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Data Analytics using R Programming

person icon DATAhill Solutions Srinivas Reddy

4.6

Data Analytics using R Programming

Master the fundamentals of data analysis and R programming with this comprehensive online course

updated on icon Updated on Apr, 2024

language icon Language - English

person icon DATAhill Solutions Srinivas Reddy

category icon Development,Data Science and AI ML,Data Analysis

Lectures -83

Resources -82

Duration -68.5 hours

4.6

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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

Data Analytics using R Programming

Curriculum

Check out the detailed breakdown of what’s inside the course

DATA ANALYTICS using R Programming
82 Lectures
  • play icon Introduction to Data Analytics and R Programming 20:05 20:05
  • play icon R Installation & Setting R Environment 50:16 50:16
  • play icon Variables, Operators & Data types 53:10 53:10
  • play icon Structures 47:08 47:08
  • play icon Vectors 01:04:04 01:04:04
  • play icon Vector Manipulation & Sub Setting 01:06:03 01:06:03
  • play icon Constants 41:38 41:38
  • play icon RStudio Installation & Lists Part 1 01:02:20 01:02:20
  • play icon Lists Part 2 47:44 47:44
  • play icon List Manipulation, Sub Setting & Merging 45:01 45:01
  • play icon List to Vector & Matrix Part 1 49:52 49:52
  • play icon Matrix Part 2 44:02 44:02
  • play icon Matrix Accessing 48:26 48:26
  • play icon Matrix Manipulation, rep fn & Data Frame 56:08 56:08
  • play icon Data Frame Accessing 54:01 54:01
  • play icon Column Bind & Row Bind 50:32 50:32
  • play icon Merging Data Frames Part 1 50:04 50:04
  • play icon Merging Data Frames Part 2 54:26 54:26
  • play icon Melting & Casting 52:55 52:55
  • play icon Arrays 43:50 43:50
  • play icon Factors 50:53 50:53
  • play icon Functions & Control Flow Statements 40:27 40:27
  • play icon Strings & String Manipulation with Base Package 53:22 53:22
  • play icon String Manipulation with Stringi Package Part 1 58:33 58:33
  • play icon String Manipulation with Stringi Package Part 2 & Date and Time Part 1 48:13 48:13
  • play icon Date and Time Part 2 53:19 53:19
  • play icon Data Extraction from CSV File 42:02 42:02
  • play icon Data Extraction from EXCEL File 50:40 50:40
  • play icon Data Extraction from CLIPBOARD, URL, XML & JSON Files 50:04 50:04
  • play icon Database management systems 50:22 50:22
  • play icon Structured Query Language 41:35 41:35
  • play icon Data Definition Language Commands 01:02:24 01:02:24
  • play icon Data Manipulation Language Commands 47:29 47:29
  • play icon Sub Queries & Constraints 16:07 16:07
  • play icon Aggregate Functions, Clauses & Views 07:21 07:21
  • play icon Data Extraction from Databases Part 1 52:31 52:31
  • play icon Data Extraction from Databases Part 2 & DPlyr Package Part 1 52:39 52:39
  • play icon DPlyr Package Part 2 51:36 51:36
  • play icon DPlyr Functions on Air Quality DataSet 57:01 57:01
  • play icon Plyr Package for Data Analysis 46:51 46:51
  • play icon Tidyr Package with Functions 50:48 50:48
  • play icon Factor Analysis 57:11 57:11
  • play icon Prob.Table & Cross Table 50:22 50:22
  • play icon Statistical Observations Part 1 51:48 51:48
  • play icon Statistical Observations Part 2 40:35 40:35
  • play icon Statistical Analysis on Credit Data set 01:00:29 01:00:29
  • play icon Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts 59:20 59:20
  • play icon Box Plots 54:38 54:38
  • play icon Histograms & Line Graphs 45:26 45:26
  • play icon Scatter Plots & Scatter plot Matrices 01:03:47 01:03:47
  • play icon Low Level Plotting 56:01 56:01
  • play icon Bar Plot & Density Plot 46:31 46:31
  • play icon Combining Plots 35:37 35:37
  • play icon Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot 51:07 51:07
  • play icon MatPlot, ECDF & BoxPlot with IRIS Data set 01:02:55 01:02:55
  • play icon Additional Box Plot Style Parameters 01:01:41 01:01:41
  • play icon Set.Seed Function & Preparing Data for Plotting 01:09:42 01:09:42
  • play icon QPlot, ViolinPlot, Statistical Methods & Correlation Analysis 59:26 59:26
  • play icon ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal 54:42 54:42
  • play icon Data Exploration and Visualization 51:00 51:00
  • play icon Machine Learning, Types of ML with Algorithms 01:04:53 01:04:53
  • play icon How Machine Solve Real Time Problems 43:33 43:33
  • play icon K-Nearest Neighbor(KNN) Classification 01:07:45 01:07:45
  • play icon KNN Classification with Cancer Data set Part 1 01:03:15 01:03:15
  • play icon KNN Classification with Cancer Data set Part 2 43:12 43:12
  • play icon Navie Bayes Classification 43:53 43:53
  • play icon Navie Bayes Classification with SMS Spam Data set & Text Mining 58:43 58:43
  • play icon WordCloud & Document Term Matrix 56:39 56:39
  • play icon Train & Evaluate a Model using Navie Bayes 01:11:40 01:11:40
  • play icon MarkDown using Knitr Package 01:02:15 01:02:15
  • play icon Decision Trees 57:16 57:16
  • play icon Decision Trees with Credit Data set Part 1 47:03 47:03
  • play icon Decision Trees with Credit Data set Part 2 45:11 45:11
  • play icon Support Vector Machine, Neural Networks & Random Forest 46:50 46:50
  • play icon Regression & Linear Regression 44:04 44:04
  • play icon Multiple Regression 48:24 48:24
  • play icon Generalized Linear Regression, Non Linear Regression & Logistic Regression 35:37 35:37
  • play icon Clustering 29:04 29:04
  • play icon K-Means Clustering with SNS Data Analysis 01:06:18 01:06:18
  • play icon Association Rules (Market Basket Analysis) 39:33 39:33
  • play icon Market Basket Analysis using Association Rules with Groceries Data set 56:19 56:19
  • play icon Python Libraries for Data Science 22:32 22:32

Instructor Details

DATAhill Solutions Srinivas Reddy

DATAhill Solutions Srinivas Reddy

Data Scientist

Mr. 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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