AI and Business Analytics Technologies

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Департамент бизнес-информатики

Статус:

Курс обязательный

Когда читается:

3-й курс, 1, 2 модуль

Course Syllabus

Abstract

The course «Business Analytics and AI» equips students with the conceptual grounding and hands-on skills needed to lead data- and AI-informed decision-making in organizations. Students progress from the foundations of business analytics and data management through machine learning, BI and dashboard design, and advanced analytics techniques (segmentation, cohort analysis, process mining), before moving into a dedicated block on generative AI: large language models, prompt engineering, retrieval-augmented generation, and AI agents. The course closes with responsible-AI governance and a structured approach to implementing analytics/AI initiatives — CRISP-DM, ROI estimation, and organizational change management. Through individual assignments, a team presentation, and a capstone group project, students build a portfolio of applied analytics and AI deliverables — including a dashboard and a working LLM-based component — and develop the judgment required to scope, justify, and oversee analytics/AI projects as future business leaders.

Learning Objectives

• Understanding modern business analytics and AI technologies and their role in managerial decision-making
• Building practical skills in data management, exploratory data analysis, and dashboard design
• Developing competence in machine learning methods for business forecasting and classification tasks
• Mastering generative AI tools — prompt engineering, retrieval-augmented generation, and AI agents — for business applications
• Developing skills for the responsible and economically justified implementation of AI/analytics solutions in organizations

Expected Learning Outcomes

• Students will understand the fundamentals of business analytics and distinguish it from AI, Data Science, and Business Intelligence, including the descriptive–diagnostic–predictive–prescriptive maturity model
• Students will understand data-driven and AI-driven decision-making and their link to organizational performance, and describe decision support system (DSS) architecture
• Students will understand data architecture concepts (data warehouses, data marts, OLAP cubes) and distinguish ETL from ELT approaches
• Students will be able to write basic SQL queries and use Python (Pandas, NumPy) for data extraction, transformation and analysis
• Students will be able to perform data profiling, data cleaning and exploratory data analysis, and assess data quality across accuracy, completeness, consistency, timeliness and validity
• Students will understand the machine learning workflow and distinguish supervised from unsupervised learning, and describe its key algorithms (regression, classification, clustering, ensemble methods).
• Students will be able to validate and evaluate ML models using standard metrics, and recognize and mitigate overfitting and underfitting
• Students will understand principles of model interpretability and explainability (SHAP, LIME) and their business importance.
• Students will know the global and Russian BI-platform landscape, identify their respective advantages and constraints and be able to select an appropriate BI tool for a given analytical task.
• Students will be able to design dashboards applying core principles of hierarchy, KPI selection, chart-type selection, and data storytelling.
• Students will be able to apply customer segmentation methods (RFM, clustering) and cohort/retention analysis
• Students will understand process mining fundamentals and be able to identify bottlenecks and inefficiencies in business processes.
• Students will understand core AI concepts (machine learning, deep learning, generative AI) and describe the shift from discriminative to generative models.
• Students will understand the operating principles of large language models: tokenization, embeddings, pretraining, fine-tuning, and context windows.
• Students will be able to apply prompt-engineering techniques (zero-shot, few-shot, chain-of-thought, prompt chaining, structured-output prompting) to business tasks.
• Students will understand RAG architecture and be able to design a minimal retrieval-augmented pipeline grounded in company-specific data
• Students will understand AI agent architecture and principles of intelligent process automation, including human-in-the-loop design
• Students will understand the AI risk taxonomy (bias, hallucination, privacy, security) and principles of responsible AI governance
• Students will be able to apply the CRISP-DM methodology to structure an AI/analytics project and estimate ROI and unit economics of ML/AI investments.
• Students will be able to formulate business requirements for AI/analytics solutions and design an implementation and change-management plan for organizational adoption.

Course Contents

• Foundations of Business Analytics and Data-Driven Decision-Making
• Data Management: Architecture and Storage models, Data Quality, and Exploratory Data Analysis
• Machine Learning Foundations for Business Decisions
• Business Intelligence Systems and Dashboard Design
• Advanced Analytics: Segmentation, Cohort Analysis, and Process Mining
• Foundations of AI and the Rise of Generative AI
• Large Language Models: Operational Principles, Capabilities, and Limitations
• Prompt Engineering for Business Applications
• Retrieval-Augmented Generation and Enterprise Knowledge Systems
• AI Agents, Chatbots, and Intelligent Process Automation
• Responsible AI: Governance, Ethics, and Risk Management
• AI Implementation Strategy: CRISP-DM, ROI, and Organizational Change

Assessment Elements

• Attendance
  Attendance is assessed based on the percentage of classes (lectures, seminars) attended by the student.
• Tests
  Proctored online multiple-choice test (e.g., Start Exam). Test 1 delivered after Lecture 6 and covering Lectures 1–6: foundations of business analytics, data management (ETL/ELT, EDA, data quality), machine learning foundations, BI systems and dashboard design, advanced analytics (segmentation, cohort analysis, process mining), and foundations of AI/generative AI. Test 2 delivered after Lecture 12 and covering Lectures 7–12: large language models, prompt engineering, retrieval-augmented generation, AI agents and process automation, responsible AI governance, and AI implementation strategy (CRISP-DM, ROI, organizational change).
• Practical Assignments
  Individual or small-team (2–3 people) assignment: clean a provided business dataset, perform exploratory data analysis, and construct a 3–6 chart interactive dashboard (Power BI / Visiology / Yandex DataLens) with a KPI panel and filters.
• Team presentation
  Teams of 3–5 students deliver a 5-minute "AI Opportunity Scan" for a company of their choice, once LLMs, prompting, and RAG have all been taught (after Lecture 9): current analytics/AI maturity stage, one AI/GenAI use case grounded in a specific technique from Lectures 6–9, expected business impact, and a technically credible implementation plan; followed by 5 minutes of Q&A.
• Group Project
  Exam in the form of a team defense of the capstone project, held during the Lecture 12 session. Required materials: (1) the project report, submitted before the exam session; (2) a 10–12 slide presentation of project results; (3) a pre-recorded 3–5 minute video demonstrating the developed solution, submitted before the exam session. The grade depends on the quality of the submitted materials and on the quality of each team member's answers to questions.

Interim Assessment

• 2026/2027 2nd module
  0.1 * Attendance + 0.2 * Tests + 0.2 * Practical Assignments + 0.1 * Team presentation + 0.4 * Group Project

Bibliography

Recommended Core Bibliography

• Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. – " O'Reilly Media, Inc.", 2013.

Recommended Additional Bibliography

• Hall, M., Witten, Ian H., Frank, E. Data Mining: practical machine learning tools and techniques. – 2011. – 664 pp.
• Miroslav Kubat. An Introduction to Machine Learning. Springer, 2015 (296 pages) ISBN: 9783319200095: — Текст электронны // ЭБС books24x7 — https://library.books24x7.com/toc.aspx?bookid=117295

Authors

• ZHILTSOV EVGENII MIKHAILOVICH