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This plan includes
- Access to 11,000+ Courses
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- Play & Pause Course Videos
HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Direct Support
- Email & Chat Support
- Cancel Anytime
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
Course Overview
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.
Pre-requisites
- Basic computer skills
Target Audience
- Students who want to learn about Data Science and Machine Learning
Curriculum 72 Lectures 22:15:26
-
Section 1 : DS and ML from A-Z Course Intro
- Lecture 2 :
- What is data science
- Lecture 3 :
- Machine Learning Overview1
- Lecture 4 :
- Whos this course is for1
- Lecture 5 :
- DL and ML Marketplace1
- Lecture 6 :
- Data Science and ML Job opps
- Lecture 7 :
- Data Science Job Roles1
-
Section 2 : Getting Started with R
- Lecture 1 :
- Getting Started
- Lecture 2 :
- Basics
- Lecture 3 :
- Files
- Lecture 4 :
- RStudio
- Lecture 5 :
- Tidyverse
- Lecture 6 :
- Resources
-
Section 3 : Data Types and Structures in R
- Lecture 1 :
- Section Introduction
- Lecture 2 :
- Basic Types
- Lecture 3 :
- Vectors Part One
- Lecture 4 :
- Vectors Part Two
- Lecture 5 :
- Vectors - Missing Values
- Lecture 6 :
- Vectors - Coercion
- Lecture 7 :
- Vectors - Naming
- Lecture 8 :
- 1.8 Vectors - Misc
- Lecture 9 :
- 1.9 Creating Matrices
- Lecture 10 :
- 1.10 Lists
- Lecture 11 :
- Introduction to Data Frames
- Lecture 12 :
- Creating Data Frames
- Lecture 13 :
- Data Frames_Helper Functions
- Lecture 14 :
- Data Frames - Tibbles
-
Section 4 : Intermediate R
- Lecture 1 :
- 1.1 Section Introduction Intermediate R
- Lecture 2 :
- 1.2 Relational Operations
- Lecture 3 :
- 1.3 Logical Operators
- Lecture 4 :
- 1.4 Conditoinal Statements
- Lecture 5 :
- 1.5 Loops
- Lecture 6 :
- 1.6 Functions
- Lecture 7 :
- 1.7 Packages
- Lecture 8 :
- 1.8 Factors
- Lecture 9 :
- 1.9 Dates and Times
- Lecture 10 :
- 1.10 Functional Programming
- Lecture 11 :
- 1.11 Data Import or Export
- Lecture 12 :
- 1.12 Database
-
Section 5 : Data Manipulation in R
- Lecture 1 :
- 1.1 Data Manipulation in R Section Introduction
- Lecture 2 :
- 1.2 Tidy Data
- Lecture 3 :
- 1.3 The Pipe Operator
- Lecture 4 :
- 1.4 The Filter Verb
- Lecture 5 :
- 1.5 The Select Verb
- Lecture 6 :
- 1.6 The Mutate Verb
- Lecture 7 :
- 1.7 The Arrange Verb
- Lecture 8 :
- 1.8 The Summarize Verb
- Lecture 9 :
- 1.9 Data Pivoting
- Lecture 10 :
- JSON Parsing
- Lecture 11 :
- String Manipulation
- Lecture 12 :
- Web Scraping 1
- Lecture 13 :
- Web Scraping 2
-
Section 6 : Data Visualization in R
- Lecture 1 :
- 1.1 Data Visualization in R Section Introduction
- Lecture 2 :
- 1.2 Getting Started
- Lecture 3 :
- 1.3 Aesthetics Mappings
- Lecture 4 :
- 1.4 Single Variables Plot
- Lecture 5 :
- 1.5 Two Varible Plots
- Lecture 6 :
- 1.6 Facets Layering and Coordinate System
- Lecture 7 :
- 1.7 Styling and Saving
-
Section 7 : Creating Reports with R Markdown
- Lecture 1 :
- Creating-Reports-with-R-Markdown
-
Section 8 : Building Webapps with R Shiny
- Lecture 1 :
- 1.1 Section-Introduction-With-R-Shiny
- Lecture 2 :
- 1.2 A Basic App
- Lecture 3 :
- 1.3 Other Examples
-
Section 9 : Introduction to Machine Learning
- Lecture 1 :
- Intro to Machine Learning - Part 1
- Lecture 2 :
- Intro to Machine Learning - Part 2
-
Section 10 : Starting A Career in Data Science
- Lecture 1 :
- Starting a Data Science Career Section Overview
- Lecture 2 :
- Creating A Data Science Resume
- Lecture 3 :
- Getting Started with Freelancing
- Lecture 4 :
- Top Freelance Websites
- Lecture 5 :
- Personal Branding
- Lecture 6 :
- Networking Do's and Don'ts
- Lecture 7 :
- Setting Up a Website
Our learners work at
Frequently Asked Questions
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