Data Science & Machine Learning with R from A-Z Course

• Learn how to 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.

• Data Science and Machine Learning Course Intro 32:01

  • 1: 1.1 Data Science and Machine Learning Intro Section Overview Preview 02:31
  • 2: 1.2 What is data science? Preview 09:48
  • 3: 1.3 Machine Learning Overview Preview 05:26
  • 4: 1.4 Who's this course is for? 02:57
  • 5: 1.5 Data Science and Machine Learning Marketplace Preview 04:38
  • 6: 1.6 Data Science and Machine Learning Job Opportunities 02:37
  • 7: 1.7 Data Science Job Roles 04:04

• Getting Started with R 44:52

  • 8: 1.1 Getting Started Preview 10:58
  • 9: 1.2 Basics 06:25
  • 10: 1.3 Working with Files 11:08
  • 11: 1.4 R Studio 06:59
  • 12: 1.5 Tidyverse Overview 05:19
  • 13: 1.6 Resources 04:03

• Data Types and Structures in R 5:01:57

  • 14: 1.1 Section Introduction 30:03
  • 15: 1.2 Basic Types 08:47
  • 16: 1.3 Vectors Part One 19:41
  • 17: 1.4 Vectors Part Two 24:52
  • 18: 1.5 Vectors - Missing Values 15:36
  • 19: 1.6 Vectors - Coercion 14:07
  • 20: 1.7 Vectors - Naming 10:16
  • 21: 1.8 Vectors - Misc 06:00
  • 22: 1.9 Working with Matrices 31:28
  • 23: 1.10 Working with Lists 31:42
  • 24: 1.11 Introduction to Data Frames 19:20
  • 25: 1.12 Creating Data Frames 19:50
  • 26: 1.13 Data Frames: Helper Functions 31:12
  • 27: 1.14 Data Frames: Tibbles 39:03

• Intermediate R 4:14:11

  • 28: 1.1 Section Introduction Intermediate R 46:31
  • 29: 1.2 Relational Operations 11:07
  • 30: 1.3 Logical Operators 07:05
  • 31: 1.4 Conditoinal Statements 11:20
  • 32: 1.5 Loops 07:57
  • 33: 1.6 Functions 14:20
  • 34: 1.7 Packages 11:29
  • 35: 1.8 Factors 28:14
  • 36: 1.9 Dates and Times 30:11
  • 37: 1.10 Functional Programming 36:41
  • 38: 1.11 Data Import or Export 22:07
  • 39: 1.12 Database 27:09

• Data Manipulation in R 5:39:59

  • 40: 1.1 Data Manipulation in R Section Introduction 36:29
  • 41: 1.2 Tidy Data 10:54
  • 42: 1.3 The Pipe Operator 14:50
  • 43: 1.4 The Filter Verb 21:35
  • 44: 1.5 The Select Verb 46:04
  • 45: 1.6 The Mutate Verb 31:57
  • 46: 1.7 The Arrange Verb 10:04
  • 47: 1.8 The Summarize Verb 23:06
  • 48: 1.9 Data Pivoting 42:42
  • 49: 1.10 JSON Parsing 10:46
  • 50: 1.11 String Manipulation 32:39
  • 51: 1.12 Web Scraping 58:53

• Data Visualization in R 2:24:30

  • 52: 1.1 Data Visualization in R Section Introduction 17:13
  • 53: 1.2 Getting Started 15:38
  • 54: 1.3 Aesthetics Mappings 24:45
  • 55: 1.4 Single Variables Plot 36:50
  • 56: 1.5 Two Varible Plots 20:34
  • 57: 1.6 Facets Layering and Coordinate System 17:56
  • 58: 1.7 Styling and Saving 11:34

• Creating Reports with R Markdown 28:54

  • 59: Creating Reports with R Markdown 28:54

• Building Webapps with R Shiny 1:31:28

  • 60: 1.1 Introduction to R Shiny 26:05
  • 61: 1.2 A Basic App 31:18
  • 62: 1.3 Other Examples 34:05

• Introduction to Machine Learning 1:08:35

  • 63: Intro to Machine Learning - Part 1 21:49
  • 64: Intro to Machine Learning - Part 2 46:46

• Data Preprocessing 1:04:51

  • 65: Introduction to Data Preprocessing 27:04
  • 66: Data Preprocessing 37:47

• Linear Regression: A Simple Model 1:18:14

  • 67: Section Introduction 25:09
  • 68: Linear Regression: A Simple Model 53:05

• Exploratory Data Analysis 1:28:00

  • 69: Section Introduction 25:03
  • 70: Hands-on Exploratory Data Analysis 1:02:57

• Linear Regression: A Real Model 1:24:52

  • 71: Linear Regression - Real Model Section Intro 32:04
  • 72: Linear Regression in R - Real model 52:48

• Logistic Regression 1:17:26

  • 73: Introduction to Logistic Regression 37:48
  • 74: Logistic Regression in R 39:38

• Starting a Career in Data Science 29:42

  • 75: 1.1 Starting a Career in Data Science 02:54
  • 76: 1.2 Data Science Resume 03:43
  • 77: 1.3 Getting Started with Freelancing 04:44
  • 78: 1.4 Top Freelancing Websites 05:19
  • 79: 1.5 Personal Branding 05:28
  • 80: 1.6 Importance of Website and Blog 03:43
  • 81: 1.7 Networking dos and donts 03:51

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 INTRO

This 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 Learning

• Data Science Industry and Marketplace

• Data Science Job Opportunities

• R Introduction

• Getting Started with R

2: DATA TYPES/STRUCTURES IN R

This section gives you a full introduction to the data types and structures in R with hands-on step by step training.

• Vectors

• Matrices

• Lists

• Data Frames

• Operators

• Loops

• Functions

• Databases + more!
  
  

3: DATA MANIPULATION IN R

This section gives you a full introduction to the Data Manipulation in R with hands-on step by step training.

• Tidy Data

• Pipe Operator

• dplyr verbs: Filter, Select, Mutate, Arrange + more!

• String Manipulation

• Web Scraping
  
  

4: DATA VISUALIZATION IN R

This section gives you a full introduction to the Data Visualization in R with hands-on step by step training.

• Aesthetics Mappings

• Single Variable Plots

• Two-Variable Plots

• Facets, Layering, and Coordinate System
  
  

5: MACHINE LEARNING

This section gives you a full introduction to Machine Learning with hands-on step by step training.

• Intro to Machine Learning

• Data Preprocessing

• Linear Regression

• Logistic Regression

• Support Vector Machines

• K-Means Clustering

• Ensemble Learning

• Natural Language Processing

• Neural Nets

6: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

• Creating a Resume

• Personal Branding

• Freelancing + Freelance websites

• Importance of Having a Website

• Networking

By 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.

• Students who want to learn about Data Science and Machine Learning

• Basic computer skills

Reed courses certificate of completion

Digital certificate - Included