1 to 3 credits
Fall 2026 Lecture Upper Division
This course offers an in-depth exploration of the principles and practices of Business Intelligence (BI) through computational methods. Students will delve into a wide array of topics, ranging from the fundamentals of BI to advanced data manipulation and the application of machine learning techniques in business contexts. The course emphasizes computational proficiency and practical utility. The methods taught are widely applicable across various functions and industries within the digital economy. A key aspect of digital transformation is the ability to make data-driven decisions and leverage artificial intelligence (AI) for business opportunities. While many firms can summarize their data, few possess the computational skills to extract meaningful insights and, if necessary, integrate additional data sources to derive comprehensive business intelligence. More importantly, firms often lack the capability to generate actionable insights. This course is highly hands-on, focusing on problem-solving and deriving insights from real-world data. Students are expected to actively participate through related readings, case discussions, in-class activities, and practical work with datasets, guided by lecture notes, readings, and assignments. The major concepts and techniques will be reinforced through a group project that involves providing data-driven insights into a real-world business problem. The course aims to bridge the gap between theoretical knowledge and practical application, preparing students to utilize computational methods for business intelligence in various professional settings.
Learning Outcomes1Grasp and apply the core principles of business intelligence.
2Utilize Python for effective web scraping and data manipulation.
3Implement various data modeling and machine learning techniques for business decision-making.
4Analyze and visualize data to inform business decisions.
5Utilize modern Large Language Models (LLMs) to assist in tasks such as sentiment analysis, code generation, and data exploration.
Restrictions
made by Purdue students.