
Discover what data science and machine learning are and how they are used. Learn who this course is for and explore future growth and job opportunities.
Explore two essential resources for data science and machine learning with R: the free online 'Are for data science' and 'An introduction to statistical learning,' ideal for beginners.
Learn how to name vector elements in R using the names function, create named vectors like x with names a, b, c, and use the letters vector to index data.
Discover data frames as a core data structure in R, with columns as vectors, uniform length, and named attributes; explore iris and other built-in datasets and viewing tools in RStudio.
Learn to work with data frames using helper functions—head, tail, dim, str, and names—to inspect structure, dimensions, and column names, and apply the apply family for column-wise analysis.
Learn to manage dates and times in R with the package that simplifies date types, time zones, today and now, and creating dates from strings or numbers.
Explore functional programming with lapply and apply in R to apply a function to each element, replace loops, return lists, and flatten results with unlist.
Explore how to connect to relational databases in R using the dbi package, create in-memory databases, list tables and fields, read and write data, run queries, and manage connections.
Discover the pipe operator in R by loading the Margaretha package, then chain functions like mean, floor, and paste for readable, step-by-step data transformations.
Learn how to use dplyr's filter verb to select rows in tidy data, using a pipe workflow with the starwars dataset to practice conditional filtering by eye color.
Explore parsing JSON data in R using the Jason Light library, converting data frames, lists, and vectors to and from JSON, with options like simplify vector and prettify.
Learn to visualize data in R by creating your first ggplot scatterplot, mapping displacement to x and highway to y using the NPG data set, and layering aesthetics and geometry.
Layer and overlay plots in R using plus signs, add density and rug plots, and facet by class. Explore coord systems, including coord flip, polar coordinates, and map coordinates.
Create a basic R Shiny app with a responsive UI and server. Link a slider input to a renderPlot to generate a histogram from the faithful data.
Explore practical R Shiny examples using the iris dataset, building a reactive web app with select inputs, dynamic plots and tables, and learning iterative, lightweight UI design.
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.