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Introduction to Business Analytics with R

Approx. 16 hours to complete

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Course Summary

Learn how to use business analytics to drive better business decisions and improve your company's performance.

Key Learning Points

Understand the fundamentals of business analytics and how it can be used to solve complex business problems
Learn how to identify and collect relevant data to analyze
Discover how to use data visualization techniques to communicate insights to stakeholders

Learning Outcomes

Understand how to use business analytics to solve complex business problems
Identify and collect relevant data to analyze
Communicate insights to stakeholders using data visualization techniques

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with Microsoft Excel

Course Difficulty Level

Intermediate

Course Format

Online Self-Paced
Video Lectures
Interactive Quizzes

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Description

Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings.

In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use R to prepare data for analysis you will gain experience using RStudio, a powerful integrated development environment (IDE), that has many built-in features that simplify coding with R. As you learn about the business analytic workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.

Knowledge

You will learn how to process data using R and RStudio. You will also explore the interaction between business principles and data analytics.

Outline

Course Overview and Module 1 How Do I Get Started Using a Data Analytic Language to Solve Business Problem?
Course Introduction
Meet Prof Ron Guymon
Learn on Your Terms
Introduction to Business Analytics and R
Overview of Business Analytics
Examples of Business Analytics
FACT Framework
Introduction to R
Getting Started with R
Calculations with R
Making Your Code Readable
Functions and Using the Built-In Help
Reading and Writing Data
Module 1 Conclusion
Syllabus
About the Discussion Forums
Glossary
Learn More About Flexible Learning Paths
Module 1 Overview
Module 1 Readings
Module 1 Quiz

Module 2 How Do I Get to Know My Data and Share It with Others?
Module 2 Introduction
Is Data an Asset?
Properties of a Tidy Dataframe
Data Dictionaries
Getting to Know Your Data 1: Explore as in Excel
Getting to Know Your Data 2: Referring to Specific Rows and Columns
Summary Statistics
Getting to Know Your Data 3: Summary Statistics for Each Column, and Quick Plots
FACT Framework: Tell Others About The Results
R Notebooks
Markdown
Dashboards Preview
Module 2 Conclusion
Module 2 Overview
Module 2 Readings
Module 2 Quiz

Module 3 How Can I Use Functions to Help with Data Preparation?
Module 3 Introduction
Assembling Data
Data Types
More on Functions
Packages
Introduction to Other Data Types
Creating Date Types
Calculations with Dates
Factors
Logical Type and Relational Operators
Character Strings
Module 3 Conclusion
Module 3 Overview
Module 3 Readings
Module 3 Quiz

Module 4 How Do I Preprocess Data?
Module 4 Introduction
Module 4 Introduction
Framing Questions for Actionable Insight
Dataframe Shape: Level of Aggregation
Dataframe: Control Versus Feasibility
Dataframe Shape: Wide Versus Long
Review of Notebooks and Introduction to dplyr
Subset Data Using dplyr's Select and Filter Functions
Useful Operators: %.% and %in%
Using dplyr's Mutate, Rename, Relocate, and Distinct Functions
Handling Missing Values
Data Aggregation and Summary
Pivoting Dataframes Between Wide and Long Shapes
Stacking and Sorting Data
Joining Data
Module 4 Conclusion
Gies Online Programs
Module 4 Overview
Module 4 Readings
Congratulations!
Get Your Course Certificate
Module 4 Quiz

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