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

Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!

Created by : Rated :4.1 8044 views

Last Updated: 2022-07-22 11:07:37 English Course Type: Self Learning

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

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.

Basic computer skills

Students who want to learn about Data Science and Machine Learning

Section 1 : DS and ML from A-Z Course Intro 7 Lectures 00:31:58
Lecture 1 :
Data Science ML Course Intro Preview
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 6 Lectures 00:44:49
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 14 Lectures 05:01:48
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 12 Lectures 04:14:03
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 13 Lectures 05:39:51
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 7 Lectures 02:24:25
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 1 Lectures 00:28:54
Lecture 1 :
Creating-Reports-with-R-Markdown
Section 8 : Building Webapps with R Shiny 3 Lectures 01:31:28
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 2 Lectures 01:08:33
Lecture 1 :
Intro to Machine Learning - Part 1
Lecture 2 :
Intro to Machine Learning - Part 2
Section 10 : Starting A Career in Data Science 7 Lectures 00:29:37
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

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Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic.
Can i play & pause the course as per my convenience?
Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.
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Most of these courses have general questions & answers already covered within the course lectures. However, if you need any further help from the instructor, you can use the inbuilt Chat with Instructor option to send a message to an instructor & they will reply you within 24 hours. You can ask as many questions as you want.
Do i need a pc to access the course or can i do it on mobile & tablet as well?
Brilliant question? Isn't it? You can access the courses on any device like PC, Mobile, Tablet & even on a smart tv. For mobile & a tablet you can download the Learnfly android or an iOS app. If mobile app is not available in your country, you can access the course directly by visting our website, its fully mobile friendly.
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