Data Management

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент бизнес-информатики и операционного менеджмента

Статус:

Курс по выбору

Когда читается:

1-й курс, 2 модуль

Преподаватель

Сичинский Антон Евгеньевич

Course Syllabus

Abstract

Managers need to be properly informed in order to make effective business decisions. In modern organizations, data is often distributed across multiple systems, departments, and formats, making it difficult to access, interpret, and use efficiently. Moreover, the lack of proper data management practices leads to low data quality, inconsistent definitions, and limited trust in analytical results. This course introduces students to the principles and practices of data management and demonstrates how data can be transformed into actionable insights using modern self-service tools. The course covers the full data lifecycle — from data collection and preparation to analysis and visualization — with a strong emphasis on practical skills.Students will learn how to work with raw and heterogeneous data, apply data transformation techniques, and build analytical data marts using a low-code approach in PolyAnalyst GRID. They will also gain practical experience working with relational databases, including designing data integrations, developing physical data storage models, and implementing them in PostgreSQL. The course also focuses on data visualization and dashboard design using Yandex DataLens, enabling students to present insights in a clear and business-oriented way.

Learning Objectives

• • Provide students with a comprehensive understanding of data management principles and frameworks
• • Develop practical skills in data preparation and transformation using self-service tools
• • Introduce core concepts of data governance, data quality, and data lifecycle
• • Enable students to perform exploratory data analysis and generate insights
• • Teach principles of effective data visualization and dashboard design
• • Prepare students to work with data in real-world business scenarios

Expected Learning Outcomes

• · Understand the role and value of data management in organizations
• · Explain the difference between data, information, and knowledge (DIKW concept)
• · Understand the data lifecycle and key components of data management
• · Identify roles and responsibilities in data governance (Data Owner, Data Steward, etc.)
• · Assess data quality and apply basic data cleaning techniques
• · Work with structured and unstructured datasets from multiple sources
• · Understand the basic principles of relational databases and data storage
• · Design logical and physical data models for analytical tasks
• · Create and manage database objects in PostgreSQL
• · Use SQL to retrieve, filter, join, aggregate, and transform data
• · Design and implement basic data integrations between source systems and databases
• · Load and transform data from external sources into a database
• · Perform data transformations using low-code tools
• · Build analytical datasets (data marts) for business analysis
• · Conduct exploratory data analysis and identify patterns, trends, and anomalies
• · Design and create dashboards using BI tools
• · Build an end-to-end analytical workflow from source data and database storage to analytical data marts and dashboards
• · Present analytical results and support business decision-making with data

Course Contents

• Introduction to Data Management and Data Analysis
• Data Management Processes, Lifecycle, and Governance
• Databases, Data Modeling, and Data Integration
• Business Intelligence and Data Visualization Principles
• Practical Module: Data Integration, Preparation, and Visualization
• Final Project (Presentation and Defense)

Assessment Elements

• Quiz 1
  Quiz 1 contains questions related to the topic “Data Management Fundamentals and Governance”
• Quiz 2
  contains questions related to the topic “Data Visualization and BI Principles”
• Final project
  Final Project is a team-based project (students work in teams of 4-5 ). The objective is to perform an end-to-end analytical workflow — from raw data to actionable business insights — and to present the results in a final project presentation session.

Interim Assessment

• 2026/2027 2nd module
  0.5 * Final project + 0.25 * Quiz 2 + 0.25 * Quiz 1

Bibliography

Recommended Core Bibliography

• DAMA-DMBOK : data management body of knowledge, , 2017
• John Ladley. (2020). Data Governance : How to Design, Deploy, and Sustain an Effective Data Governance Program: Vol. Second edition. Academic Press.

Recommended Additional Bibliography

• The Data warehouse lifecycle toolkit : expert methods for designing, developing, and deploying data warehouses, Kimball, R., 1998

Authors

• Orlova Ekaterina Dmitrievna