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Data Analysis Techniques in Finance

Учебный год
Обучение ведется на английском языке
Курс по выбору
Когда читается:
1-й курс, 1, 2 модуль


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

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

Course Contents

  • Method and tools of financial data analysis
    Data use for communication: analyze, customize, visualize, communicate. Analytical financial reports and dashboards. Problem of data interpretation. Use of ERP and Excel in data analysis and forecasting.
  • Use of financial models in management
    Communicating strategic decisions through operational plans. KPIs as a driver of data structure and focus of modelling. Financial models for budgeting – constructing a data-based road map.
  • Data for financial and management reporting and performance analysis
    Principles of reporting and analysis. Financial structure, responsibility centers, segregation of duties. Performance evaluation using marginal approach and data segmentation. Object-based and process-based data analysis.
  • Unit-economics and major performance metrics
    Product and marketing metrics. Contribution. CVP analysis in short-term and investment decision-making. Data analysis in production versus platform-based services.
  • Data visualization techniques in creating BI reports
    Adding visualization items. Choice of an appropriate visualization type. Formatting and configuring visualizations. Application of slicing and filtering. Configuring the report page. Design for accessibility and automatic
  • Preparing the Data
    Getting data from different data sources. Identifying and connecting to a data source. Changing data source settings. Selecting a shared dataset or creating a local dataset. Choosing an appropriate query type. Identifying query performance issues and parameters. Profiling the data. Identifying data anomalies and examining data structures. Interrogating column properties and data statistics. Cleaning, transforming, and loading the data. Resolving inconsistencies, unexpected or null values, and data quality issues. Identifying and creating appropriate keys for joins. Evaluating and transforming column data types Applying data shape transformations to table structures. Combining queries. Configuring data loading. Resolving data import errors.
  • Modeling the Data
    Designing a data model. Defining the tables and configure table and column properties. Defining quick measures. Flattening out a parent-child hierarchy. Defining role-playing dimensions. Defining a relationship's cardinality and cross-filter direction. Designing the data model to meet performance requirements. Resolving many-to-many relationships. Creating a common date table. Choosing the appropriate level of data granularity. Developing a data model. Applying cross-filter direction and security filtering. Creating calculated tables. Creating hierarchies. Creating calculated columns. Creating measures by using DAX. Usage of DAX to build complex measures. Usage of CALCULATE to manipulate filters. Implement Time Intelligence using DAX. Replacing numeric columns with measures. Usage of basic statistical functions to enhance data. Removing unnecessary rows and columns. Identifying poorly performing measures, relationships, and visuals. Improving cardinality levels by changing data types. Improving cardinality levels through summarization. Creating and managing aggregation.
  • Analyzing the Data
    Enhancing reports to expose insights. Application of conditional formatting. Application of slicers and filters. Exploring of statistical summary. Performing advanced analysis. Usage of groupings and binnings Usage of the Key Influencers to explore dimensional variances. Usage of the decomposition tree visual to break down a measure.
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

  • Interim assessment (2 module)
    0.3 * Data Analysis Model in Power BI + 0.3 * Financial Model in Excel + 0.4 * test


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