Mastering Data Science With R: Complete Diploma Program

Acquire Essential R Skills and Achieve Professional Certification with Our Comprehensive Bootcamp

What you'll learn

Professional R Tools and Environment: A Comprehensive Guide

Understanding R Syntax: How to Explain and Document Code with Comments

Understanding Variables, Values, and Assignments: Core Concepts Explained

Comprehensive Guide to All Data Types in R Programming

Executing Mathematical Operations, Type Conversions, and Utilizing Built-In Functions for Enhanced Math Calculations

Working with Character Collections and Strings in R: Essential Character Operations

Understanding Logical Values and Booleans in Programming

Utilizing Various Operators for Efficient Handling of Operations on Variables and Values

Comprehensive Guide to Data Structures in R: Vectors, Lists, Matrices, Data Frames, and Factors, Including Essential Operations

Decision-Making with Conditional Statements in R Programming

Efficient Code Reuse and Iteration: Mastering Loops for Collection Processing

Advanced Functional Programming and Code Reusability in R

Comprehensive Statistics and Data Analysis: Exploring Datasets, Utilizing Built-In Functions, and Applying Techniques and Tools for Statistical Operations

Data Visualization and Graphics in R: Techniques for Points, Line Plots, Pie Charts, Bar Charts, Histograms, and More

Instructor Q&A Support

Requirements

No prior programming experience required. This course will cover all the essential knowledge you need to succeed

Description

Welcome to the Exciting World of R Programming!Data Science: R Programming Complete DiplomaR is a powerful programming language renowned for its capabilities in statistical computing and graphical presentation. It excels in analyzing and visualizing data, making it an invaluable tool for data scientists.In this comprehensive course, you'll journey from the basics of R to advanced concepts, mastering coding techniques and exploring how R can be utilized for effective data analysis and stunning visualizations.Every topic and tool in this course is explained in detail, both theoretically and practically, with step-by-step real-world examples.What You'll Learn:R Tools and Environment: Set up your professional R working environment.R Syntax: Understand and document code with comments.Variables and Data Types: Work with variables, values, and various data types.Mathematical Operations: Perform math operations, type conversions, and use built-in functions.Character Operations: Handle and manipulate characters and strings.Logical Values and Booleans: Use logical operations and Boolean values.Operators: Apply different types of operators for variable and value manipulation.Data Structures: Master vectors, lists, matrices, data frames, and factors.Conditional Statements: Make decisions with conditional statements.Loops: Repeat code and iterate over collections.Functional Programming: Learn about code reuse and functional programming concepts.Statistics and Data Analysis: Utilize built-in functions and techniques for statistical operations.Graphics and Visualization: Create points, line plots, pie charts, bar charts, histograms, and more.R is an open-source language, widely used by statisticians and data scientists, particularly in fields like bioinformatics. Its dynamic typing, extensive built-in functions, and customizable libraries make it a versatile tool for interactive programming.Don’t miss this opportunity to become a professional R programmer and data scientist. Enroll now and embark on a comprehensive bootcamp to master one of the most powerful programming languages in the industry!Let's get started!

