Analytical tools for enterprise financial management

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Finance

Course type:

Compulsory course

When:

1 year, 4 module

Instructors

Lambert, Jerome

Nazarova, Varvara

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

• Analyse financial and non-financial statements to select metrics for building the financial model.
• Apply Regression Tools to make Predictions, and improve Forecasts with Multiple Regression.
• 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. Crash course into Pandas and Numpy
• Lecture 7. Effective Pandas.
• Lecture 8. Data visualization in Python.

Assessment Elements

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

Interim Assessment

• 2024/2025 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
• Churakova Iiia Iurevna