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Бакалавриат 2022/2023

Анализ данных в бизнесе

Статус: Курс по выбору (Прикладной анализ данных)
Направление: 01.03.02. Прикладная математика и информатика
Когда читается: 3-й курс, 3, 4 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 5
Контактные часы: 52

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

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.
Expected Learning Outcomes

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
Course Contents

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
  • The concept of validation of risk assessment models
  • Optimizing Marketing Campaigns to Increase Response and Increase Revenue.
Assessment Elements

Assessment Elements

  • non-blocking Home assignment 1
  • non-blocking Exam
    Written exam, 80 min, questions in the form of a multiple-choice test.
  • non-blocking Home assignment 2
  • non-blocking Home assignment 3
  • non-blocking Project
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.1*HA1 + 0.1*HA2 + 0.1*HA3 + 0.2*Exam + 0.5*Project
Bibliography

Bibliography

Recommended Core Bibliography

  • Advanced management accounting, Kaplan, R. S., 2014
  • Elements of financial risk management, Christoffersen, P. F., 2012
  • Моделирование цепи поставок, Шапиро, Дж., 2006

Recommended Additional Bibliography

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