Data Analysis in Business

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Департамент больших данных и информационного поиска

Статус:

Курс обязательный

Когда читается:

3-й курс, 1, 2 модуль

Преподаватель

Титова Наталия Николаевна

Course Syllabus

Abstract

Data Analysis is increasingly being used in various sectors of the economy. Mathematical methods are being improved, new models and approaches are being developed to solve applied business problems. At the same time, the practical application of data mining methods in business requires specialized knowledge and skills. The purpose of this course is to review modern approaches, tools and methods of data analysis used in such applied areas as customer analytics, risk management and retail network organization. The training is based not only on the study of relevant mathematical models and algorithms, but also on the consideration of examples of their real application in these areas, which will allow students to study the entire life cycle of an analytical model, starting from the stage of requirements formation and data preparation and ending with the stage of implementation and operation.

Learning Objectives

• То get an idea about the features of data analysis tasks in business, taking into account the specifics of different sectors of the economy, to get acquainted with specific examples of business tasks that use data analysis, as well as to get acquainted with specialized software for solving these problems.
• Develop Expertise in Risk Management Analytics: Equip students with the knowledge and tools to identify, assess, and mitigate risks through advanced data analysis techniques, including credit risk modeling, fraud detection, and operational risk assessment.
• Master Text Analytics for Business Applications: Provide students with the skills to extract insights from unstructured text data using natural language processing (NLP) methods, enabling applications such as sentiment analysis, customer feedback evaluation, and automated document classification.
• Enhance Customer Analytics Capabilities: Train students to analyze customer behavior and preferences using segmentation, churn prediction, lifetime value modeling, and personalization techniques to optimize customer engagement and retention strategies.
• Optimize Retail Network Organization: Teach students how to use data-driven approaches to optimize product assortment, inventory management, and store location planning, ensuring efficient retail network operations and improved profitability.
• Implement End-to-End Analytical Solutions: Guide students through the entire lifecycle of analytical models in applied business contexts, from defining requirements and preparing data to deploying models and evaluating their performance in real-world scenarios.

Expected Learning Outcomes

• Know key performance indicators and main metrics of operational and financial activities used in different sectors of the economy, industry and functional specifics of the implementation of data analysis tasks in business.
• Know the basic formulations, features and characteristics of applied problems of data analysis in business, arising in the field of client analytics, in retail sales networks of goods and in risk analysis and assessment.
• Know mathematical methods and models for solving data analysis problems in business that arise in the field of customer analytics, in retail sales networks of goods and in risk analysis and assessment, principles for verifying and presenting the result of solving these problems.
• Be able to formulate, solve and evaluate the result of solving data analysis problems in business that arise in the field of customer analytics, in retail chains of goods sales and in risk analysis and assessment, and in other sectors of the economy.
• Be able to use software tools for loading, processing, visualizing and interactive data exploration, as well as building and applying descriptive and predictive data mining and machine learning models
• Be able to prepare and present your results in the form of a business presentation
• Identify key business tasks that can be addressed using text classification and information extraction techniques.
• Analyze examples of unstructured text data, such as reports, user reviews, and news articles, to understand their relevance to specific industries.
• Recognize industries where text data analysis tasks are commonly applied and explain their significance in solving business challenges.
• Differentiate between various text analysis methods, such as classification and fact extraction, based on their use cases.
• Evaluate the potential impact of text data analysis on improving decision-making processes in different industries.
• Analyze various methods of text processing and knowledge extraction from unstructured data.
• Evaluate the capabilities and limitations of different text analytics tools and software products.
• Demonstrate the use of Python libraries for text processing tasks, such as tokenization, stemming, and sentiment analysis.
• Apply free software solutions to solve practical problems in text analytics.
• Develop skills to compare and select appropriate tools and methods for specific text analytics projects.
• Analyze the structure of retail companies and their role within the supply chain.
• Identify and interpret key KPIs of retail companies, including logistics-related metrics.
• Apply demand forecasting techniques to support various business processes in retail.
• Develop strategies for clustering stores, optimizing inventory, and pricing based on demand forecasts.
• Evaluate the impact of demand forecasting on overall retail performance and decision-making processes.
• Understand the concept of price optimization and its importance in retail decision-making processes.
• Analyze demand elasticity models and identify key causal variables affecting demand elasticity.
• Apply machine learning techniques to formulate and solve promo forecasting problems in retail networks.
• Evaluate business constraints and limitations in price optimization strategies and propose feasible solutions.
• Develop actionable insights from store clustering and product segmentation to improve demand recovery strategies.
• Define the problem of assortment optimization and formulate it as a machine learning problem.
• Identify key constraints in determining the product assortment for retail outlets.
• Analyze the main strategies for stock optimization in a retail network, including safety stock (ss) and base stock (bs) approaches.
• Explain the multi-echelon inventory optimization approach and the whiplash (bullwhip) effect in supply chain management.
• Apply learned concepts to propose solutions for optimizing stock levels, pricing, and assortment in a retail network.
• Identify different types of credit risk and explain their relevance in credit risk management.
• Analyze various types of data used for credit analysis and demonstrate the ability to preprocess data, including filtering, gap filling, and addressing errors such as "survivor error."
• Evaluate the requirements for credit analysis models, including accuracy, stability, and transparency, and discuss their importance in risk assessment.
• Apply different modeling approaches (e.g., generalized linear models, decision trees, default intensity models) to credit risk analysis while incorporating macroeconomic and external factors.
• Design a portfolio-based approach to credit risk management that integrates external factors and supports informed decision-making.
• Understand the key concepts and definitions related to market risk, including its significance in financial markets.
• Apply various methods for assessing Value-at-Risk (VaR), such as delta-normal, historical simulation, and model-based approaches.
• Analyze portfolio risk using fundamental principles of portfolio arithmetic and assess the impact of diversification.
• Evaluate market risk using advanced models, including factor models, GARCH, and techniques for handling heavy-tailed distributions.
• Interpret the results of market risk assessments and propose strategies for mitigating potential risks in portfolios.
• Analyze the concept and development of predictive models to optimize marketing campaigns.
• Evaluate business constraints and their impact on campaign profitability using sensitivity analysis.
• Apply optimization techniques to improve campaign management processes.
• Demonstrate the relationship between resource availability and profit through data-driven insights.
• Develop actionable strategies for increasing campaign response rates and revenue growth.

