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Бакалавриат 2022/2023

Анализ данных в экономике и финансах

Направление: 41.03.05. Международные отношения
Когда читается: 3-й курс, 1, 2 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Преподаватели: Максимовская Анастасия Максимовна, Перевышина Татьяна Олеговна
Язык: английский
Кредиты: 3
Контактные часы: 22

Course Syllabus

Abstract

In this intermediate Python course, you will learn how to apply data science methods and techniques to economics and finance. This course will provide you with knowledge and skills in exploratory data analysis, data visualization and linear models. The practical classes are project oriented and cover the basic topics of data science applications as well as Markowitz portfolio theory. By the end of the course, you will be able to perform your own projects in Python.
Learning Objectives

Learning Objectives

  • To provide an introduction to Python applications in economics and finance and enable students to conduct research in a reproducible manner.
Expected Learning Outcomes

Expected Learning Outcomes

  • ability to perform exploratory data analysis, hypothesis testing and visualization
  • Intermediate proficiency in Python libraries for data analysis and visualization (NumPy, Pandas, Matplotlib, Plotly, Scikit-Learn, etc.)
  • the knowledge and skills for implementation of own projects in Python
  • computing optimal asset allocations based on modern approaches
Course Contents

Course Contents

  • Review of Python basics, concepts and syntax for data manipulation.
  • Exploratory data analysis and descriptive statistics using Python packages (Pandas, NumPy).
  • Data visualization using matplotlib, seaborn, plotly
  • Hypothesis testing (t-test, z-test, etc). Confidence intervals.
  • Linear regression. Metrics for quality evaluation (MSE, RMSE, MAE, R2, etc)
  • k-Nearest Neighbours. Model selection, validation and analysis. Cross-validation, train-test split. Parameter tuning.
  • Logistic regression. Metrics for quality evaluation (Accuracy, Precision, Recall, AUC-ROC, etc)
  • Stock prices analysis. Modeling the stock portfolio performance and evaluation. Markowitz Portfolio Theory. Portfolio returns, covariance matrix, mean-variance analysis. The efficient frontier.
Assessment Elements

Assessment Elements

  • non-blocking Контрольная работа
  • non-blocking Экзамен
  • non-blocking Домашнее задание
  • non-blocking Мини-тесты
  • non-blocking Проект
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.2 * Домашнее задание + 0.2 * Проект + 0.2 * Контрольная работа + 0.2 * Экзамен + 0.2 * Мини-тесты
Bibliography

Bibliography

Recommended Core Bibliography

  • Pattern recognition and machine learning, Bishop, C. M., 2006
  • Quantitative equity portfolio management : an active approach to portfolio construction and management, Chincarini, L. B., 2006
  • The data science handbook, Cady, F., 2017

Recommended Additional Bibliography

  • Portfolio selection : efficient diversification of investments, Markowitz, H. M., 1995
  • Valuation for financial reporting : fair value measurements and reporting,intangible assets, goodwill and impairment, Mard, M. J., 2007

Authors

  • MAKSIMOVSKAYA ANASTASIYA MAKSIMOVNA
  • ABROSKIN ILYA DMITRIEVICH