Bachelor
2022/2023
Data Analysis in Economics and Finance
Type:
Elective course (International Bachelor's Programme in World Politics)
Area of studies:
Public Policy and Social Sciences
Delivered by:
Faculty of World Economy and International Affairs
When:
3 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
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
- To provide an introduction to Python applications in economics and finance and enable students to conduct research in a reproducible manner.
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
- 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.
Interim Assessment
- 2022/2023 2nd module0.2 * Домашнее задание + 0.2 * Проект + 0.2 * Контрольная работа + 0.2 * Экзамен + 0.2 * Мини-тесты
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