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Regular version of the site

Data Analysis Techniques in Finance

Academic Year
Instruction in English
ECTS credits
Course type:
Elective course
2 year, 1, 2 module


Smirnova, Elena

Course Syllabus


The course focuses on the tools of data analysis and visualization used for financial analysis, financial modeling and investment analysis. In the end of the course, students will be able to apply Excel and Power BI for different purposes in the area of Finance. The course has a practical nature and is based on real-life cases.
Learning Objectives

Learning Objectives

  • Ability to construct a data model, create financial dashboard from scratch based on a business case for getting insights and performing analysis.
Expected Learning Outcomes

Expected Learning Outcomes

  • Cleaning, transforming, and loading the data.
  • Ability to construct financial models
  • Analyzing Financial Data for Solving Business Problems
  • Business performance evaluation
  • Creating BI reports. Configuring the report page.
  • Defining the tables and configure table and column properties. Defining quick measures.
  • Understanding data value cycle
  • Understanding principles of Data-Driven Decision Making
Course Contents

Course Contents

  • Method and tools of financial data analysis
  • Use of financial models in management
  • Data for financial and management reporting and performance analysis
  • Unit-economics and major performance metrics
  • Data visualization techniques in creating BI reports
  • Preparing the Data
  • Modeling the Data
  • Analyzing the Data
Assessment Elements

Assessment Elements

  • non-blocking Data Analysis Model in Power BI
  • non-blocking Financial Model in Excel
  • non-blocking test
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.3 * Data Analysis Model in Power BI + 0.4 * test + 0.3 * Financial Model in Excel


Recommended Core Bibliography

  • Adam Aspin. (2020). Pro Power BI Desktop : Self-Service Analytics and Data Visualization for the Power User: Vol. Third edition. Apress.
  • Aspin A. Pro Power BI Desktop. - Apress, 2018. - ЭБС Books 24x7.
  • Clark, D. (2017). Beginning Power BI : A Practical Guide to Self-Service Data Analytics with Excel 2016 and Power BI Desktop (Vol. Second edition). Camp Hill, Pennsylvania: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1478775
  • Data analysis and decision making with Microsoft Excel, Albright, S. Ch., 2009
  • Decision modeling with Microsoft Excel, Moore, J., 2001
  • Greg Deckler. (2019). Learn Power BI : A Beginner’s Guide to Developing Interactive Business Intelligence Solutions Using Microsoft Power BI. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2252653
  • Russo, M., & Ferrari, A. (2015). The Definitive Guide to DAX : Business Intelligence with Microsoft Excel, SQL Server Analysis Services, and Power BI. Redmond, Washington: Microsoft Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1601522
  • Seamark, P. (2018). Beginning DAX with Power BI : The SQL Pro’s Guide to Better Business Intelligence. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1743806
  • The definitive guide to DAX : business intelligence for Microsoft Power BI, SQL server analysis services, and Excel, Russo, M., 2020

Recommended Additional Bibliography

  • Andros, S. V., & Chang Shichao. (2019). Information Technologies as the Basis of Innovative Development of Enterprises. Economics: Time Realities, 5, 16–25. https://doi.org/10.5281/zenodo.3766794
  • Anup Maheshwari. (2019). Digital Transformation : Building Intelligent Enterprises. Wiley.
  • Data analysis and decision making with Microsoft Excel, Albright, S. Ch., 2006
  • Data analysis for managers with Microsoft Excel, Albright, S. Ch., 2004
  • Quantitative finance: a simulation-based introduction using excel, Davison, M., 2014
  • Rackley, J. (2015). Marketing Analytics Roadmap : Methods, Metrics, and Tools. [Berkley]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1000698