Course Contents

• Introduction to client and online analytics
• Building predictive models and visualizing data
• Introduction to text data analysis tasks
• Tools and methods of text analytics.
• Introduction to the tasks of data analysis in retail. Demand Forecasting
• Descriptive analytics in Retail: store clustering, product segmentation, demand recovery
• Problems of optimizing stocks of goods in a retail network, price optimization, assortment optimization
• Introduction: the role of risk assessment in risk management Understanding credit risk
• The concept of market risk
• Optimizing Marketing Campaigns to Increase Response and Increase Revenue.
  Преподаватель Глазков Артем Александрович
• Logistics and Supply Chain
• Machine Learning in Manufacturing: Task Types and Examples
• Designing an ML solution for an enterprise
• Role of AI Product Manager. Optimization Tasks in Banking: Case Studies on ATM Cash Collection Automation and Task Assignment
• AI Assistants and Agents: Applications in Banking
• Product Recommendation Tasks: How Target Metrics Shape Approaches

Assessment Elements

• home work
• project
• Final certification - in-person exam
• Test in class
  In the classroom, at the end of the lesson, texts will be given during the lecture to check the material learned

Interim Assessment

• 2025/2026 2nd module
  0.2 * Final certification - in-person exam + 0.1 * Test in class + 0.4 * home work + 0.3 * project

