Description

In this practical, hands-on course, you'll learn how to use R for effective data analysis and 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.

The course covers practical issues in statistical computing that 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 also 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. By the end of the course, you'll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up. All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-

What You Will Learn

Learn data cleaning, processing, wrangling, and manipulation
Learn plotting in R (graphs, charts, plots, histograms, and more)
How to create a resume and land your first job as a data scientist
Learn machine learning and its various practical applications
Learn data and file management in R
Use R to clean, analyze, and visualize data

Audience

This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.

Approach

This course takes you through data analysis in R, data visualization in R, and machine learning with hands-on step-by-step training.

Key Features

Learn data extraction and web scraping * Learn to build custom data solutions * Learn automating dynamic report generation

Github Repo

https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-

About the Author

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 people expand their skillsets.

Course Outline

1. Data Science and Machine Leaning Course Introduction

1. Data Science and Machine Learning Introduction Section Overview

This video explains data science and machine learning.

2. What is Data Science?

This video explains the concept of data science.

3. Machine Learning Overview

This video gives an overview of machine learning.

4. Data Science + Machine Learning Marketplace

This video explains the various trends and job opportunities in data science and machine learning.

5. Who is this Course For?

This video explains the audience for this course.

6. Data Science and Machine Learning Job Opportunities

This video explains the various job opportunities in data science and machine learning.

7. Data Science Job Roles

This video explains the various job roles for data science.

2. Getting Started with R

1. Getting Started with R

This video explains the concept of R.

2. R Basics

This video explains the R basics.

3. Working with Files

This video explains working with files in R.

4. R Studio

This video explains R studio.

5. Tidyverse Overview

This video provides an overview of tidyverse.

6. Additional Resources

This video shares some additional resources that will help you get along with the course.

3. Data Types and Structures in R

1. Data Types and Structures in R Section Overview

This video explains data types and structures in R.

2. Basic Types

This video explains basic types in R.

3. Vectors - Part One

This video introduces you to vectors.

4. Vectors - Part Two

This video explains how to interact with R using vectors.

5. Vectors: Missing Values

This video explains vector missing values.

6. Vectors: Coercion

This video explains coercion.

7. Vectors: Naming

This video explains vector naming.

8. Vectors: Miscellaneous

This video explains miscellaneous yet important concepts in vectors.

9. Working with Matrices

This video explains working with matrices.

10. Working with Lists

This video explains working with lists.

11. Introduction to Data Frames

This video explains introduction to data frames.

12. Creating Data Frames

This video explains creating data frames.

13. Data Frames: Helper Functions

This video explains data frames helper functions.

14. Data Frames: Tibbles

This video explains Tibbles in data frames.

4. Intermediate R

1. Intermedia R Section Introduction

This video introduces you to R section.

2. Relational Operators

This video explains relational operators.

3. Logical Operators

This video explains logical operators,

4. Conditional Statements

This video explains conditional statements.

5. Working with Loops

This video explains working with loops.

6. Working with Functions

This video explains working with functions in R.

7. Working with Packages

This video explains working with packages.

8. Working with Factors

This video explains working with factors in R.

9. Dates and Times

This video explains dates and times in R.

10. Functional Programming

This video explains functional programming.

11. Data Import/Export

This video explains how to import and export data in R.

12. Working with Databases

This video explains working with databases.

5. Data Manipulation in R

1. Data Manipulation Section Introduction

This video introduces you to data manipulation.

2. Tidy Data

This video explains tidy data.

3. The Pipe Operator

This video explains pipe operator.

4. {dplyr}: The Filter Verb

This video explains the filter verb.

5. {dplyr}: The Select Verb

This video explains the select verb

6. {dplyr}: The Mutate Verb

This video explains the mutate verb.

7. {dplyr}: The Arrange Verb

This video explains the purpose of arrange verb.

8. {dplyr}: The Summarize Verb

This video explains the summarize verb.

9. Data Pivoting: {tidyr}

This video explains data pivoting.

10. String Manipulation: {stringr}

This video explains string manipulation.

11. Web Scraping: {rvest}

This video explains web scraping.

12. JSON Parsing: {jsonlite}

This video explains JSON parsing.

6. Data Visualization in R

1. Data Visualization in R Section Introduction

This video explains data visualization in R.

2. Getting Started with Data Visualization in R

This video explains getting started with data visualization in R.

3. Aesthetics Mappings

This vide explains aesthetics mappings.

4. Single Variable Plots

This video focuses on single variable plots.

5. Two Variable Plots

This video explains two variable plots.

6. Facets, Layering, and Coordinate Systems

This video explains facets, layering, and coordinate systems.

7. Styling and Saving

This video explains styling and saving.

7. Creating Reports with R Markdown

1. Introduction to R Markdown

This video is an introduction to R.

8. Building Webapps with R Shiny

1. Introduction to R Shiny

This video is an introduction to R Shiny

2. Creating a Basic R Shiny App

This video helps you to create a basic R shiny app.

3. Other Examples with R Shiny

This video gives various examples with R shiny.

9. Introduction to Machine Learning

1. Introduction to Machine Learning Part One

This video is an introduction to machine learning.

2. Introduction to Machine Learning Part Two

This video explains the different approaches in machine learning.

10. Data Preprocessing

1. Data Preprocessing Introduction

This video introduces you to data preprocessing.

2. Data Preprocessing

This video explains some of the practical ways of data preprocessing.

11. Linear Regression: A Simple Model

1. Linear Regression: A Simple Model Introduction

This video explains a simple linear regression model.

2. A Simple Model

This video demonstrates a practical application of machine learning.

12. Exploratory Data Analysis

1. Exploratory Data Analysis Introduction

This video gives an introduction to exploratory data analysis.

2. Hands-on Exploratory Data Analysis

This video explains hands-on exploratory data analysis.

13. Linear Regression - a Real Model

1. Linear Regression - Real Model Section Introduction

This video explains linear regression - a real model.

2. Linear Regression in R - Real Model

This video explains linear regression in R.

14. Logistic Regression

1. Introduction to Logistic Regression

This video introduces you to logistic regression.

2. Logistic Regression in R

This video explains logistic regression in R.

15. Starting a Career in Data Science

1. Starting a Data Science Career Section Overview

This video explains section overview of data science and career.

2. Creating a Data Science Resume

This video explains creating a data science resume.

3. Getting Started with Freelancing

This vide explains how to get started with freelancing.

4. Top Freelance Websites

This video focuses on top freelancing websites.

5. Personal Branding

This video explains personal branding.

6. Networking Do's and Don'ts

This video explains networking do's and don'ts.

7. Setting Up a Website

This video explains how to set up a website.

Course Images

Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

By Packt

Booking options

Highlights

Description

Course Content

About The Provider

Tags

Reviews