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

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
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