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Development Data Science Machine Learning

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!
Rating: 4.6 out of 54.6 (517 ratings)
32,272 students
Created by Juan E. Galvan, Ismail Tigrek
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

15 sections • 80 lectures • 28h 39m total length

  • Preview02:30
  • Preview09:47
  • Preview05:26
  • Preview04:38
  • Who is This Course For?
    02:57
  • Data Science and Machine Learning Job Opportunities
    02:37

  • Preview10:58
  • Preview06:24
  • Preview11:08
  • 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 & 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 Markdown
    28:54

  • 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

Requirements

  • Basic computer skills

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’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.

Who this course is for:

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

Instructors

Juan E. Galvan
Digital Entrepreneur | Marketer | Visionary
Juan E. Galvan
  • 4.5 Instructor Rating
  • 3,859 Reviews
  • 175,197 Students
  • 19 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.

Ismail Tigrek
Data Strategy Consultant | Full-Stack Data Scientist
Ismail Tigrek
  • 4.6 Instructor Rating
  • 517 Reviews
  • 32,272 Students
  • 1 Course

After graduating as the top student in the Faculty of Engineering and Nature Sciences, I pursued a career in Data Science. Currently, I work as a Data Strategy Consultant, applying my experience and expertise to helping dozens of businesses in industries ranging from energy and medicine to biotech and finance. My passion is building custom, end-to-end data solutions and solving industry-specific business problems.

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