Data Science and Machine Learning Bootcamp with R

Data Science and Machine Learning Bootcamp with R, available at $99.99, has an average rating of 4.7, with 128 lectures, 1 quizzes, based on 17057 reviews, and has 95765 subscribers.

You will learn about Program in R Use R for Data Analysis Create Data Visualizations Use R to handle csv,excel,SQL files or web scraping Use R to manipulate data easily Use R for Machine Learning Algorithms Use R for Data Science This course is ideal for individuals who are Anyone interested in becoming a Data Scientist It is particularly useful for Anyone interested in becoming a Data Scientist.

Enroll now: Data Science and Machine Learning Bootcamp with R

Summary

Title: Data Science and Machine Learning Bootcamp with R

Price: $99.99

Average Rating: 4.7

Number of Lectures: 128

Number of Quizzes: 1

Number of Published Lectures: 128

Number of Published Quizzes: 1

Number of Curriculum Items: 129

Number of Published Curriculum Objects: 129

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Program in R
Use R for Data Analysis
Create Data Visualizations
Use R to handle csv,excel,SQL files or web scraping
Use R to manipulate data easily
Use R for Machine Learning Algorithms
Use R for Data Science

Who Should Attend

Anyone interested in becoming a Data Scientist

Target Audiences

Anyone interested in becoming a Data Scientist

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture�this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:

Programming with R
Advanced R Features
Using R Data Frames to solve complex tasks
Use R to handle Excel Files
Web scraping with R
Connect R to SQL
Use ggplot2 for data visualizations
Use plotly for interactive visualizations
Machine Learning with R, including:
Linear Regression
K Nearest Neighbors
K Means Clustering
Decision Trees
Random Forests
Data Mining Twitter
Neural Nets and Deep Learning
Support Vectore Machines
and much, much more!

Enroll in the course and become a data scientist today!

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction to Course

Lecture 2: Course Curriculum

Lecture 3: What is Data Science?

Lecture 4: Course FAQ

Chapter 2: Course Best Practices

Lecture 1: How to Get Help in the Course!

Lecture 2: Installation and Set-Up

Chapter 3: Windows Installation Set-Up

Lecture 1: Windows Installation Procedure

Chapter 4: Mac OS Installation Set-Up

Lecture 1: Mac OS Installation Procedure

Chapter 5: Linux Installation

Lecture 1: Linux/Unbuntu Installation Procedure

Chapter 6: Development Environment Overview

Lecture 1: Development Environment Overview

Lecture 2: Course Notes

Lecture 3: Guide to RStudio

Chapter 7: Introduction to R Basics

Lecture 1: Introduction to R Basics

Lecture 2: Arithmetic in R

Lecture 3: Variables

Lecture 4: R Basic Data Types

Lecture 5: Vector Basics

Lecture 6: Vector Operations

Lecture 7: Comparison Operators

Lecture 8: Vector Indexing and Slicing

Lecture 9: Getting Help with R and RStudio

Lecture 10: R Basics Training Exercise

Lecture 11: R Basics Training Exercise – Solutions Walkthrough

Chapter 8: R Matrices

Lecture 1: Introduction to R Matrices

Lecture 2: Creating a Matrix

Lecture 3: Matrix Arithmetic

Lecture 4: Matrix Operations

Lecture 5: Matrix Selection and Indexing

Lecture 6: Factor and Categorical Matrices

Lecture 7: Matrix Training Exercise

Lecture 8: Matrix Training Exercises – Solutions Walkthrough

Chapter 9: R Data Frames

Lecture 1: Introduction to R Data Frames

Lecture 2: Data Frame Basics

Lecture 3: Data Frame Indexing and Selection

Lecture 4: Overview of Data Frame Operations – Part 1

Lecture 5: Overview of Data Frame Operations – Part 2

Lecture 6: Data Frame Training Exercise

Lecture 7: Data Frame Training Exercises – Solutions Walkthrough

Chapter 10: R Lists

Lecture 1: List Basics

Chapter 11: Data Input and Output with R

Lecture 1: Introduction to Data Input and Output with R

Lecture 2: CSV Files with R

Lecture 3: Note on R with Excel Download

Lecture 4: Excel Files with R

Lecture 5: SQL with R

Lecture 6: Web Scraping with R

Chapter 12: R Programming Basics

Lecture 1: Introduction to Programming Basics

Lecture 2: Logical Operators

Lecture 3: if, else, and else if Statements

Lecture 4: Conditional Statements Training Exercise

Lecture 5: Conditional Statements Training Exercise – Solutions Walkthrough

Lecture 6: While Loops

Lecture 7: For Loops

Lecture 8: Functions

Lecture 9: Functions Training Exercise

Lecture 10: Functions Training Exercise – Solutions

Chapter 13: Advanced R Programming

Lecture 1: Introduction to Advanced R Programming

Lecture 2: Built-in R Features

Lecture 3: Apply

Lecture 4: Math Functions with R

Lecture 5: Regular Expressions

Lecture 6: Dates and Timestamps

Chapter 14: Data Manipulation with R

Lecture 1: Data Manipulation Overview

Lecture 2: Guide to Using Dplyr

Lecture 3: Guide to Using Dplyr – Part 2

Lecture 4: Pipe Operator

Lecture 5: Quick note on Dpylr exercise

Lecture 6: Dplyr Training Exercise

Lecture 7: Dplyr Training Exercise – Solutions Walkthrough

Lecture 8: Guide to Using Tidyr

Chapter 15: Data Visualization with R

Lecture 1: Overview of ggplot2

Lecture 2: Histograms

Lecture 3: Scatterplots

Lecture 4: Barplots

Lecture 5: Boxplots

Lecture 6: 2 Variable Plotting

Lecture 7: Coordinates and Faceting

Lecture 8: Themes

Lecture 9: ggplot2 Exercises

Lecture 10: ggplot2 Exercise Solutions

Chapter 16: Data Visualization Project

Lecture 1: Data Visualization Project

Lecture 2: Data Visualization Project – Solutions Walkthrough – Part 1

Lecture 3: Data Visualization Project Solutions Walkthrough – Part 2

Chapter 17: Interactive Visualizations with Plotly

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 73 votes
2 stars: 106 votes
3 stars: 992 votes
4 stars: 5601 votes
5 stars: 10285 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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