Overview What You Will Learn Curriculum Instructor

Course Overview

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. 

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. 

By the end of this course, you will be familiar with how to write code in R, analyze and visualize data. At the completion, you would also learn how to get paid for your newly developed programming skills.

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.
  • Determine how to use R to clean, analyze, and visualize data.
  • Learn the Tidyverse.
  • Learn Operators, Vectors, Lists and their application.
  • Understand Data visualization (ggplot2).
  • Familiarize with data extraction and web scraping.
  • Learn full-stack data science development.
  • Learn how to build custom data solutions.
  • Introduction to automating dynamic report generation.
  • Learn about Data science for business.

Program Curriculum

  • Data Science and Machine Learning Intro Section Overview
  • What is Data Science?
  • Machine Learning Overview
  • Data Science + Machine Learning Marketplace
  • Who is This Course For?
  • Data Science and Machine Learning Job Opportunities
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Getting Started with R
  • R Basics
  • Working with Files
  • R Studio
  • Tidyverse Overview
  • Additional Resources
  • Chapter 2 Quiz

  • Data Types and Structures in R Section Overview
  • Basic Types
  • Vectors Part One
  • Vectors Part Two
  • Vectors: Missing Values
  • Vectors: Coercion
  • Vectors: Naming
  • Vectors: Misc.
  • Working with Matrices
  • Working with Lists
  • Introduction to Data Frames
  • Creating Data Frames
  • Data Frames: Helper Functions
  • Data Frames: Tibbles
  • Chapter 3 Quiz

  • Intermedia R Section Introduction
  • Relational Operators
  • Logical Operators
  • Conditional Statements
  • Working with Loops
  • Working with Functions
  • Working with Packages
  • Working with Factors
  • Dates & Times
  • Functional Programming
  • Data Import/Export
  • Working with Databases
  • Chapter 4 Quiz

  • Data Manipulation Section Intro
  • Tidy Data
  • The Pipe Operator
  • {dplyr}: The Filter Verb
  • {dplyr}: The Select Verb
  • {dplyr}: The Mutate Verb
  • {dplyr}: The Arrange Verb
  • {dplyr}: The Summarize Verb
  • Data Pivoting: {tidyr}
  • String Manipulation: {stringr}
  • Web Scraping: {rvest}
  • JSON Parsing: {jsonlite}
  • Chapter 5 Quiz

  • Data Visualization in R Section Intro
  • Getting Started with Data Visualization in R
  • Aesthetics Mappings
  • Single Variable Plots
  • Two Variable Plots
  • Facets, Layering, and Coordinate Systems
  • Styling and Saving
  • Chapter 6 Quiz

  • Introduction to R Markdown
  • Chapter 7 Quiz

  • Introduction to R Shiny
  • Creating A Basic R Shiny App
  • Other Examples with R Shiny
  • Chapter 8 Quiz

  • Introduction to Machine Learning Part One
  • Introduction to Machine Learning Part Two
  • Chapter 9 Quiz

  • Data Preprocessing Intro
  • Data Preprocessing
  • Chapter 10 Quiz

  • Linear Regression: A Simple Model Intro
  • A Simple Model
  • Chapter 11 Quiz

  • Exploratory Data Analysis Intro
  • Hands-on Exploratory Data Analysis
  • Chapter 12 Quiz

  • Linear Regression - Real Model Section Intro
  • Linear Regression in R - Real Model
  • Chapter 13 Quiz

  • Introduction to Logistic Regression
  • Logistic Regression in R
  • Chapter 14 Quiz

  • Starting a Data Science Career Section Overview
  • Creating A Data Science Resume
  • Getting Started with Freelancing
  • Top Freelance Websites
  • Personal Branding
  • Networking Do's and Don'ts
  • Setting Up a Website
  • Chapter 15 Quiz
Load more modules

Instructor

Juan E. Galvan

Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. He believes in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping you expand your skillsets.

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