2026/2027
Business Analytics & AI as a Tool for Effective Management
Type:
Mago-Lego
Delivered by:
Department of Business Informatics
When:
1, 2 module
Open to:
students of one campus
Language:
English
ECTS credits:
6
Contact hours:
48
Course Syllabus
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
- Посещаемость
- Аудиторная работа: домашнее задание
- Групповой проект
- Экзамен: защита проекта и ответы на вопросы
Interim Assessment
- 2026/2027 2nd module0.1 * Посещаемость + 0.2 * Аудиторная работа: домашнее задание + 0.4 * Экзамен: защита проекта и ответы на вопросы + 0.3 * Групповой проект
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