Бизнес-аналитика и ИИ как инструмент эффективного управления

Магистратура 2025/2026

Статус: Курс обязательный (Международный менеджмент / Master in International Management)

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 1-й курс, 3 модуль

Охват аудитории: для своего кампуса

Преподаватели: Никивинце Ирина Сергеевна

Язык: английский

Кредиты: 3

Контактные часы: 24

Full Syllabus Ask Question

Abstract

The course covers both theoretical and practical aspects of business analytics (BA) and AI tools, in the context of real business problems. In today’s data-rich environment, businesses need analytics to remain competitive. The course emphasizes data analytics, a popular method for gaining insights from digital data. It also incorporates AI tools into business analysis. Assignments in the course teach students how to utilize open data sources and BI services for extracting, manipulating, analyzing, and visualizing data. The goal of the course is to assist students in making better decisions based on the data. Lectures, data analysis, case studies, discussions, and a group project are all part of the course. Lectures focus on the main theoretical concepts of BA, supported by supplementary reading materials. The group project allows students to practice BA techniques and tools using various data sources (datasets, online datasets, open datasets), data types (structured, semi-structured, unstructured data), and AI capabilities. Group project allows students to develop strategies for integrating AI into business processes, chatbots, applications, and consider the advantages of AI and ML for enhancing business performance. A case study based on real-world business data is included in the course.

Learning Objectives

The course is aimed at building skills in applying business analytics to real-world business and industrial problems.

Expected Learning Outcomes

Student applies business analytics to real-world business and industrial problems.
Student uses a variety of data sources and tools in business analytics process.
Student is able to find, extract, evaluate and prepare data for analysis.
Student creates clear visualizations of data and prepares presentation.
Student creates interactive dashboards and reports using business analytics tools.
Student selects appropriate business analytics tools and methods for solving business task.
Student interprets the findings based on business analytics.

Course Contents

Introduction to business analytics
Data sources and data preparation for business analytics
Analytical reporting and dashboards
Data mining and machine learning

Assessment Elements

Average (Test1, Test2, Test3, Test4)
Electronic test in SmartExam for 40 minutes.
Participation
Solving in-class assignments. Participating discussions.
Project
Students work in groups (usually up to 3). Project includes project presentation and defense (15 slides presentation). The analytical solution (a chatbot and a written ML report) must be submitted beforehand. Students carry out a project to apply the models and methods studied in the course to solve business problems. Students independently choose a subject area, form a problem statement, select the necessary data, perform analysis and interpret results.
AI Use Presentation
Prepare and present a team presentation (2-4 slides, 5 minutes of report + 5 minutes of Q&A) on the implementation of AI in a logistics organization: briefly describe the implementation object (company type and operational context), identify key business problems and solutions; attach a brief technical implementation plan (required resources).
Dashboard
Develop an interactive dashboard in Yandex DataLens /Visiology/Power BI, which will help analyze key business indicators and processes for making managerial decisions based on data. The dashboard should be clear, visually appealing, contain important information, 3-6 graphs, a panel with KPI metrics and several filters.

Interim Assessment

2025/2026 3rd module
0.07 * AI Use Presentation + 0.38 * Average (Test1, Test2, Test3, Test4) + 0.2 * Dashboard + 0.1 * Participation + 0.25 * Project

Bibliography

Recommended Core Bibliography

Doing statistical analysis : a student's guide to quantitative research, Thrane, C., 2023
Introduction to Statistics and Data Analysis, With Exercises, Solutions and Applications in R, Christian Heumann, Michael Schomaker, Shalabh, Springer Nature Switzerland AG 2022, 978-3-031-11833-3, published: 30 January 2023

Recommended Additional Bibliography

9781800206571 - Serg Masís - Interpretable Machine Learning with Python : Learn to Build Interpretable High-performance Models with Hands-on Real-world Examples - 2021 - Packt Publishing - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2901980 - nlebk - 2901980
Multi-criteria decision making : studies in systems, decision and control, Thakkar, J. J., 2021
Nesrine Ben Yahia, Jihen Hlel, & Ricardo Colomo-Palacios. (2021). From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction. IEEE Access, 9, 60447–60458. https://doi.org/10.1109/ACCESS.2021.3074559

Authors

Nikivintse Irina Sergeevna
REDKINA GALINA SERGEEVNA

Course Syllabus