Data Analytics using R Programming
Master the fundamentals of data analysis and R programming with this comprehensive online course
Updated on Apr, 2024
Language - English
Development,Data Science and AI ML,Data Analysis
Lectures -83
Resources -82
Duration -68.5 hours
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Course Description
Data Analysis using R Programming is a thorough course that gives students a clear understanding of the most recent and sophisticated features that are offered in a variety of formats. It provides a detailed explanation of how to use R programming to carry out various data analysis tasks. There are several resources in the course that provide step-by-step instructions on how to use a specific feature.
Data Analytics using R Programming Overview
The foundation of data analytics is the process of turning massive amounts of unstructured raw data gathered from many sources into a data product usable for enterprises.
Over the past ten years, the amount of data that one must manage has increased to unfathomable levels while the cost of data storage has steadily decreased. Terabytes of information regarding user interactions, business transactions, social media activity, and sensor data from autos and mobile phones are collected by private companies and academic organizations. Making sense of this deluge of data is the problem of our time.
Data analytics primarily entails gathering data from various sources, processing it so that analysts can use it, and then producing products that are beneficial to the organization's operations.
We will cover the most cutting-edge theories and practices of data analytics in this online course.
Who this course is for:
Data Analyst
Developers curious about Data Analytics
Those who are practicing Machine Learning, and Data Science
Goals
What will you learn in this course:
Learn fundamentals of data analysis
Understand the basics of R programming
Learn how to use R programming for data analysis
Develop robust data analysis skills
See how real-world data sets work
Prerequisites
What are the prerequisites for this course?
Basic knowledge of statistics
Basic programming knowledge is a plus
Curriculum
Check out the detailed breakdown of what’s inside the course
DATA ANALYTICS using R Programming
82 Lectures
-
Introduction to Data Analytics and 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 fn & 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
-
Database management systems
50:22
50:22
-
Structured Query Language
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 DataSet
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 & Cross Table
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
-
MatPlot, ECDF & BoxPlot 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
-
K-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
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