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Course Syllabus

Bachelor 2023/2024

Statistical Data Analysis

Type: Elective course (International Programme in Economics and Finance)
Area of studies: Economics
Delivered by: International College of Economics and Finance
Where: International College of Economics and Finance
When: 3 year, 3, 4 module
Mode of studies: offline
Open to: students of one campus
Language: English
ECTS credits: 4
Contact hours: 56

Course Syllabus

Full Syllabus

Abstract

Statistical data analysis is a one-semester course which is taught for 3rd-year ICEF BSc students at modules 3, 4. The course focuses on practical aspects and applications of probability theory, statistics and mathematical finance. The first part of the course covers classic mathematical models of financial markets, pricing of derivatives via analytical method, partial differential equations (PDE) and Monte-Carlo simulations. Second part of the course focuses in recent developments and trends such as basics of machine learning, neural networks and blockchain. Inclass activity and problem solving are primarily done by programming in Python, special attention will be paid to developing object-oriented approach (OOP). Problem solving sessions will also be included to work out theoretical material from lectures.
Learning Objectives

Learning Objectives

  • Familiarise students with contemporary data analysis methods and tools.
  • Give introduction to modern areas where statistical analysis can be applied.
  • Learn how to use programming language (Python) to analyse the data.
  • Give necessary knowledge to allow students to further study statistical methods which are available at modern software including reading relevant documentation, extra materials (books, articles) and applying new methods of data analysis.
  • Give necessary coding skills to allow students to further enhance programming skills by studying advanced techniques which allow to write more effective and professional code meeting international coding standards.
Expected Learning Outcomes

Expected Learning Outcomes

  • Is able to create and edit scientific and popular texts, to present complex historical information in a publicly accessible form (ОПК-4) Capable of conducting independent research, including problem analysis, setting goals and objectives, identifying the object and subject of re-search, choosing the mode and methods of research, and assessing its quality (ОПК-7) Is able to improve and develop his intellectual and cultural level, to build a trajectory of professional development and career (УК-4)
  • Is able to take part in scientific polemics in oral and written form (ПК-4); Capable of extracting, selecting and structuring information from a varie-ty of types of sources according to professional objectives (ПК-7); Is able to analyze historical sources, scientific texts and reports, to review scientific literature in Russian and foreign languages (ОПК-2); Is able to reflex (evaluate and rework) the learned scientific and activity methods (УК-1)
  • Be able to create statistical models based on observation of economic processes and phenomena, analyse the data and interpret results (ПК-13).
  • Be able to estimate probability density function via kernel methods.
  • Be able to use splines for discount curve construction.
  • Understand OOP basic principles: polymorphism, inheritance, encapsulation.
  • Be able to solve programming problem via OOP, write necessary classes and functionality.
  • Be able to choose the best design for specific problem, design classes and their inheritance.
  • Understand how stochastic processes can be used to model prices and returns of financial assets.
  • Understand Binomial, Bachelier and Black-Scholes models.
  • Know main methods of financial instruments pricing: analytic, PDE, Monte-Carlo.
  • Be able to design and develop representation of financial instrument via OOP approach.
  • Understand Bayesian approach in statistics, prior and posterior distributions.
  • Be able to apply Bayesian approach for data analysis, correctly update data and recompute model parameters.
  • Be able to write tests, understand the difference between unit and regression tests.
  • Understand basic blockchain concepts: block, token, mining, node, digital asset.
  • Be able to create simple blockchain using OOP principles.
  • Know basic machine learning algorithms, how statistical methods can be used in machine learning.
  • Know common functionality and design features which various APIs share.
Course Contents

Course Contents

  • Python essentials
  • Calibration of parameters in financial modelling
  • Monte-Carlo methods for finance
  • Principles of Object-oriented programming (OOP)
  • Models for evolution of prices of financial instruments
  • Methods of pricing of financial instruments
  • Bayesian methods
  • Comprehensive data analysis
  • Blockchain
  • Artificial intelligence (AI), basics of neural networks
  • Application public interfaces (API)
Assessment Elements

Assessment Elements

  • non-blocking Final exam
  • non-blocking Midterm test
  • non-blocking Home assignments
  • non-blocking Class activity
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.12 * Class activity + 0.4 * Final exam + 0.2 * Home assignments + 0.28 * Midterm test
Bibliography

Bibliography

Recommended Core Bibliography

  • Hull, J. (2017). Fundamentals of Futures and Options Markets, Global Edition (Vol. Eighth edition). Boston: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419711
  • Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017

Recommended Additional Bibliography

  • The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017