AI Tools for Business Analytics

2026/2027

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Management

Course type:

Compulsory course

When:

2 year, 2, 3 module

Course Syllabus

Abstract

The course “AI Tools for Business Analytics” is designed to equip students with practical skills to leverage AI, BI, and no code/low code tools for business analytics in an international context, covering the full analytics lifecycle - from framing a business problem to generating insights and recommendations. Students will learn to collect and assess data, build dashboards, apply basic predictive analytics, and use generative AI for research and reporting, while critically evaluating data quality, tool limitations, and ethical risks. The course culminates in a team project where students deliver a concise AI assisted analytics report, integrating problem definition, evidence, outputs, recommendations, and implementation considerations.

Learning Objectives

• • To develop practical understanding of how AI can support business analytics and decision-making. • To teach students to move from a business problem to an analytical task, suitable data, tool output and recommendation. • To develop basic skills in using approved AI, BI and no-code/low-code analytics tools for applied business cases. • To develop critical judgement about data quality, tool limitations, risks, confidentiality and business relevance. • To prepare students to deliver a concise AI-assisted business analytics project report.

Expected Learning Outcomes

• Understand the role, limits and risks of AI-enabled business analytics in international business. Formulate a business problem as an analytical task with relevant questions, indicators and expected outputs. Collect, assess and prepare business data and evidence for applied analysis. Use approved analytics and AI tools to produce simple analytical outputs. Interpret analytical outputs and prepare a management recommendation. Work on an applied team project and produce a written report.

Course Contents

• 1. AI-enabled business analytics for international business decisions
• 2. Data and evidence for business analytics
• 3. Exploratory analytics, dashboards and interpretation
• 4. Simple predictive analytics and decision support
• 5. Generative AI-assisted business research and reporting
• 6. Responsible AI use, implementation and final project

Assessment Elements

• Homework 1: Business problem and data/evidence analysis
  Applied homework uploaded to LMS. Students formulate a business problem, select relevant data/evidence and prepare a basic analytical output.
• Classroom test
  Individual closed-book test in the classroom. Covers the analytics cycle, AI tool limitations, data quality, interpretation and responsible use.
• Homework 2: AI-assisted analytics output
  Applied homework uploaded to LMS. Students use approved AI/analytics tools to prepare an analytical memo, dashboard, structured report section or comparable practical output.
• Project report (exam)
  Written project report uploaded to LMS by the deadline. This component is the exam. The report presents an AI-assisted analytics solution for an international business problem.

Interim Assessment

• 2026/2027 3rd module
  0.25 * Homework 2: AI-assisted analytics output + 0.2 * Homework 1: Business problem and data/evidence analysis + 0.3 * Project report (exam) + 0.25 * Classroom test

Bibliography

Recommended Core Bibliography

• All-in on AI : how smart companies win big with artificial intelligence, Davenport, T. H., 2023
• Competing on analytics: the new science of winning, Davenport, T. H., 2007

Recommended Additional Bibliography

• The definitive guide to DAX : business intelligence for Microsoft Power BI, SQL server analysis services, and Excel, Russo, M., 2020

Authors

• Kaplun Mariia Nikitichna
• Orlova Ekaterina Dmitrievna