Бакалавриат
2026/2027
ИИ и технологии бизнес-аналитики
ID 1209346
Статус:
Курс обязательный (Международный бизнес)
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
3-й курс, 2 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
36
Course Syllabus
Abstract
The course provides a foundation and rationale for informed business decision-making. Modern AI-based analytics platforms enable managers and entrepreneurs to work independently with data sources, process and analyse data, and use scenario modelling and hypothesis testing to support decisions, including strategic decisions.
The course examines approaches and intelligent technologies suitable for both individual and collaborative use in addressing a range of tasks through business analytics methods and AI technologies.
Learning Objectives
- Develop the competencies needed to solve business problems independently using modern business analytics and AI technologies.
- Acquire the skills needed to formulate tasks for business analysts and data scientists working on projects to implement intelligent technologies.
Expected Learning Outcomes
- Apply machine learning to data analysis problems.
- Identify the main stages of introducing intelligent analytical systems into a company’s business processes, taking account of business requirements and the intelligent technologies used.
- Develop functional requirements for an AI-based analytical application, taking account of the business problem and the availability and quality of data.
- Select and apply appropriate analytical technologies and machine learning methods to solve business problems.
- Understand the classification and main capabilities of business analytics and AI methods and tools used in management and business administration.
- Have a comprehensive understanding of global developments in business analytics and intelligent technologies as tools for supporting management decisions across their principal business application areas.
- Understand how business analytics methods and tools can be applied under uncertainty.
- Define the scope of a business analyst’s responsibilities and all key roles in a data science project.
- Understand and formulate business requirements for an analytical model and its supporting AI technologies.
- Know the main stages of the CRISP-DM methodology and formulate the steps of an analytics project in its terms.
- Use basic data analytics tools, including exploratory analysis, predictive models and machine learning methods.
- Demonstrate basic skills in developing dashboards in business intelligence systems.
- Know the main methods of visual analysis and descriptive analytics technologies based on business intelligence systems.
- Understand the market, functionality and development trends of business intelligence systems.
- Know basic machine learning methods and the principles of building machine learning models with them.
- Understand the operating principles of generative models and neural networks for business.
- Use the main concepts and terminology of artificial intelligence.
- Have a basic understanding of chatbot applications and prompt engineering for generative networks.
Course Contents
- AI and Business Analytics: From Business Problem to Product
- Data, Metrics, and Exploratory Data Analysis
- How Machine Learning Works: Core Concepts and Models
- Evaluating and Improving ML Models
- Neural Networks: Principles and Applications
- Beyond Model Development: Experimentation, Deployment, and MLOps
Assessment Elements
- HomeworksAt least two homework assignments are expected. In teams of two to five, students complete homework and other practical assignments using machine learning studios and other business data analysis tools.
- Project ReportThe project report is a written document prepared by project teams on the basis of a project brief provided in advance by the instructor. It is the final component of ongoing and milestone assessment and must be submitted no later than one working week before the examination period. Each project team may consist of two to five students.
- Team Project DefenceThe examination is an oral assessment in the form of a project defence by previously registered teams. Each project team may consist of two to five students. The project includes exploratory data analysis, an ML model with three improvement iterations, and an interactive DataLens dashboard for monitoring operational metrics.
- Class Attendance
Interim Assessment
- 2026/2027 2nd module0.4 * Team Project Defence + 0.4 * Homeworks + 0.1 * Project Report + 0.1 * Class Attendance
Bibliography
Recommended Core Bibliography
- Neural Networks and Deep Learning - Michael Nielson - Open Educational Resources: libretexts.org - CCBY4_068 - 2022 - русский - https://ibooks.ru/product/336053 - iBOOKS - 336053
- Python и анализ данных. Первичная обработка данных с применением pandas, NumPy и Jupiter - Маккинни У. - Издательство "ДМК Пресс" - 978-5-93700-174-0 - 2023 - русский - https://e.lanbook.com/book/348086 - ЛАНЬ - 348086
- Миркин, Б. Г. Базовые методы анализа данных : учебник и практикум для вузов / Б. Г. Миркин. — 3-е изд., перераб. и доп. — Москва : Издательство Юрайт, 2026. — 297 с. — (Высшее образование). — ISBN 978-5-534-19709-9. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/583143 (дата обращения: 02.07.2026).
Recommended Additional Bibliography
- Анализ и визуализация данных в Yandex DataLens. Подробное руководство: от новичка до эксперта - Гинько А. Ю. - Издательство "ДМК Пресс" - 978-5-93700-171-9 - 2022 - русский - https://e.lanbook.com/book/314909 - ЛАНЬ - 314909
- Визуализация данных при помощи дашбордов и отчетов в Excel - Куслейка Д. - Издательство "ДМК Пресс" - 978-5-97060-966-8 - 2022 - русский - https://e.lanbook.com/book/241169 - ЛАНЬ - 241169
- Искусственный интеллект, аналитика и новые технологии - (HBR) Коллектив авторов; А. Барабанщиков - Альпина Паблишер - 9785961412697 - 2022 - русский - https://hse.alpinadigital.ru/audio/23602 - Alpina - 23602
- Ключевые инструменты бизнес-аналитики. 67 инструментов, которые должен знать каждый менеджер - Марр Б. - Издательство "Лаборатория знаний" - 978-5-00101-962-6 - 2022 - русский - https://e.lanbook.com/book/249944 - ЛАНЬ - 249944
- Полковникова Н.А. - Анализ и визуализация данных в Microsoft Excel в примерах и задачах - 978-5-9729-1485-2 - Инфра-Инженерия - 2023 - https://znanium.ru/catalog/document?id=432959 - 432959 - ZNANIUM