Welcome To BUS 1301: Managing with AI

Author

Robert W. Walker

Published

January 12, 2026

obertwwalker.github.io

Welcome to Managing with Data, Analytics, and Artificial Intelligence!

Our Goal in Four Parts

Together, we will investigate how managers collect, process, and interpret observations about the world around them to facilitate informed decisions.

1. Models

  • Our organizing framework is the model. What is a model? Why models?

2. Data

  • What is data and how do we get some? We start by defining data and our relationship with data. We will explore practical methods of data gathering – from surveys and archives to experiments and crowd sourcing.

  • Have data, how is it typically organized? We will explore basic methods of organizing data for robust, sustainable use.

3. Deploying Data

  • How can managers use data? We will deploy basic tools of cross tabulation, frequency distributions, and simple plots before some statistical inference and model building.

4. Artificial Intelligence

  • What is artificial intelligence (AI)? We will highlight general features of how commonly deployed AI systems function and how they can solve challenges across various areas of business.

Learning Objectives

Our key learning objectives, for the course, are:

  1. What is a model? What role do models play in the origination of data [for empiricists]?
  2. Distinguish among means of data collection and discuss their tradeoffs.
  3. Describe common models of data management and their tradeoffs.
  4. Articulate the applicability of popular analytic techniques to their uses and their function.
  5. Discuss the historical development and current functionality of AI.
  6. Ethical issues are ubiquitous in data, analytics, and AI in the workplace; we should confront them.

Our week 1 learning objective is to define a model, give examples of models and their use in management activities, and the interrelationship between models and measures, a lead-in to specifics on data.

Core Tools

We will make extensive use of a few software and computing tools.

  • Microsoft Word and Microsoft Excel [licenses provided via Willamette if needed]
  • Google Docs and Sheets
  • Google Gemini, in lieu of a subscription to OpenAI, Anthropic, or similar
  • NotebookLM

Readings

A free and open-source textbook will provide background and exercises in the generic field of statistics.

  • Open Intro Statistics. The .pdf file is available on canvas and a screen reader version can be obtained from the link.