This course provides an introduction to the concepts and applications of computational intelligence (CI) in the fields of business, economics, and finance. Students will explore various computational techniques such as artificial intelligence (AI), machine learning (ML), and data analytics, and how these techniques are applied to decision-making, strategy development, and optimization in business and finance. The course will emphasize practical applications in digital business, e-commerce, and economic decision-making, with examples and case studies drawn from real-world scenarios.

Prerequisites:
Introductory courses in Business or Economics
Basic knowledge of statistics and data analysis
Familiarity with digital technologies and e-commerce platforms is helpful but not mandatory.


Syllabus: Computational Intelligence in Business, Economics, and Finance
Course Title:
Computational Intelligence in Business, Economics, and Finance

Course Code:
To be determined (TBD)

Course Credits:
3 credits

Course Duration:
14 weeks (1 semester)

Level:
Undergraduate

Instructor:
Instructor Name: TBD
Email: TBD
Office Hours: TBD
Course Description:
This course provides an introduction to the concepts and applications of computational intelligence (CI) in the fields of business, economics, and finance. Students will explore various computational techniques such as artificial intelligence (AI), machine learning (ML), and data analytics, and how these techniques are applied to decision-making, strategy development, and optimization in business and finance. The course will emphasize practical applications in digital business, e-commerce, and economic decision-making, with examples and case studies drawn from real-world scenarios.

The primary textbook for the course is "Digital Business and E-Commerce Management" (7th Edition) by Dave Chaffey, Tanya Hemphill, and David Edmundson-Bird, which provides the foundation for digital business concepts that will be integrated with computational intelligence.

Prerequisites:
Introductory courses in Business or Economics
Basic knowledge of statistics and data analysis
Familiarity with digital technologies and e-commerce platforms is helpful but not mandatory
Course Objectives:
By the end of this course, students will:

Understand the fundamental concepts of computational intelligence and its applications in business, economics, and finance.
Analyze how machine learning and AI can be used to optimize business operations, financial forecasting, and economic decision-making.
Apply computational models to real-world business problems such as customer segmentation, financial risk assessment, and market analysis.
Explore the role of e-commerce platforms and digital business management strategies in modern economic environments.
Develop practical skills in using computational tools for data analysis, predictive modelling, and optimization in business and finance.

Textbook:

Main Textbook:

Chaffey, D., Hemphill, T., & Edmundson-Bird, D. (2020). Digital Business and E-Commerce Management (7th Edition). Pearson Education. Available online.

Supplementary Texts/Readings:

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th Edition). Pearson.
Hastie, T., Tibshirani, R., & Friedman, J. (2017). The Elements of Statistical Learning. Springer.