Learn Data Science & Machine Learning With R From A-Z

Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!

What you'll learn

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant

How to write complex R programs for practical industry scenarios

Learn data cleaning, processing, wrangling and manipulation

Learn Plotting in R (graphs, charts, plots, histograms etc)

How to create resume and land your first job as a Data Scientist

Step by step practical knowledge of R programming language

Learn Machine Learning and it's various practical applications

Building web apps and online, interactive dashboards with R Shiny

Learn Data and File Management in R

Use R to clean, analyze, and visualize data

Learn the Tidyverse

Learn Operators, Vectors, Lists and their application

Data visualization (ggplot2)

Data extraction and web scraping

Full-stack data science development

Building custom data solutions

Automating dynamic report generation

Data science for business

Requirements

Basic computer skills

Description

Welcome to the Learn Data Science and Machine Learning with R from A-Z Course!In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills.The course covers 6 main areas:1: DS + ML COURSE + R INTROThis intro section gives you a full introduction to the R programming language, data science industry and marketplace, job opportunities and salaries, and the various data science job roles.Intro to Data Science + Machine LearningData Science Industry and MarketplaceData Science Job OpportunitiesR IntroductionGetting Started with R2: DATA TYPES/STRUCTURES IN RThis section gives you a full introduction to the data types and structures in R with hands-on step by step training.VectorsMatricesListsData FramesOperatorsLoopsFunctionsDatabases + more!3: DATA MANIPULATION IN RThis section gives you a full introduction to the Data Manipulation in R with hands-on step by step training.Tidy DataPipe Operatordplyr verbs: Filter, Select, Mutate, Arrange + more!String ManipulationWeb Scraping4: DATA VISUALIZATION IN RThis section gives you a full introduction to the Data Visualization in R with hands-on step by step training.Aesthetics MappingsSingle Variable PlotsTwo-Variable PlotsFacets, Layering, and Coordinate System5: MACHINE LEARNINGThis section gives you a full introduction to Machine Learning with hands-on step by step training.Intro to Machine LearningData PreprocessingLinear RegressionLogistic RegressionSupport Vector MachinesK-Means ClusteringEnsemble LearningNatural Language ProcessingNeural Nets6: STARTING A DATA SCIENCE CAREERThis section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.Creating a ResumePersonal BrandingFreelancing + Freelance websitesImportance of Having a WebsiteNetworkingBy the end of the course you’ll be a professional Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

Overview

Section 1: Data Science and Machine Learning Course Intro

Lecture 1 Data Science and Machine Learning Intro Section Overview

Lecture 2 What is Data Science?

Lecture 3 Machine Learning Overview

Lecture 4 Data Science + Machine Learning Marketplace

Lecture 5 Who is This Course For?

Lecture 6 Data Science and Machine Learning Job Opportunities

Section 2: Getting Started with R

Lecture 7 Getting Started with R

Lecture 8 R Basics

Lecture 9 Working with Files

Lecture 10 R Studio

Lecture 11 Tidyverse Overview

Lecture 12 Additional Resources

Section 3: Data Types and Structures in R

Lecture 13 Data Types and Structures in R Section Overview

Lecture 14 Basic Types

Lecture 15 Vectors Part One

Lecture 16 Vectors Part Two

Lecture 17 Vectors: Missing Values

Lecture 18 Vectors: Coercion

Lecture 19 Vectors: Naming

Lecture 20 Vectors: Misc.

Lecture 21 Working with Matrices

Lecture 22 Working with Lists

Lecture 23 Introduction to Data Frames

Lecture 24 Creating Data Frames

Lecture 25 Data Frames: Helper Functions

Lecture 26 Data Frames: Tibbles

Section 4: Intermediate R

Lecture 27 Intermedia R Section Introduction

Lecture 28 Relational Operators

Lecture 29 Logical Operators

Lecture 30 Conditional Statements

Lecture 31 Working with Loops

Lecture 32 Working with Functions

Lecture 33 Working with Packages

Lecture 34 Working with Factors

Lecture 35 Dates & Times

Lecture 36 Functional Programming

Lecture 37 Data Import/Export

Lecture 38 Working with Databases

Section 5: Data Manipulation in R

Lecture 39 Data Manipulation Section Intro

Lecture 40 Tidy Data

Lecture 41 The Pipe Operator

Lecture 42 {dplyr}: The Filter Verb

Lecture 43 {dplyr}: The Select Verb

Lecture 44 {dplyr}: The Mutate Verb

Lecture 45 {dplyr}: The Arrange Verb

Lecture 46 {dplyr}: The Summarize Verb

Lecture 47 Data Pivoting: {tidyr}

Lecture 48 String Manipulation: {stringr}

Lecture 49 Web Scraping: {rvest}

Lecture 50 JSON Parsing: {jsonlite}

Section 6: Data Visualization in R

Lecture 51 Data Visualization in R Section Intro

Lecture 52 Getting Started with Data Visualization in R

Lecture 53 Aesthetics Mappings

Lecture 54 Single Variable Plots

Lecture 55 Two Variable Plots

Lecture 56 Facets, Layering, and Coordinate Systems

Lecture 57 Styling and Saving

Section 7: Creating Reports with R Markdown

Lecture 58 Introduction to R Markdown

Section 8: Building Webapps with R Shiny

Lecture 59 Introduction to R Shiny

Lecture 60 Creating A Basic R Shiny App

Lecture 61 Other Examples with R Shiny

Section 9: Introduction to Machine Learning

Lecture 62 Introduction to Machine Learning Part One

Lecture 63 Introduction to Machine Learning Part Two

Section 10: Data Preprocessing

Lecture 64 Data Preprocessing Intro

Lecture 65 Data Preprocessing

Section 11: Linear Regression: A Simple Model

Lecture 66 Linear Regression: A Simple Model Intro

Lecture 67 A Simple Model

Section 12: Exploratory Data Analysis

Lecture 68 Exploratory Data Analysis Intro

Lecture 69 Hands-on Exploratory Data Analysis

Section 13: Linear Regression - A Real Model

Lecture 70 Linear Regression - Real Model Section Intro

Lecture 71 Linear Regression in R - Real Model

Section 14: Logistic Regression

Lecture 72 Introduction to Logistic Regression

Lecture 73 Logistic Regression in R

Section 15: Starting A Career in Data Science

Lecture 74 Starting a Data Science Career Section Overview

Lecture 75 Creating A Data Science Resume

Lecture 76 Getting Started with Freelancing

Lecture 77 Top Freelance Websites

Lecture 78 Personal Branding

Lecture 79 Networking Do's and Don'ts

Lecture 80 Setting Up a Website

Students who want to learn about Data Science and Machine Learning