Skip to content

Bootcamps
About
Explore
Get started
- Sign up
- Log in
Book a Consultation

Bootcamps
About
Explore
Get started
- Sign up
- Log in
Book a Consultation

Data Science Bootcamp with R

Home
Courses
Data Science Bootcamp with R

Current Status

Not Enrolled

Price

Closed

Get Started

Take this Course

This course requires no prior knowledge of R Programming, Data Science, or Machine Learning.

Course & topics

Course Home Expand All

Prerequisites

2 Topics

Important Information!

1 Topic

Getting Help (IMPORTANT!!!)

Module 1: Jump Start

1 Topic

1.1 Goal: Better Serving Customers With Data

1 Topic

The Business Case

1.2 What Tools Are In Our Toolbox?

2 Topics

Download the ULTIMATE R Cheatsheet

How To Use The ULTIMATE R Cheatsheet

1.3 Data Science Project Setup

8 Topics

Installing R & RStudio IDE, Part 1: Installing R

Installing R & RStudio IDE, Part 2: The RStudio IDE

RStudio IDE: Setup & Customization

Setting Up The Project (File Download)

Project Directory Structure & Contents

Windows Users: Install RTools

Installing R Packages (File Download)

Package Installation Checkpoint

1.4 Optional: Transactional Database Primer

5 Topics

Who Should Watch This?

Transactional Data: What Is It? What Will We Do With It In This Course?

Orders: The Building Blocks Of Transactional Data (File Download)

Database 101: The Entity Relationship Diagram (ERD)

Understanding Database Relationships

1.5 Sales Analysis – Diving In!

3 Topics

Read This! – Jumpstart Intent

Setup (File Download)

1.5.1 Sales Analysis, Part 1 – Importing, Examining, & Joining Data

6 Topics

Importing Excel Files

Examining Data: Console, Data Window, glimpse()

Joining Data, Part 1: Combining 2 Tibbles With left_join()

Joining Data, Part 2: Combining Multiple Tibbles With The Pipe

Code Checkpoint: Joining Data (File Download)

1.5.2 Sales Analysis, Part 2 – Wrangling Data With dplyr

10 Topics

Wrangling Data Overview

Splitting Description Into Category 1, Category 2, & Frame Material: separate()

Splitting Location Into City & State: separate()

Adding Total Price Column: mutate()

Removing Unnecessary Columns: select()

Getting The Order ID Column Back: bind_cols()

Reordering Columns: select()

Renaming Columns: rename() & set_names()

Storing The Wrangled Data

Code Checkpoint: Wrangling Data (File Download)

1.5.3 Sales Analysis, Part 3 – Visualizing Data With ggplot2

8 Topics

Sales Analysis Visualizations Overview

Sales By Year: Data Manipulation

Sales By Year: ggplot geometries

Sales By Year: ggplot2 formatting

Sales By Year & Category 2: Data Manipulation

Sales By Year & Category 2: ggplot geometries + facet_wrap

Sales By Year & Category 2: ggplot2 Formatting

Checkpoint Download

1.5.4 Visualization Process & Saving Key Data

3 Topics

Data Visualization Process (File Download)

Writing Files: Excel, CSV, RDS

Checkpoint Download

Module 2: Import & Data Wrangling Foundations (Level 1)

Module 2 Overview

1 Topic

Module 2 Overview

Important Concepts Before Diving In

2 Topics

Data Type & Structure Basics (File Download)

Tidy Data Primer (File Download)

2.1 Importing Data: readr, readxl, odbc, & RSQLite

10 Topics

Importing Data Overview

Data Import Cheatsheet

Set Up File Download

CSV Files: Fixing Parsing Errors With Column Specifications

Database Connection Resources

Connecting To Databases: SQLite

Code Checkpoint: Importing Data (File Download)

2.2 Wrangling Data (Cleaning, Preparing, & Manipulating Data): dplyr & tidyr

4 Topics

Data Wrangling Overview

Setup (File Download)

Data Wrangling Outline

Examining The Data

2.2.1 Using The ULTIMATE R Cheatsheet For Data Wrangling

1 Topic

Using The R Cheatsheet For Data Wrangling

2.2.2 Working With Columns (Features)

5 Topics

Selecting Columns: select() & select helpers

Pull Vectorized Contents: pull()

Select Data By Data Type: select_if()

Sorting Columns: arrange()

2.2.3 Working With Rows (Observations)

5 Topics

Filtering Rows: filter()

Filtering Categorical Data: %in%, ==, and !=

Filtering By Row Number: slice()

Filtering Unique Observations: distinct()

Code Checkpoint: filter (File Download)

2.2.4 Performing Feature-Based Calculations

4 Topics

Adding Calculated Columns: mutate()

Binning Numeric Data: mutate() + ntile()

If-Then Statements Inside Mutate: mutate() + case_when()

Code Checkpoint: mutate (File Download)

1 of 4

© 2026 Dataset Nexus Tech. All rights reserved.

Contact
Careers
Privacy
Terms
Refund

Login

Accessing this course requires a login. Please enter your credentials below!

Username or Email Address

Password

Remember Me

Lost Your Password?

Register

Don't have an account? Register one!

Register an Account

Username

Email

Registration confirmation will be emailed to you.