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

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, and 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, and 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, the 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 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 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.

Who is this course for?

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