Course and Faculty Introduction

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 yet inconspicuous step in the business analytic workflow. While learning how to use R to prepare data for analysis you will gain experience using RStudio, a powerful integrated development environment (IDE) with many built-in features that simplify coding with R. Exploring 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. Additionally, you will 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. 

Upon successful completion of this course, you will earn 20 ICMA CPE, a certificate of completion, and a digital badge.


Learning Outcomes

  • Examine the interplay between business principles and data analytics

  • Build a foundation for performing data analytics by installing and gaining experience using R, RStudio, tidyverse packages

  • Explore a dataset and share findings with others

  • Manipulate the most commonly used data types using functions

  • Develop efficient, easy-to-read approaches for assembling and processing data for analysis

Course Overview

Getting Started

You will be introduced to the role of data analytics in business domains. After reviewing examples of how business analytics have helped large and small companies, you will be introduced to the FACT framework for obtaining actionable insights in a business domain. You will then be introduced to R and RStudio and will receive guidance for installing those tools on your machine or using them in a cloud environment. By the end of this module you will be able to use R and RStudio to read and write data.

Get to Know Your Data

We will explore whether data is an asset and characteristics of a tidy dataframe. You will then learn how to explore a dataset by browsing it with spreadsheet software, as well as by using summary information and statistics. Importantly, you will also be introduced to notebooks and dashboards, which are convenient alternatives to copying and pasting from one software platform into another.

Using Functions

You will focus on the importance of assembling data, and learn how functions help with automating many of the data assembly and transformation tasks. We then explore various data types, and introduce you to several tidyverse packages for working with those data types. By the end of this module you will know how to import packages, access help using the functions that are in the packages, and apply functions for manipulating data types.

Preprocessing Data

We will explore how delegation, control, and feasibility influence the level at which data is aggregated. We then examine why the shape of the data matters. Upon completion of this module you will learn how to create easy-to-read code for performing many data preprocessing tasks such as aggregation, handling missing values, stacks, pivots, and joins.

Course curriculum

  • 1

    Module 0: Course Introduction

    • Course Introduction

    • Glossary
  • 2

    Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problem?

    • Module 1 Overview

    • Module 1 Readings

    • Overview of Business Analytics

    • Examples of Business Analytics

    • FACT Framework

    • Knowledge Check 1-1

    • Introduction to R

    • Getting Started with R

    • Calculations with R

    • Knowledge Check 1-2

    • Making Your Code Readable

    • Knowledge Check 1-3

    • Functions and Using the Built-In Help

    • Reading and Writing Data

    • Module 1 Conclusion

    • Module 1 Quiz
  • 3

    Module 2: How Do I Get to Know My Data and Share It with Others?

    • Module 2 Overview

    • Module 2 Readings

    • Is Data an Asset?

    • Properties of a Tidy Dataframe

    • Data Dictionaries

    • Knowledge Check 2-1

    • Getting to Know Your Data 1: Explore as in Excel

    • Getting to Know Your Data 2: Referring to Specific Rows and Columns

    • Statistics Summary

    • Knowledge Check 2-2

    • Getting to Know Your Data 3: Summary Statistics for Each Column, and Quick Plots

    • FACT Framework: Tell Others About The Results

    • R Notebooks

    • Markdown

    • Knowledge Check 2-3

    • Dashboards Preview

    • Module 2 Conclusion

    • Module 2 Quiz
  • 4

    Module 3: How Can I Use Functions to Help with Data Preparation?

    • Module 3 Overview

    • Module 3 Readings

    • Assembling Data

    • Data Types

    • Knowledge Check 3-1

    • Packages

    • Knowledge Check 3-2

    • Introduction to Other Data Types

    • Knowledge Check 3-3

    • Creating Date Types

    • Calculations with Dates

    • Knowledge Check 3-4

    • Factors

    • Logical Type and Relational Operators

    • Character Strings

    • Knowledge Check 3-5

    • Module 3 Conclusion

    • Module 3 Quiz

    • Activity 3-1

    • Activity 3-1 Debrief

    • More on Functions: Variables, argument names within the function, and returning values
  • 5

    Module 4: How Do I Preprocess Data?

    • Module 4 Overview

    • Module 4 Readings

    • Framing Questions for Actionable Insight

    • Dataframe Shape: Level of Aggregation

    • Dataframe Shape: Control Versus Feasibility

    • Dataframe Shape: Wide Versus Long

    • Knowledge Check 4-1

    • Review of Notebooks and Introduction to dplyr

    • Subset Data Using dplyr's Select and Filter Functions

    • Knowledge Check 4-2

    • Useful Operators: %>% and %in%

    • Using dplyr's Mutate, Rename, Relocate, and Distinct Functions

    • Handling Missing Values

    • Knowledge Checks 4-3

    • Data Aggregation and Summary

    • Pivoting Dataframes Between Wide and Long Shapes

    • Stacking and Sorting Data

    • Joining Data

    • Knowledge Check 4-4

    • Module 4 Conclusion

    • Module 4 Quiz

    • Survey Instructions

    • Feedback Survey

    • Survey Verification

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$139 USD one time fee
12 months access

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