Introduction to Applied Business Analytics

Overview

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.

Syllabus

Course Introduction and Module 1: How Do I Get Started Using a Data Analytic Language to Solve Business Problems?

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.

Module 2: How Can I Frame Business Questions and Data Analytic Questions?

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.

Module 3: How Can I Explore the Data?

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.

Module 4: How Do I Assemble the Data?

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.