2021 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!
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2021 Data Science & Machine Learning with R from A-Z Course.

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Course Outline

A comprehensive list of all sections & lectures for this course can be found below.

Data Science and Machine Learning Course Intro

Intro and Section Overview - 02:31 [Play]

What is Data Science? - 09:48

Machine Learning Overview - 05:26

Data Science + Machine Learning Marketplace - 04:38

Who is this Course for? - 02:57

Data Science and Machine Learning Job Opportunities - 02:37

Data Science Job Roles - 04:04

Getting Started with R - 10:58 [Play]

R Basics - 06:25

Working with Files - 11:08

R Studio - 06:59

Tidyverse Overview - 05:19

Additional Resources - 04:03

Data Types and Structures in R Section Overview - 30:03 [Play]

Basic Types - 08:47

Vectors Part One - 19:41

Vectors Part Two - 24:52

Vectors - Missing Values - 15:36

Vectors - Coercion - 14:07

Vectors - Naming - 10:16

Vectors - Misc - 06:00

Working with Matrices - 31:28

Working with Lists - 31:42

Introduction to Data Frames - 19:20

Creating Data Frames - 19:50

Data Frames: Helper Functions - 31:12

Data Frames: Tibbles - 39:03

Intermedia R Section Introduction - 46:31 [Play]

Relational Operations - 11:07

Logical Operators - 07:05

Conditoinal Statements - 11:20

Working with Loops - 07:57

Working with Functions - 14:20

Working with Packages - 11:29

Working with Factors - 28:14

Dates and Times - 30:11

Functional Programming - 36:41

Data Import or Export - 22:07

Working with Databases - 27:09

Data Manipulation Section Intro - 36:29 [Play]

Tidy Data - 10:54

The Pipe Operator - 14:50

{dplyr}: The Filter Verb - 21:35

{dplyr}: The Select Verb - 46:04

{dplyr}: The Mutate Verb - 31:57

{dplyr}: The Arrange Verb - 10:04

{dplyr}: The Summarize Verb - 23:06

Data Pivoting: {tidyr} - 42:42

String Manipulation - 32:39

Web Scraping - 58:53

JSON Parsing - 10:46

Data Visualization in R Section Intro - 17:13 [Play]

Getting Started with Data Visualization in R - 15:38

Aesthetics Mappings - 24:45

Single Variables Plot - 36:50

Two Variable Plots - 20:34

Facets Layering and Coordinate System - 17:56

Styling and Saving - 11:34

Introduction to R Markdown - 28:55 [Play]

Introduction to R Shiny - 26:05 [Play]

Creating A Basic R Shiny App - 26:05

Other Examples with R Shiny - 34:05

Introduction to Machine Learning Part One - 21:49 [Play]

Introduction to Machine Learning Part Two - 46:46

Data Preprocessing Intro - 27:04 [Play]

Data Preprocessing - 37:47

Linear Regression: A Simple Model Intro - 25:09 [Play]

A Simple Model - 53:05

Exploratory Data Analysis Intro - 25:03 [Play]

Hands-on Exploratory Data Analysis - 02:57

Linear Regression - Real Model Section Intro - 37:48 [Play]

Linear Regression in R - Real Model - 52:48

Introduction to Logistic Regression - 37:48 [Play]

Logistic Regression in R - 39:38

Starting a Data Science Career Section Overview - 02:54 [Play]

Creating A Data Science Resume - 03:43

Getting Started with Freelancing - 04:44

Top Freelance Websites - 05:19

Personal Branding - 05:28

Networking Do's and Don'ts - 03:51

Setting Up a Website - 03:43

Course Description

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%u2019s 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.


2021 Data Science & Machine Learning with R from A-Z Course

All course reviews are written by students who have completed the course or are currently enrolled.

Course Instructor - Juan Galvan

juan galvan
Teaching 12 Courses

juan galvan is currently teaching 12 courses. All courses are currently open for enrollment.

215,500 Enrollments

juan galvan currently has 215,500 global enrollments across 12 courses that are active on the platform.

4.5 Star Rating

juan galvan has an average rating of 4.5/5 stars, across 12 courses.

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.



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