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Data Science and Machine Learning with R

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

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Created by Juan E. Galvan
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4.5 (979 ratings)
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This course includes

28 h 39 min of video content
Beginner Difficulty
Perpetual Access
Access on mobile and Tablet
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Learning Objectives

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 applications 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

Table of Contents

80 lectures
28 h 39 min
Data Science and Machine Learning Introduction Section Overview
02:30
What is Data Science?
09:47
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
Getting Started with R
10:58
R Basics
06:24
Working with Files
11:08
R Studio
06:58
Tidyverse Overview
05:19
Additional Resources
04:02
Data Types and Structures in R Section Overview
24:23
Basic Types
08:46
Vectors Part One
19:40
Vectors Part Two
24:51
Vectors: Missing Values
15:35
Vectors: Coercion
14:07
Vectors: Naming
10:15
Vectors: Misc.
05:59
Working with Matrices
31:27
Working with Lists
31:41
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
Relational Operators
11:06
Logical Operators
07:04
Conditional Statements
11:20
Working with Loops
07:56
Working with Functions
14:19
Working with Packages
11:29
Working with Factors
28:14
Dates and Times
30:10
Functional Programming
36:41
Data Import/Export
22:06
Working with Databases
27:08
Data Manipulation Section Intro
36:29
Tidy Data
10:53
The Pipe Operator
14:50
{dplyr}: The Filter Verb
21:34
{dplyr}: The Select Verb
46:03
{dplyr}: The Mutate Verb
31:57
{dplyr}: The Arrange Verb
10:03
{dplyr}: The Summarize Verb
23:05
Data Pivoting: {tidyr}
42:41
String Manipulation: {stringr
32:38
Web Scraping: {rvest}
58:53
JSON Parsing: {jsonlite}
10:46
Data Visualization in R Section Intro
17:13
Getting Started with Data Visualization in R
15:37
Aesthetics Mappings
24:45
Single Variable Plots
36:50
Two Variable Plots
20:33
Facets, Layering, and Coordinate Systems
17:56
Styling and Saving
11:33
Introduction to R Shiny
26:05
Creating A Basic R Shiny App
31:18
Other Examples with R Shiny
34:05
Introduction to Machine Learning Part One
21:48
Introduction to Machine Learning Part Two
46:45
Data Preprocessing Intro
27:03
Data Preprocessing
37:47
Linear Regression: A Simple Model Intro
25:09
A Simple Model
53:05
Exploratory Data Analysis Intro
25:03
Hands-on Exploratory Data Analysis
01:02:57
Linear Regression - Real Model Section Intro
32:04
Linear Regression in R - Real Model
52:48
Introduction to Logistic Regression
37:48
Logistic Regression in R
39:37
Starting a Data Science Career Section Overview
02:54
Creating A Data Science Resume
03:43
Getting Started with Freelancing
04:44
Top Freelance Websites
05:18
Personal Branding
05:27
Networking Do's and Don'ts
03:50
ID3 - Setting Up a Website
03:42

Description

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
  • 2: DATA TYPES/STRUCTURES IN R
  • 3: DATA MANIPULATION IN R
  • 4: DATA VISUALIZATION IN R
  • 5: MACHINE LEARNING
  • 6: STARTING A DATA SCIENCE CAREER

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.

About Instructor

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Juan E. Galvan

Digital Entrepreneur | Marketer | Consultant | Visionary

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 skill sets.

Featured Reviews

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Jan 12, 2021

Mathias Erhodayene

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It was absolutely a good match for me. I love data science and i want to learn it with all joy to become a professional in this field.
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Mar 02, 2021

Harish N

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Wonderful Lecturer . very nice course ....thanks to lectures
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Jul 30, 2021

Omar Hanif

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Yet another great feat by this instructor. Everything about data science is covered here with detailed examples. An excellent courses, no doubt!