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Regular version of the site
Master 2021/2022

Data and Analytics in Finance

Category 'Best Course for Career Development'
Type: Compulsory course
Area of studies: Finance and Credit
When: 1 year, 3, 4 module
Mode of studies: offline
Open to: students of all HSE University campuses
Master’s programme: Financial Strategies and Analitics
Language: English
ECTS credits: 6

Course Syllabus

Abstract

The course is aimed to provide students with the basic understanding of data analytics and machine learning concepts with regard to finance and practical implementation of these concepts by using programming software in order to provide organizations with data-driven solutions. The course begins with essentials of data collection and wrangling. The aim of this part is to teach students how to find, parse, import, manipulate and visualize financial data. The next part of the course provides students with research and analytical skills and covers such methods as principal component analysis, clustering, different techniques of curve fitting and LASSO regression. The final part of the course shows how machine learning methods can be applied to finance through the example of fraud detection. The course is based on real data from open sources and data on Russian and European public companies collected by International laboratory of intangible-driven economy NRU HSE and data on sales and customer analytics provided by laboratory GAMES NRU HSE. After completing the course students will be able to use data management techniques, to optimise asset portfolio, to provide customer analytics and detect fraud.
Learning Objectives

Learning Objectives

  • Work easily in R, import data in R, make basic manipulation with it to prepare data for calculations and export results of calculations.
  • Apply methods of data analysis and understand their objectives.
  • Understand limitation and relevance of the methods.
Expected Learning Outcomes

Expected Learning Outcomes

  • Apply skills in data cleaning.
  • Demonstrate the ability to work in different software environments for data analysis and to explain the choice of software.
  • Make decision in finance on base of data analysis and prove them.
  • Master ability of making decision on base of data analysis and proving them.
  • Understand basic theories in analysis of financial data, invent and write a code for a particular task in finance data analysis.
Course Contents

Course Contents

  • Data wrangling with R
  • Optimization problems on financial data
  • Fraud detection using machine learning
Assessment Elements

Assessment Elements

  • non-blocking Test 1
  • non-blocking Test 2
  • non-blocking Seminar activities
  • non-blocking Self-study students’ work
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.15 * Test 1 + 0.15 * Test 2 + 0.15 * Self-study students’ work + 0.15 * Seminar activities + 0.4 * Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Provost, F., & Fawcett, T. (2013). Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking (Vol. 1st ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=619895

Recommended Additional Bibliography

  • Tsay, R. S. (2013). An Introduction to Analysis of Financial Data with R. Wiley.