Data Analytics Using R Programming

Data analytics, R programming

What you'll learn

What is data and its types

Overview of the R programming language.

Installation of R and Rstudio in Ubuntu environment

Basic syntax and data structures

Operators, control and looping statement in R

String handling, vector operator in R

Built-in and user defined function in R

Vectorization in R

Data Structure Data Manipulation, Data Reshaping, Data visualization

Data visualization using base R, ggplot2 and other visualization libraries.

Reading and importing and handling missing data from different source (CSV, Excel, databases).

Different Case studies and practical projects.

Requirements

Having a basic understanding of programming concepts can be beneficial.

A foundational understanding of basic statistical concepts like mean, median, standard deviation, and so on.

Basic mathematical operations used in data analytics.

An awareness of fundamental data concepts, such as types of data, and basic data structures, can be beneficial.

Description

Unlock the power of data with our comprehensive "Data Analytics Using R Programming" course. In this immersive learning experience, participants will delve into the world of data analytics, mastering the R programming language to extract valuable insights from complex datasets. Whether you're a seasoned data professional or a newcomer to the field, this course provides a solid foundation and advanced techniques to elevate your analytical skills.Key Learning Objectives:R Programming Fundamentals:Gain a deep understanding of the R programming language, covering syntax, data structures, and essential functions.Data Import and Cleaning:Learn how to import data from various sources and perform data cleaning and preprocessing to ensure accurate analysis.Exploratory Data Analysis (EDA):Develop skills in descriptive statistics, data summarization, and advanced visualization techniques using ggplot2.Real-World Applications:Apply your newfound knowledge to real-world data analytics challenges, working on hands-on projects that simulate the complexities of professional scenarios.Course Format:This course is delivered through a combination of video lectures, hands-on exercises, and real-world projects. Participants will have access to a supportive online community and regular opportunities for live Q&A sessions.By the end of this course, you will be equipped with the skills to navigate the data analytics landscape confidently, making informed decisions and uncovering hidden patterns in data. Join us on this journey to become a proficient data analyst using the versatile R programming language. Enroll today and harness the power of data!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Prerequisites