Bibliography

Recommended Core Bibliography

• Advanced management accounting, Kaplan, R. S., 2014
• Alexander J. McNeil, Rüdiger Frey, & Paul Embrechts. (2015). Quantitative Risk Management: Concepts, Techniques and Tools Revised edition. Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.pup.pbooks.10496
• An introduction to management science : quantitative approaches to decision making, , 2017
• Carol Alexander. Market Risk Analysis: Value-at-Risk Models, Volume IV. John Wiley & Sons, 2009.
• Dipanjan Sarkar. (2019). Text Analytics with Python : A Practitioner’s Guide to Natural Language Processing: Vol. Second edition. Apress.
• Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.
• Hull, J. (2018). Risk Management and Financial Institutions (Vol. Fifth edition). Hoboken, NewJersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1733295
• Interest rate analysis and forecasting, Kern, D., 1992
• M Narasimha Murty, & V Susheela Devi. (2015). Introduction To Pattern Recognition And Machine Learning. World Scientific.
• Marketing engineering : computer-assisted marketing analysis and planning, Lilien, G.L., 1998
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
• Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Vol. Second edition). Hoboken, New Jersey: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=985114
• Montgomery, D. C., Vining, G. G., & Peck, E. A. (2012). Introduction to Linear Regression Analysis (Vol. 5th ed). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1021709
• Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
• Quantitative risk management: concepts, techniques and tools, McNeil, A. J., 2015
• Sarkar, D. Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data [Электронный ресурс] / Dipanjan Sarkar; БД Books 24x7. – Chicago: Apress, 2016. – 412 p. – ISBN 978-1-4842-2387-1
• Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data Mining for Business Analytics : Concepts, Techniques and Applications in Python. Newark: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2273611
• Solé, J. L. (2007). Book review: Pattern recognition and machine learning. Cristopher M. Bishop. Information Science and Statistics. Springer 2006, 738 pages.
• Text analytics : an introduction to the science and applications of unstructured information analysis, Atkinson-Abutridy, J., 2022
• Аналитика : методология, технология и организация информационно- аналитической работы, Курносов, Ю. В., 2004
• Аналитика веб-сайтов, Гусев, В.С., 2008
• Введение в управление кредитными рисками : пер. с англ., , 1994
• Веб-аналитика, Яковлев, А., Довжиков, А., 2010
• Джафаров, К. А. Методы оптимальных решений. Задачи управления запасами, очередью и конфликтами : учебное пособие / К. А. Джафаров, Л. В. Роева. - Новосибирск : Изд-во НГТУ, 2018. - 112 с. - ISBN 978-5-7782-3747-6. - Текст : электронный. - URL: https://znanium.com/catalog/product/1870008
• Куценко, Е. И. Системы управления запасами в цепях поставок : учебное пособие / Е. И. Куценко. — Оренбург : ОГУ, 2019. — 143 с. — ISBN 978-5-7410-2137-8. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/159884 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
• Логистика и управление цепями поставок - взгляд в будущее : макроэкономический аспект, Проценко, О. Д., 2012
• Логистика и управление цепями поставок : учебник и практикум для академич. бакалавриата, Лукинский В.С., Лукинский В.В., 2017
• Логистика и управление цепями поставок : Учебник и практикум для академического бакалавриата, Лукинский, В. С., 2016
• Муртазина, Э. И. Logistics and Supplyhain Management (Логистика и управление цепями поставок) : учебное пособие / Э. И. Муртазина, Э. З. Фахрутдинова. — Казань : КНИТУ, 2013. — 168 с. — ISBN 978-5-7882-1434-4. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/73202 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
• Поиск в Интернете и сервисы Яндекс, Холмогоров, В., 2006
• Прибыльная контекстная реклама : быстрый способ привлечения клиентов с помощью Яндекс.Директа, Смирнов, В. В., 2013
• Тюхтина, А. А. Модели управления запасами : учебно-методическое пособие / А. А. Тюхтина. — Нижний Новгород : ННГУ им. Н. И. Лобачевского, 2017. — 84 с. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/153175 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
• Управление запасами в цепях поставок : учебник и практикум для вузов — Москва : Издательство Юрайт, 2024. — 625 с. — (Высшее образование). — ISBN 978-5-534-18478-5. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/535114 (дата обращения: 04.07.2025).
• Управление запасами: многофакторная оптимизация процесса поставок : учебник для среднего профессионального образования / Г. Л. Бродецкий, В. Д. Герами, А. В. Колик, И. Г. Шидловский. — Москва : Издательство Юрайт, 2023. — 322 с. — (Профессиональное образование). — ISBN 978-5-534-10776-0. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/517345 (дата обращения: 04.07.2025).
• Учирова, М. Ю., Организация системы управления запасами торогового предприятия : монография / М. Ю. Учирова. — Москва : Русайнс, 2023. — 99 с. — ISBN 978-5-466-03266-6. — URL: https://book.ru/book/949898 (дата обращения: 04.07.2025). — Текст : электронный.

Recommended Additional Bibliography

• Математическая статистика, Ивченко, Г. И., 1990

Authors

• Titova Nataliya Nikolaevna
• Dimova Elena Anatolevna