Learn Data Science Machine Learning with R from A-Z

Learn Data Science & Machine Learning with R from A-Z, available at $59.99, has an average rating of 4.35, with 80 lectures, based on 1364 reviews, and has 95123 subscribers.

You will learn about 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 its 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 This course is ideal for individuals who are Students who want to learn about Data Science and Machine Learning It is particularly useful for Students who want to learn about Data Science and Machine Learning.

Enroll now: Learn Data Science & Machine Learning with R from A-Z

Summary

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

Price: $59.99

Average Rating: 4.35

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $129.99

Quality Status: approved

Status: Live

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 its 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 Attend

Students who want to learn about Data Science and Machine Learning

Target Audiences

Students who want to learn about Data Science and Machine Learning

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.

Course Curriculum

Chapter 1: Data Science and Machine Learning Course Intro

Lecture 1: Data Science and Machine Learning Intro Section Overview

Lecture 2: What is Data Science?

Lecture 3: Machine Learning Overview

Lecture 4: Data Science + Machine Learning Marketplace

Lecture 5: Who is This Course For?

Lecture 6: Data Science and Machine Learning Job Opportunities

Chapter 2: Getting Started with R

Lecture 1: Getting Started with R

Lecture 2: R Basics

Lecture 3: Working with Files

Lecture 4: R Studio

Lecture 5: Tidyverse Overview

Lecture 6: Additional Resources

Chapter 3: Data Types and Structures in R

Lecture 1: Data Types and Structures in R Section Overview

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: Vectors: Misc.

Lecture 9: Working with Matrices

Lecture 10: Working with Lists

Lecture 11: Introduction to Data Frames

Lecture 12: Creating Data Frames

Lecture 13: Data Frames: Helper Functions

Lecture 14: Data Frames: Tibbles

Chapter 4: Intermediate R

Lecture 1: Intermedia R Section Introduction

Lecture 2: Relational Operators

Lecture 3: Logical Operators

Lecture 4: Conditional Statements

Lecture 5: Working with Loops

Lecture 6: Working with Functions

Lecture 7: Working with Packages

Lecture 8: Working with Factors

Lecture 9: Dates & Times

Lecture 10: Functional Programming

Lecture 11: Data Import/Export

Lecture 12: Working with Databases

Chapter 5: Data Manipulation in R

Lecture 1: Data Manipulation Section Intro

Lecture 2: Tidy Data

Lecture 3: The Pipe Operator

Lecture 4: {dplyr}: The Filter Verb

Lecture 5: {dplyr}: The Select Verb

Lecture 6: {dplyr}: The Mutate Verb

Lecture 7: {dplyr}: The Arrange Verb

Lecture 8: {dplyr}: The Summarize Verb

Lecture 9: Data Pivoting: {tidyr}

Lecture 10: String Manipulation: {stringr}

Lecture 11: Web Scraping: {rvest}

Lecture 12: JSON Parsing: {jsonlite}

Chapter 6: Data Visualization in R

Lecture 1: Data Visualization in R Section Intro

Lecture 2: Getting Started with Data Visualization in R

Lecture 3: Aesthetics Mappings

Lecture 4: Single Variable Plots

Lecture 5: Two Variable Plots

Lecture 6: Facets, Layering, and Coordinate Systems

Lecture 7: Styling and Saving

Chapter 7: Creating Reports with R Markdown

Lecture 1: Introduction to R Markdown

Chapter 8: Building Webapps with R Shiny

Lecture 1: Introduction to R Shiny

Lecture 2: Creating A Basic R Shiny App

Lecture 3: Other Examples with R Shiny

Chapter 9: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning Part One

Lecture 2: Introduction to Machine Learning Part Two

Chapter 10: Data Preprocessing

Lecture 1: Data Preprocessing Intro

Lecture 2: Data Preprocessing

Chapter 11: Linear Regression: A Simple Model

Lecture 1: Linear Regression: A Simple Model Intro

Lecture 2: A Simple Model

Chapter 12: Exploratory Data Analysis

Lecture 1: Exploratory Data Analysis Intro

Lecture 2: Hands-on Exploratory Data Analysis

Chapter 13: Linear Regression – A Real Model

Lecture 1: Linear Regression – Real Model Section Intro

Lecture 2: Linear Regression in R – Real Model

Chapter 14: Logistic Regression

Lecture 1: Introduction to Logistic Regression

Lecture 2: Logistic Regression in R

Chapter 15: 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 Dos and Donts

Lecture 7: Setting Up a Website

Instructors

Juan E. Galvan
Digital Entrepreneur | Business Coach

Ismail Tigrek
Data Strategy Consultant | Full-Stack Data Scientist

Rating Distribution

1 stars: 10 votes

2 stars: 10 votes

3 stars: 119 votes

4 stars: 494 votes

5 stars: 731 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!