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Course Syllabus

Bachelor 2025/2026

AI and Business Analytics Technologies

Type: Compulsory course (Supply Chain Management and Business Analytics)
Delivered by: Department of Business Informatics
Where: Graduate School of Business
When: 3 year, 1, 2 module
Open to: students of one campus
Language: English
Contact hours: 40

Course Syllabus

Full SyllabusAsk Question

Abstract

This course develops practical skills in applying modern business analytics methods and artificial intelligence to logistics problems. Students will learn the fundamentals of digital culture and data management, master data preparation (ETL), methods of descriptive and predictive analytics using KNIME (Konstanz Information Miner), and the principles of dashboard design and data modeling using specific business examples and large language models (LLMs) with RAG approaches. Special attention is paid to data analytics leadership, implementation of new technologies (dashboards, chatbots), model management, and preparation of presentations and business recommendations. The training program combines theoretical lectures, practical seminars, and a team project. As a result, students build a portfolio of ready-made analytical solution prototypes and acquire the skills necessary for successful work in business.
Learning Objectives

Learning Objectives

  • Learning Objectives • Understanding modern AI technologies and their applications: learn the basic principles of AI tools, their capabilities, and application in logistics. • Knowledge of business analytics fundamentals: study key concepts and methods of business analytics, including data collection, processing, and analysis. • Gaining practical data handling skills: practice processing, visualizing, and analyzing data from various sources. • Applying BI tools: master the use of analytical tools to create reports and dashboards for informed decision-making. • Evaluating solution effectiveness: develop skills for assessing the effectiveness of business decisions based on analytical data.
Expected Learning Outcomes

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

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

Assessment Elements

  • non-blocking Tests 1 - 4
    10 test questions (2 open-ended questions, 1-2 practical tasks for calculation or construction)
  • non-blocking 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).
  • non-blocking 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.
  • non-blocking Group Project
Interim Assessment

Interim Assessment

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

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