2 40:35 40:35
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Statistical Analysis on Credit Data set 01:00:29 01:00:29
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Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts 59:20 59:20
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Box Plots 54:38 54:38
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Histograms & Line Graphs 45:26 45:26
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Scatter Plots & Scatter plot Matrices 01:03:47 01:03:47
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Low Level Plotting 56:01 56:01
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Bar Plot & Density Plot 46:31 46:31
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Combining Plots 35:37 35:37
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Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot 51:07 51:07
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Mat Plot, ECDF & Box Plot with IRIS Data set 01:02:55 01:02:55
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Additional Box Plot Style Parameters 01:01:41 01:01:41
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Set.Seed Function & Preparing Data for Plotting 01:09:42 01:09:42
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QPlot, ViolinPlot, Statistical Methods & Correlation Analysis 59:26 59:26
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ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal 54:42 54:42
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Data Exploration and Visualization 51:00 51:00
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Machine Learning, Types of ML with Algorithms 01:04:53 01:04:53
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How Machine Solve Real Time Problems 43:33 43:33
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Nearest Neighbor(KNN) Classification 01:07:45 01:07:45
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KNN Classification with Cancer Data set Part 1 01:03:15 01:03:15
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KNN Classification with Cancer Data set Part 2 43:12 43:12
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Navie Bayes Classification 43:53 43:53
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Navie Bayes Classification with SMS Spam Data set & Text Mining 58:43 58:43
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WordCloud & Document Term Matrix 56:39 56:39
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Train & Evaluate a Model using Navie Bayes 01:11:40 01:11:40
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MarkDown using Knitr Package 01:02:15 01:02:15
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Decision Trees 57:16 57:16
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Decision Trees with Credit Data set Part 1 47:03 47:03
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Decision Trees with Credit Data set Part 2 45:11 45:11
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Support Vector Machine, Neural Networks & Random Forest 46:50 46:50
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Regression & Linear Regression 44:04 44:04
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Multiple Regression 48:24 48:24
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Generalized Linear Regression, Non Linear Regression & Logistic Regression 35:37 35:37
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Clustering 29:04 29:04
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K-Means Clustering with SNS Data Analysis 01:06:18 01:06:18
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Association Rules (Market Basket Analysis) 39:33 39:33
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Market Basket Analysis using Association Rules with Groceries Data set 56:19 56:19
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Python Libraries for Data Science 22:32 22:32
Instructor Details
![DATAhill Solutions Srinivas Reddy](https://www.tutorialspoint.com/assets/profiles/163817/profile/200_161404-1579756642.jpg)
DATAhill Solutions Srinivas Reddy
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