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Introduction to Applied Business Analytics

Introduction to Applied Business Analytics

This course is part of multiple programs.

Instructor: Ronald Guymon

44,333 already enrolled

Included with

4 modules

Gain insight into a topic and learn the fundamentals.

325 reviews

Beginner level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

325 reviews

Beginner level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

What you'll learn

Examine the interplay between business principles and data analytics.
Build a foundation in data analytics by installing and using a data analytics language, an integrated development environment (IDE), and key modules.
Manipulate the most commonly used data types using functions.
Develop efficient, easy-to-read approaches for assembling and processing data for analysis.

Skills you'll gain

Category: Data Preprocessing
Category: Analytics
Category: Data Presentation
Category: Descriptive Analytics
Category: Data-Driven Decision-Making
Category: Business Workflow Analysis
Category: Business Solutions
Category: Data Analysis
Category: Business Analysis

Tools you'll learn

Category: Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

Build your subject-matter expertise

This course is available as part of

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Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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 Python, a widely adopted data analytics language, 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 Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics 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.

In this module, you will be introduced to (1) the FACT framework for approaching business analytics, and (2) Python and the use of JupyterLab or Google Colab for running basic analyses.

What's included

20 videos8 readings2 assignments1 discussion prompt1 plugin

20 videosTotal 118 minutes

Course Introduction5 minutes
Meet Professor Ron Guymon4 minutes
The Impact of the Gies Community2 minutes
Module 1 Introduction7 minutes
Overview of Business Analytics4 minutes
Examples of Business Analytics8 minutes
FACT Framework 8 minutes
Why Python for Business Analytics?1 minute
Python Accoutrements4 minutes
Python and Integrated Development Environments (IDEs)7 minutes
Installing Python Using JupyterLab Desktop (Recommended for Windows and Mac Users)6 minutes
Installing Python Using Homebrew and Pyenv (For advanced Mac users)7 minutes
Installing Python from Python.org for Windows (For advanced Windows users)5 minutes
An Example Workflow With JupyterLab9 minutes
Tour of JupyterLab8 minutes
Using Interactive Python Notebook (IPYNB) Files9 minutes
Tour of Jupyter Notebook3 minutes
Basic Calculations with Python8 minutes
Google Colab - An Online Version of Jupyter Notebook9 minutes
Module 1 Conclusion3 minutes

8 readingsTotal 80 minutes

Syllabus10 minutes
Glossary10 minutes
About the Discussion Forums10 minutes
Online Education at Gies College of Business10 minutes
Update Your Profile10 minutes
Module 1 Overview10 minutes
Module 1 Readings10 minutes
Installing Python and JupyterLab: Start Here10 minutes

2 assignmentsTotal 30 minutes

Module 1 Quiz30 minutes
Orientation Quiz0 minutes

1 discussion promptTotal 10 minutes

Getting to Know Your Classmates10 minutes

1 pluginTotal 15 minutes

Welcome! Please Tell Us About Yourself15 minutes

In this module, you’ll focus on framing clear, purposeful questions—whether you're identifying business problems or writing Python code. You’ll explore strategies for troubleshooting and gathering help from various sources, including AI tools, built-in documentation, and error messages. You’ll also be introduced to foundational Python data structures like DataFrames, dictionaries, lists, and strings.

What's included

14 videos2 readings1 assignment

14 videosTotal 85 minutes

Module 2 Introduction4 minutes
Framing Questions for Actionable Insight8 minutes
Framing Python Questions14 minutes
Framing Questions for External Sources9 minutes
Framing Questions for Python's Built-In Documentation6 minutes
Framing Questions About Module Functions and Methods5 minutes
Framing Questions About Pandas Dataframes8 minutes
Framing Questions About Python Dictionaries5 minutes
Framing Questions About Python Lists4 minutes
Framing Questions About Python Strings7 minutes
Acting on the Answer3 minutes
Acting on the Answer by Running Code Experiments7 minutes
Acting on the Answer by Reading Error Messages5 minutes
Module 2 Conclusion3 minutes

2 readingsTotal 20 minutes

Module 2 Overview10 minutes
Module 2 Readings10 minutes

1 assignmentTotal 30 minutes

Module 2 Quiz30 minutes

In this module, you will learn about tidy data and then gain practice using basic exploratory techniques for evaluating the tidiness of pandas DataFrames. Specifically, you’ll first learn various approaches for filtering data to specific rows and columns. You’ll then learn how to explore the data using descriptive statistics and visualizations. By mastering these techniques, you'll be equipped to efficiently identify the value of real-world data and the potential of that data for providing insight to the business questions that have been framed.

What's included

12 videos2 readings1 assignment1 peer review

12 videosTotal 106 minutes

Module 3 Introduction3 minutes
Is Data an Asset?7 minutes
Assembling Data7 minutes
Properties of a Tidy Dataframe5 minutes
Data Dictionaries5 minutes
Characteristics of a Tidy Dataset13 minutes
Exploring Dataframes Using Filters10 minutes
Exploring Dataframes Using Conditional Statements13 minutes
Summary Statistics12 minutes
Exploring Data with Summary Statistics12 minutes
Exploring Dataframes with Visualizations16 minutes
Module 3 Conclusion3 minutes

2 readingsTotal 20 minutes

Module 3 Overview10 minutes
Module 3 Readings10 minutes

1 assignmentTotal 30 minutes

Module 3 Quiz30 minutes

1 peer reviewTotal 120 minutes

Module 3 Peer Reviewed Assignment 120 minutes

In this module, you’ll clean and prepare data using core Python and pandas tools. Through hands-on examples, you’ll fix issues like missing values and formatting problems, organize data into a tidy structure, write clear and efficient code, and save data in a more compact format that preserves the cleaned data.

What's included

13 videos4 readings1 assignment1 plugin

13 videosTotal 87 minutes

Module 4 Introduction5 minutes
Cleaning and Preprocessing the Data9 minutes
General Data Cleaning Tasks for Columns of a Dataframe6 minutes
General Data Cleaning Tasks for Rows of a Dataframe10 minutes
Cleaning String Columns of a Dataframe13 minutes
Cleaning Date Columns of a Dataframe11 minutes
Dataframe Shape: Wide Versus Long4 minutes
Changing the Shape of a Dataframe5 minutes
Combining Dataframes9 minutes
Cleaning Your Code6 minutes
Saving Cleaned Data7 minutes
Module 4 Conclusion2 minutes
Learn on Your Terms1 minute

4 readingsTotal 40 minutes

Module 4 Overview10 minutes
Module 4 Readings10 minutes
Congratulations on completing the course!10 minutes
Get Your Course Certificate10 minutes

1 assignmentTotal 30 minutes

Module 4 Quiz30 minutes

1 pluginTotal 15 minutes

How Was the Course?15 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Instructor ratings

(100 ratings)

Ronald Guymon

University of Illinois Urbana-Champaign

5 Courses92,459 learners

Offered by

University of Illinois Urbana-Champaign

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Reviewed on Aug 2, 2023

Excellent content for someone with no background in R. The course is thoughtfully designed and well presented. I am willing to ignore a couple of minor issues I noticed.

Reviewed on Dec 13, 2020

This course is well designed for newbies like me and our teachers are super dedicated.

Reviewed on Sep 2, 2023

Thank you! Great learning experience and easy to follow guidance.

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