Section 2: Data Analytics

Lecture 3 What is Data

Lecture 4 Importance of Data

Lecture 5 Type of Data - Categorical

Lecture 6 Type of Data - Numerical

Lecture 7 Analytics and Analysis

Lecture 8 Data Analytics

Lecture 9 Data Analysis

Lecture 10 Classification of Data Analytics

Lecture 11 Process

Section 3: Intro to R and R studio

Lecture 12 Introduction to R

Lecture 13 Benefits of R

Section 4: R and R studio installation in Ubuntu

Lecture 14 install R in Ubuntu GUI

Lecture 15 install R in Ubuntu terminal

Lecture 16 R studio GUI overview

Lecture 17 How to create and run R file in GUI

Lecture 18 How to save and run R file in Terminal

Lecture 19 Rdata and Rhistory

Section 5: R programming Basics

Lecture 20 Variable in R

Lecture 21 DataTypes in R

Lecture 22 Print vs Cat function in R

Lecture 23 ls,rm function in R

Lecture 24 Rules to create variable in R

Lecture 25 Special keywords in R

Lecture 26 Different datatypes in R

Lecture 27 Vectorization in R

Lecture 28 Implicit Cohesion

Lecture 29 ls function in detail

Section 6: Operators in R

Lecture 30 Operators in R

Lecture 31 Arithmetic Operators

Lecture 32 Relational Operators

Lecture 33 Logical Operators

Lecture 34 Miscellaneous Operators

Lecture 35 R basics summary

Section 7: Control structures in R

Lecture 36 Conditional statement - if, else, else if

Lecture 37 Conditional statement - switch

Lecture 38 Lab exercise

Section 8: Looping Statement in R

Lecture 39 For

Lecture 40 While

Lecture 41 Repeat

Section 9: String Handling in R

Lecture 42 getting user input and explicit cohersion

Lecture 43 getting user input part 2

Lecture 44 logical check for string - grepl and grep

Lecture 45 print vs cat vs paste method

Lecture 46 String methods - toupper, tolower, substr, format

Section 10: Vector operation in R

Lecture 47 Indexing in vector

Lecture 48 Indexing in vector - part 2

Lecture 49 Built-in operation in R

Lecture 50 Repeat operation in R

Lecture 51 Lab exercise

Lecture 52 Lab solution - part 1

Lecture 53 Lab solution - part 2

Section 11: Functions in R

Lecture 54 Intro to Function in R

Lecture 55 Built-in function - seq, seq_along

Lecture 56 Built-in function - seq_len

Lecture 57 Built-in function rnorm

Lecture 58 law of large number

Lecture 59 Built-in function rnorm - part 2

Lecture 60 Built-in function - runif

Lecture 61 Built-in function - sample

Lecture 62 Lab exercise

Lecture 63 Lab solution - part 1

Lecture 64 Lab solution - part 2

Lecture 65 Lab solution - part 3

Section 12: User defined function in R

Lecture 66 User defined function - part 1

Lecture 67 User defined function - part 2

Lecture 68 User defined function - part 3

Lecture 69 User defined function - part 4

Lecture 70 User defined function - part 5

Lecture 71 User defined function - part 6

Lecture 72 User defined function - part 7

Lecture 73 User defined function - part 8

Lecture 74 Lab exercise

Section 13: Vectorization in R

Lecture 75 Vectorized Approach

Lecture 76 Vectorized Function

Section 14: Data Structure in R

Lecture 77 Introduction to Data Structure

Lecture 78 List - Part 1

Lecture 79 List - Part 2

Lecture 80 List summary

Lecture 81 Manipulating List

Lecture 82 Converting List to Vector

Lecture 83 Matrix - Part 1

Lecture 84 Matrix - Part 2

Lecture 85 Matrix - Part 3

Lecture 86 Matrix - Part 4

Lecture 87 Matrix - Part 5

Lecture 88 Lab exercise

Lecture 89 Date - Part 1

Lecture 90 Date - Part 2

Lecture 91 Factor - Part 1

Lecture 92 Factor - Part 2

Lecture 93 Factor - Part 3

Lecture 94 Array - Part 1

Lecture 95 Array - Part 2

Lecture 96 Array - Part 3

Lecture 97 Array - Part 4

Lecture 98 Lab Exercise

Lecture 99 DataFrame - Part 1

Lecture 100 DataFrame - Part 2

Lecture 101 DataFrame - Part 3

Lecture 102 DataFrame - Part 4

Lecture 103 DataFrame - Part 5

Lecture 104 DataFrame - Part 6

Lecture 105 DataFrame - Summary

Lecture 106 Lab exercise

Section 15: Data Manipulation

Lecture 107 Data Manipulation - Part 1

Lecture 108 Data Manipulation - Part 2

Lecture 109 Data Manipulation - Part 3

Lecture 110 Data Manipulation - Part 4

Section 16: R Package

Lecture 111 R Package - Part 1

Lecture 112 R Package - Part 2

Section 17: apply functions in R

Lecture 113 apply function - part 1

Lecture 114 apply function - part 2

Lecture 115 apply function - part 3

Lecture 116 lapply function - part 1

Lecture 117 lapply function - part 2

Lecture 118 sapply function - part 1

Lecture 119 sapply function - part 2

Lecture 120 tapply function

Lecture 121 summary

Section 18: Data Reshaping

Lecture 122 Data Reshaping introduction

Lecture 123 Aggregating - Part 1

Lecture 124 Aggregating - Part 2

Lecture 125 sorting

Lecture 126 mergining - inner join

Lecture 127 types of joins

Lecture 128 left, right and full join

Lecture 129 Lab exercise

Section 19: Data visualization

Lecture 130 Data visualization - part 1

Lecture 131 Data visualization - part 2

Lecture 132 scatter plot using base R

Lecture 133 scatter plot using ggplot - part 1

Lecture 134 scatter plot using ggplot - part 2

Lecture 135 Summary

Lecture 136 Line plot using base R

Lecture 137 Line plot using ggplot - part 1

Lecture 138 Line plot using ggplot - part2

Lecture 139 Histogram using base R

Lecture 140 Histogram uisng ggplot

Lecture 141 Bar plot using base R - part 1

Lecture 142 Bar plot using base R - part 2

Lecture 143 Bar plot using ggplot

Lecture 144 Box plot using Base R

Lecture 145 Box plot using ggplot

Section 20: Working with Excel file

Lecture 146 Introduction to working with excel file

Lecture 147 Data cleaning - part 1

Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics.,IT professionals seeking to expand their skills into the field of data analytics using R.,Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data.