Analytical Tools for Enterprise Financial Management

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент финансов

Статус:

Курс обязательный

Когда читается:

1-й курс, 4 модуль

Course Syllabus

Abstract

The purpose of the discipline is to form the student's knowledge of analytical methods of tools in the management of enterprise finances. As part of the course, students will study decision-making methods in various functional areas of enterprise finance, as well as methods and algorithms for quantitative assessments of the processes of providing enterprise activities with financial resources. The main goal of the course is to familiarize with modern approaches to modeling and optimization of financial operations and functions, approaches to the problem of making economically sound decisions in conditions of uncertainty.

Learning Objectives

• Introduction to relational database management systems with MS SQL Server as an example
• Learning solid basis of SQL querying
• Exploring Window Functions
• Acquiring bases in Pandas and Numpy for data transformation, including Effective Pandas, as well as simple data visualization

Expected Learning Outcomes

• Ability to write SQL queries in a relational DBMS

Course Contents

• Lecture 1. MS SQL Server and SQL foundations.
• Lecture 2. Single-Table Queries, Data types and built-in functions
• Lecture 3. Table Joins and Set operators.
• Lecture 4. Subqueries and table expressions.
• Lecture 5. Window functions.
• Lecture 6. Data modification, transactions and concurrency
• Lecture 7. Query optimization and indexing.
• Lecture 8. T-SQL programming fundamentals: stored procedures and functions.

Assessment Elements

• Final Examination
• In class test 2
• In class test 1

Interim Assessment

• 2025/2026 4th module
  0.5 * Final Examination + 0.25 * In class test 1 + 0.25 * In class test 2

Bibliography

Recommended Core Bibliography

• McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822
• 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

Recommended Additional Bibliography

• Date, C. J. (2015). SQL and Relational Theory : How to Write Accurate SQL Code (Vol. Third edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1099367

Authors

• SOLOVEVA EKATERINA EVGENEVNA