ИИ и технологии бизнес-аналитики

Бакалавриат 2025/2026

ID 989277

Статус: Курс обязательный (Управление цепями поставок и бизнес-аналитика)

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 3-й курс, 1, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Никивинце Ирина Сергеевна

Язык: английский

Кредиты: 4

Контактные часы: 40

Full Syllabus Ask Question

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 modern trends in business analytics, such as big data, AI, cloud technologies, neural networks, and large language models (LLMs).
Students will master basic concepts and terms related to data analysis, distinguish between data types, and select relevant methods for collecting and processing source data depending on their type.
Students will be able to perform exploratory data analysis, calculate means, medians, standard deviations, visualize data using charts, and assess the quality of source data.
Students will be able to independently perform data cleaning, identify errors and anomalies.
Students will be able to define business analytics and distinguish its key tasks from those of systems analysis.
Students will understand the importance of analytics in the decision-making process and its impact on planning efficiency in logistics.
Students will be able to describe how artificial intelligence is changing approaches to business analytics, shifting from traditional methods to intelligent solutions.
They will understand key areas of AI application in analytics and be able to provide examples of successful real-world cases.
Students will be able to choose the most relevant tools for solving an analytical task.
Students will be able to more purposefully select different types of visualizations and apply them depending on analysis goals and data types.
Students will master the principles of designing effective dashboards for visualizing key performance indicators (KPIs) in logistics.
They will acquire practical skills in working with data visualization tools such as Yandex DataLens, Visiology, and other BI applications.
They will be able to use tools and software to form customer segments and visualize the results.
They will master cluster analysis techniques and other algorithms for segmenting customers by similar characteristics.
Students will be able to apply various customer segmentation methods.
Students will be able to extract useful insights from feedback analysis that can be used to improve products and services.

Course Contents

Lecture 1: Digital organizations, data culture and AI
Lecture 2: Fundamentals of AI, training paradigms and deployment patterns
Lecture 3: Automating statistical analysis and data preparation
Lecture 4: Data visualization and persuasive infographics
Lecture 5: Dashboard design principles and analytics automation
Lecture 6: Machine learning (part 1): Unsupervised machine learning methods
Lecture 7: Machine learning (part 2): Supervised machine learning methods
Lecture 8: Large Language Models: internals and analytical uses
Lecture 9: LLMs in practice: chatbots, process mining and RPA integration
Lecture 10: Advanced analyses, strategy, change management and deployment lessons

Assessment Elements

Tests 1 - 4
10 test questions (2 open-ended questions, 1-2 practical tasks for calculation or construction)
Presentation: AI Implementation
Prepare and deliver a team presentation (2-4 slides, 5 minutes presentation + 5 minutes Q&A) on implementing AI in a logistics organization: briefly describe the implementation object (company type and operational context), identify key business problems and the solution; attach a brief technical implementation plan (required resources).
Practical Assignment: Dashboard Creation
Develop an interactive dashboard in Yandex DataLens or Visiology that helps analyze key business indicators and processes for making data-driven management decisions. The dashboard should be clear, visually appealing, contain important information, 3-6 charts, a panel with KPI metrics, and several filters.
Group Project

Interim Assessment

2025/2026 2nd module
0.07 * Presentation: AI Implementation + 0.2 * Practical Assignment: Dashboard Creation + 0.25 * Group Project + 0.48 * Tests 1 - 4

Bibliography

Recommended Core Bibliography

9781000645279 - Noura Metawa, M. Kabir Hassan - Artificial Intelligence and Big Data for Financial Risk Management - 2023 - Routledge - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3344214 - nlebk - 3344214

Recommended Additional Bibliography

Spiliopoulou, M., Gesellschaft für Klassifikation, Schmidt-Thieme, L., & Janning, R. (2014). Data Analysis, Machine Learning and Knowledge Discovery. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=669270

Authors

Nikivintse Irina Sergeevna
Korotkin Boris Aleksandrovich

Course Syllabus