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Programming and data science

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

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

4.3
(332 ratings) 1137 students



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What you will 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

Who should take this training

Prerequisites

  • Basic computer skills

Target audience

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

About this training

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.

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.

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.

 

 

Course Language : EN
On-demand video
Full lifetime access to videos
Downloadable resources
Assignments
Certificate of Completion
Enroll now

Training options

Only Videos

$ 30

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • Hours of Individual Coaching

Blended

$ 550

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • 10 Hours of Individual Coaching
Save 10%

Blended

$ 1040 $ 963

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • 20 Hours of Individual Coaching
Save 20%

Blended

$ 1590 $ 1272

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • 30 Hours of Individual Coaching

Course Content

Section 1 : DS and ML from A-Z Course Intro
Intro to Data Science + Machine Learning 02:31 mins
What is data science 09:48 mins
Machine learning Overview 05:26 mins
Who's this course is for 02:57 mins
DL and ML Marketplace 04:38 mins
Data Science and ML Job opps 02:37 mins
Data Science Job Roles 04:04 mins
Section 2 : Getting Started with R
Getting Started 10:58 mins
Basics 06:25 mins
Files 11:08 mins
RStudio 06:59 mins
Tidyverse 05:19 mins
Resources 04:03 mins
Section 3 : Data types and Structures in R
Introduction 30:03 mins
Basic Types 08:47 mins
Vectors - Part 1 19:41 mins
Vectors - Part 2 24:52 mins
Vectors - Missing Values 15:36 mins
Vectors - Coercion 14:07 mins
Vectors - Naming 10:16 mins
Vectors - Misc 06:00 mins
Creating Matrices 31:28 mins
Lists 31:42 mins
Introduction to Data Frames 19:20 mins
Creating Data Frames 19:50 mins
Data Frames - Helper Functions 31:12 mins
Data Frames - Tibbles 39:03 mins
Section 4 : Intermediate R
Introduction Intermediate R 46:31 mins
Relational Operations 11:07 mins
Logical Operators 07:05 mins
Conditional Statements 11:20 mins
Loops 07:57 mins
Functions 14:20 mins
Packages 11:29 mins
Factors 28:14 mins
Dates and Times 30:11 mins
Functional Programming 36:41 mins
Data Import or Export 22:07 mins
Database 27:09 mins
Section 5 : Data Manipulation R
Introduction 36:29 mins
Tidy Data 10:54 mins
The Pipe Operator 14:50 mins
The Filtre Verb 21:35 mins
The Select Verb 48:04 mins
The Mutate Verb 31:57 mins
The Arrange Verb 10:04 mins
The Summarize Verb 23:06 mins
Data Pivoting 42:42 mins
JSON Parsing 10:46 mins
String Manipulation 32:39 mins
Web Scraping 58:53 mins
Section 6 : Data Visualization in R
Introduction 17:13 mins
Getting Started 15:38 mins
Aesthetics Mappings 24:45 mins
Single Variables Plot 38:50 mins
Two Variable Piots 20:34 mins
Facets Layering and Coordinate System 17:56 mins
630989287 11:34 mins
Section 7 : Creating Reports with R Markdown
Creating Reports with R Markdown 28:54 mins
Section 8 : Building Webapps with R Shiny
Introduction with R Shiny 26:05 mins
A Basic App 31:18 mins
Other Examples 34:05 mins
Section 9 : Introduction to Machine Learning
Introduction to Machine Learning - Part 1 21:49 mins
Introduction to Machine Learning - Part 2 46:46 mins
Section 10 : Data Preprocessing
Data Preprocessing 37:47 mins
Introduction to Data Preprocessing 27:04 mins
Section 11 : Linear Regression - A Simple Model
Introduction 25:09 mins
Linear Regression - Simple Model 53:05 mins
Section 12 : Exploratory Data Analysis
Introduction 25:03 mins
Hands on Exploratory Data Analysis 62:57 mins
Section 13 : Linear Regression - A Real Model
Introduction 32:04 mins
Linear Regression in R - Real Model 52:48 mins
Section 14 : Logistic Regression
Introduction 37:48 mins
Logistic Regression in R 39:38 mins
Section 15 : Starting A Career in Data Science
Starting A Career in Data Science 02:54 mins
Data Science Resume 03:43 mins
Getting Started with Freelancing 04:44 mins
Top Freelancing Websites 05:19 mins
Personal Branding 05:28 mins
Improtance Website and Blog 03:43 mins
Networking dos and donts 03:51 mins

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