R Programming for Data Science

R Programming for Data Science

What you’ll learn

Fundamentals of R Programming
Work with RStudio
Use Vectors, Matrices, Lists, Data Frames
Importing and Handling Large CSV files Data in R
Import packages in R & use dplyr Package for Data Wrangling
Create Data Visualization in R
Using R for Basic Statistical Data Analysis

Requirements

No prior knowledge or technical backgrounds is required

Description

Welcome to this course of R Programming for Beginners with the hands-on tutorial, and become an R Professional which is one of the most favoured skills, that employer’s need.Whether you are new to programming or have never programmed before in R Language, this course is for you! This course covers the R Programming from scratch. This course is self-paced. There is no need to rush – you learn on your own schedule.  R programming language iѕ one of the best open-source programming language and more powerful than other programming languages. It iѕ well documented and has a clean syntax and quite еаѕу tо lеаrn. This course will help anyone who wants to start a саrееr in Data Science and Machine Lеаrning. You need to have basic undеrѕtаnding оf R Programming to become a Data Scientist or Data Analyst. This course begins with the introduction to R course that will help you write R code in no time. Then we help you with the installation of R and RStudio on your computer and setting up the programming environment. This course will provide you with everything you need to know about the basics of R Programming. In this course we will cover the following topics:Basics of R Programming including OperatorsFundamentals of R ProgrammingVectors, Matrices, ListsData FramesImporting Data in Data Frames using Text and CSV filesData Wrangling using dplyr packageData VisualizationThis course teaches R Programming in a practical manner with hands-on experience with coding screen-cast.  Once you complete this course, you will be able to create or develop R Programs to solve any complex problems with ease.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is R ?

Lecture 3 Why Learn R ?

Lecture 4 Features of R Language

Lecture 5 Importance of R in Data Science

Lecture 6 Advantages of using R

Lecture 7 Applications of R Programming

Lecture 8 Career Opportunities and Job Roles

Section 2: Getting Started with R

Lecture 9 Installing R Software

Lecture 10 Installing RStudio

Lecture 11 Look around RStudio Interface

Lecture 12 Help & Examples Facility for R Features and Functions

Lecture 13 Changing Look and Feel of RStudio (Optional)

Lecture 14 Some General Functions Good to Know

Lecture 15 Writing R Program using RGui

Lecture 16 Writing R Program using RStudio

Lecture 17 Using Comments in R Scripts

Section 3: R Basics

Lecture 18 Using R for Arithmetics

Lecture 19 Using Mathematical Functions

Lecture 20 Variables

Lecture 21 Keywords or Reserved Words

Lecture 22 Simple Program to Compute Interest

Lecture 23 Variable Assignments

Lecture 24 Displaying Output

Lecture 25 Reading Input

Section 4: Atomic Data Types

Lecture 26 Statically and Dynamically Typed Languages

Lecture 27 Atomic Data Types

Lecture 28 Numeric Type

Lecture 29 Integer Type

Lecture 30 Complex Type

Lecture 31 Logical Type

Lecture 32 Character Type

Lecture 33 Type Conversions

Lecture 34 Conversion to Numeric Type

Lecture 35 Conversion to Integer Type

Lecture 36 Conversion to Complex Type

Lecture 37 Conversion to Logical Type

Lecture 38 Conversion to Character Type

Section 5: Operators

Lecture 39 Relational Operators

Lecture 40 Logical Operators

Section 6: Vectors

Lecture 41 Creating Vectors

Lecture 42 Subsetting Vectors

Lecture 43 Matching Operator

Lecture 44 Vector Arithmetic

Lecture 45 Vector Methods & Operations

Lecture 46 Implicit & Explicit Coercion

Lecture 47 Logical Vectors

Lecture 48 Mathematical Functions

Lecture 49 Generating Random Numbers

Lecture 50 Sequences

Lecture 51 Replicate

Section 7: Matrices

Lecture 52 Creating Matrix

Lecture 53 Using diag() Function

Lecture 54 Naming Rows and Columns of Matrix

Lecture 55 Subsetting Matrix

Lecture 56 Martix rbind() and cbind()

Lecture 57 Matrix Operations

Lecture 58 Matrix Specific Function

Section 8: Lists

Lecture 59 Creating Lists

Lecture 60 Subsetting or Slicing List

Lecture 61 Naming List & Subset Operator

Lecture 62 Lists Concatenation

Section 9: Factors

Lecture 63 Factors

Section 10: Data Frames

Lecture 64 What are Data Frames?

Lecture 65 Creating Data Frames

Lecture 66 Subseting Data Frame

Lecture 67 Data Frame subset() function

Lecture 68 Data Frame rbind() and cbind() functions

Lecture 69 Data Frame edit() function

Lecture 70 Missing Data in Data Frames

Section 11: Control Structures

Lecture 71 Control Structures

Lecture 72 if, if-else and else-if statements

Lecture 73 ifelse() function

Lecture 74 for Loop

Lecture 75 while Loop

Lecture 76 repeat Loop

Lecture 77 break & next statement

Section 12: Functions

Lecture 78 Functions

Lecture 79 Default and Named Arguments

Lecture 80 Lazy Evaluation

Lecture 81 Functions Returning Multiple Values

Lecture 82 Inline Functions

Section 13: Importing Data in Data Frame

Lecture 83 Import Data from Text Files

Lecture 84 Import Data from CSV Files

Lecture 85 Import Data from RDS Files

Lecture 86 Import Data from Internet

Lecture 87 Import Data from Clipboard

Lecture 88 Exporting Data to CSV Files

Section 14: Data Handling using dplyr Package

Lecture 89 Installing dplyr Package

Lecture 90 dplyr select() – Select Columns of Data Frame

Lecture 91 dplyr filter() – Extract Rows from Data Frame

Lecture 92 dplyr arrange() – Sort or Reorder rows of Data Frame

Lecture 93 dplyr rename() – Renaming Columns of Data Frame

Lecture 94 dplyr mutate() – Mutate Data Frames

Lecture 95 dplyrgroup_by() – Generate Summary Statistics

Lecture 96 dplyr %>% – Pipeline Operator

Section 15: Data Visualization

Lecture 97 Bar Plots

Lecture 98 Horizontal Bar Plots

Lecture 99 Histograms

Lecture 100 Scatter Plots

Lecture 101 Line Plots

Lecture 102 Box Plots

Lecture 103 Stacked Bar Plots

Lecture 104 Multiple Plots in a Layout

Section 16: Statistical Data Analysis

Lecture 105 Exploring Stock Prices Datasets

Lecture 106 Find Highest and Lowest Stock Price and Dates

Lecture 107 Graphically Analyzing Stock Prices

Lecture 108 Analyzing Skewness of Stock Prices – Mean, Median and Standard Deviation

Lecture 109 Graphically Comparing Stock Prices in same Layout

Lecture 110 How to Get Certificate of Completion

Beginner who wants to learn R Programming

Course Information:

Udemy | English | 7h 45m | 2.06 GB
Created by: Syed Mohiuddin

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