Overview

Section 1: 1 Introductions

Lecture 1 Introduction

Lecture 2 2 Downloading and Installing R

Lecture 3 3 Downloading and Installing the RStudio IDE

Lecture 4 4 Setup working directory

Section 2: 2 Variable, Data Types and Hints

Lecture 5 5 Variables in detail 1

Lecture 6 6 Variables in detail 2

Lecture 7 7 Variables in detail 3

Lecture 8 8 Variables in detail 4

Lecture 9 9 Data types 1

Lecture 10 10 Data types 2

Lecture 11 11 Data types 3

Lecture 12 12 Code hints

Section 3: 3 Characters

Lecture 13 13 Type of numbers

Lecture 14 14 Type conversion

Lecture 15 15 Math operations

Section 4: 4 Numbers and Math

Lecture 16 16 Strings 1

Lecture 17 17 Strings 2

Lecture 18 18 Strings 3

Lecture 19 19 Strings 4

Lecture 20 20 Strings 5

Lecture 21 21 Strings 6

Lecture 22 22 Strings 7

Section 5: 5 Logical and Operators

Lecture 23 23 Logical values 1

Lecture 24 24 Logical values 2

Lecture 25 25 Operators 1

Lecture 26 26 Operators 2

Lecture 27 27 Operators 3

Lecture 28 28 Operators 4

Lecture 29 29 Operators 5

Section 6: 6 DS Vectors

Lecture 30 30 DS -Vectors 1

Lecture 31 31 DS -Vectors 2

Lecture 32 32 DS -Vectors 3

Lecture 33 33 DS -Vectors 4

Lecture 34 34 DS -Vectors 5

Lecture 35 35 DS -Vectors 6

Lecture 36 36 DS -Vectors 7

Section 7: 7 DS Lists

Lecture 37 37 DS - Lists 1

Lecture 38 38 DS - Lists 2

Lecture 39 39 DS - Lists 3

Lecture 40 40 DS - Lists 4

Section 8: 8 Matrices

Lecture 41 41 DS - Matrices 1

Lecture 42 42 DS - Matrices 2

Lecture 43 43 DS - Matrices 3

Lecture 44 44 DS - Matrices 4

Lecture 45 45 DS - Matrices 5

Section 9: 9 Arrays

Lecture 46 46 DS - Arrays 1

Lecture 47 47 DS - Arrays 2

Lecture 48 48 DS - Arrays 3

Lecture 49 49 DS - Arrays 4

Lecture 50 50 DS - Arrays 5

Section 10: 10 Data Frame

Lecture 51 51 DS - Data Frame 1

Lecture 52 52 DS - Data Frame 2

Lecture 53 53 DS - Data Frame 3

Lecture 54 54 DS - Data Frame 4

Lecture 55 55 DS - Data Frame 5

Lecture 56 56 DS - Data Frame 6

Section 11: 11 Factors

Lecture 57 57 DS - Factors 1

Lecture 58 58 DS - Factors 2

Lecture 59 59 DS - Factors 3

Lecture 60 60 DS - Factors 4

Section 12: 12 Decision Making

Lecture 61 61 Conditional statements 1

Lecture 62 62 Conditional statements 2

Lecture 63 63 Conditional statements 3

Lecture 64 64 Conditional statements 4

Section 13: 13 Repetition

Lecture 65 65 While loop 1

Lecture 66 66 While loop 2

Lecture 67 67 While loop 3

Lecture 68 68 For loop 1

Lecture 69 69 For loop 2

Lecture 70 70 For loop 3

Lecture 71 71 For loop 4

Section 14: 14 Functional Programming

Lecture 72 72 Functions 1

Lecture 73 73 Functions 2

Lecture 74 74 Functions 3

Lecture 75 75 Functions 4

Lecture 76 76 Functions 5

Section 15: 15 Statistics And Data Analysis

Lecture 77 77 Statistics 1

Lecture 78 78 Statistics 2

Lecture 79 79 Statistics 3

Lecture 80 80 Statistics 4

Lecture 81 81 Statistics 5

Section 16: 16 Data Visualization and Graphics

Lecture 82 82 Plotting in R 1

Lecture 83 83 Plotting in R 2

Lecture 84 84 Plotting in R 3

Lecture 85 85 Plotting in R 4

Lecture 86 86 Lines 1

Lecture 87 87 Lines 2

Lecture 88 88 Lines 3

Lecture 89 89 Pie Charts 1

Lecture 90 90 Pie Charts 2

Lecture 91 91 Pie Charts 3

Lecture 92 92 Bars and Histograms 1

Lecture 93 93 Bars and Histograms 2

Lecture 94 94 Bars and Histograms 3

Lecture 95 95 Bars and Histograms 4

Lecture 96 96 Bars and Histograms 5

Beginner R Programmers,New Developers and Engineers,Individuals New to Programming and Software Development,Developers and Engineers Experienced in Other Programming Languages but New to R,Professionals Curious About Learning R for Data Science,Aspiring Data Engineers and Data Scientists