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Regular version of the site

Data Analysis in Business

2021/2022
Academic Year
ENG
Instruction in English
5
ECTS credits
Delivered at:
Joint Department with SAS
Course type:
Elective course
When:
3 year, 3, 4 module

Instructors

Course Syllabus

Abstract

Data mining is increasingly being used in various sectors of the economy. Mathematical methods are being improved, new models and approaches for solving applied business problems are being developed. 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 mining 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 the analytical model, from the stage of requirements formation and data preparation to the stage of implementation and operation.
Learning Objectives

Learning Objectives

  • Getting an idea of the specifics of data analysis tasks in business, taking into account the specifics of different sectors of the economy, getting acquainted with specific examples of business tasks that use data analysis
  • Familiarity with specialized SAS software for solving tasks in the course.
Course Contents

Course Contents

  • Client analytics. Lecture 1.1. Introduction to Client and Online Analytics
  • Client analytics. Workshop 1.1. Introduction to Client and Online Analytics
  • Client analytics. Lecture 1.2. Building predictive models and data visualization
  • Client analytics. Workshop 1.2. Building predictive models and data visualization
  • Text analytics. Lecture 2.1. Introduction to Text Analytics
  • Text analytics. Workshop 2.1. Introduction to Text Analytics
  • Text analytics. Lecture 2.2. Building predictive models in text analytics
  • Text analytics. Workshop 2.2. Building predictive models in text analytics
  • Tasks of data analysis in retail chains of goods sales. Lecture 3.1. Introduction to the tasks of data analysis in retail. Demand forecasting
  • Tasks of data analysis in retail chains of goods sales. Workshop 3.1. Introduction to the tasks of data analysis in retail. Demand forecasting
  • Tasks of data analysis in retail chains of goods sales. Lecture 3.2. Descriptive analytics in Retail: clustering of stores, segmentation of goods, recovery of demand
  • Tasks of data analysis in retail chains of goods sales. Workshop 3.2. Descriptive analytics in Retail: clustering of stores, segmentation of goods, recovery of demand
  • Tasks of data analysis in retail chains of goods sales. Lecture 3.3. Tasks of optimizing stocks of goods in the retail network, price optimization, assortment optimization
  • Tasks of data analysis in retail chains of goods sales.Workshop 3.3. Tasks of optimizing stocks of goods in the retail network, price optimization, assortment optimization
  • Fundamentals of risk assessment. Lecture 4.1. Introduction to Credit risks
  • Fundamentals of risk assessment. Workshop 4.1. Introduction to Credit risks
  • Fundamentals of risk assessment. Lecture 4.2. Introduction to Market risks
  • Fundamentals of risk assessment. Workshop 4.2. Introduction to Market Risks
  • Fundamentals of risk assessment. Lecture 4.3. Model validation
  • Fundamentals of risk assessment. Workshop 4.3. Model validation
  • Modelos. Lecture 5. ModelOps - Operationalization of machine learning models
  • Modelos. Workshop 5. ModelOps - Operationalization of machine learning models
  • Presentation technique
  • Industry specifics and applied aspects of data analysis tasks
  • Team project
Assessment Elements

Assessment Elements

  • non-blocking Control work No. 1
  • non-blocking Control work No. 2
  • non-blocking Control work No. 3
  • non-blocking Exam
    The exam is conducted remotely in the format of a computer test. A link to the test will be sent to students before the exam begins.
  • non-blocking Practical project (Team project)
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.5 * Practical project (Team project) + 0.1 * Control work No. 1 + 0.1 * Control work No. 3 + 0.2 * Exam + 0.1 * Control work No. 2
Bibliography

Bibliography

Recommended Core Bibliography

  • Математика для экономистов, учебное пособие, 464 с., Красс, М. С., Чупрынов, Б.В., 2010
  • Математическая статистика : учеб. пособие для вузов, Ивченко, Г. И., 1992
  • Теория вероятностей и математическая статистика : учебник для вузов, Колемаев, В. А., 2003
  • Теория вероятностей и математическая статистика в задачах : более 360 задач и упражнений, Борзых, Д. А., 2020
  • Теория вероятностей и математическая статистика. Базовый курс с примерами и задачами : учебник для вузов, Кибзун, А. И., 2013

Recommended Additional Bibliography

  • Высшая математика для экономистов : учебник для вузов, Кремер, Н. Ш., 2004
  • Теория вероятностей и математическая статистика : учебник для вузов, Колемаев, В. А., 2000