Бакалавриат 2025/2026

Методы работы с данными для экономистов

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»

Лучший по критерию «Новизна полученных знаний»

Статус: Курс по выбору (Международная программа по экономике и финансам)

Кто читает: Международный институт экономики и финансов

Где читается: Международный институт экономики и финансов

Когда читается: 3-й курс, 3, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Лагиос Николас, Хассан Яна Нибаль

Язык: английский

Кредиты: 4

Контактные часы: 64

Course Syllabus

Abstract

This course introduces students to data science for economics and finance. It combines core machine learning methods, text analysis, and practical applications using Python. The lectures cover the main concepts and tools of machine learning, including regression, classification, resampling, model selection, non-linear models, tree-based methods, and unsupervised learning. The course also introduces text analysis, including how to represent text as data, preprocess textual information, and apply methods such as dictionary-based analysis and topic modeling. The final part connects machine learning with econometrics and discusses how these tools can be used in empirical economic research. The seminars provide hands-on training in Python and give students the opportunity to apply the methods covered in the lectures to structured and unstructured datasets. They begin with a review of the foundations of Python, including objects, data types, data structures, indexing, control structures, and functions. Students will then use standard data science workflows to prepare, explore, visualize, analyze, and model data with tools such as NumPy, Pandas, Matplotlib, and relevant machine learning libraries. By the end of the course, students will be able to use Python to implement common machine learning and text analysis methods, evaluate model performance, and apply data science tools to economic and finance questions.

Learning Objectives

• The objective of this course is to provide students with a hands on introduction to data science in economics (or more broadly to data science in the social sciences).
• At the end of the course students should have developed the following skills: • Ability to write simple computer programs using computing language Python; • Implement basic machine learning algorithms; • Understand assumptions and statistical properties of machine learning algorithms; • Be able to use machine learning algorithms to solve real world business problems.

Expected Learning Outcomes

• analyse unbiasedness, consistency and obtain asymptotic distribution of these estimators
• apply basic ideas of statistical learning
• be able to use non – parametric techniques
• derive OLS estimator both in the univariate and in the multivariate case
• explain how the data science is used in industry and in academia
• solve data science problems implementing control structures and functions
• be able to use Python, NumPy, Pandas and Matplotlib
• • implement OLS estimator in the computing language Python, analyse their properties using Monte – Carlo simulations and also apply OLS estimation techniques to the real data
• • Implement logit and linear (quadratic) discriminant analysis using the computing language Python
• • Implement estimators of linear model selection on the computer, using the computing language Python
• • implement algorithms (regression trees, classification trees, bagging, random forest, boosting) using the computing language Python
• • analyze text data in Python
• • be able to derive high-quality information from text (text analysis)
• • formulate research questions that machine learning and/or text data can help answer
• • be able to manipulate basic data structures used in the computing language Python
• • write basic regular expressions
• • Implement both model selection techniques and bootstrap methods using the computing language Python
• • implement PCA, K – Means clustering, Hierarchical clustering using the computing language Python

Course Contents

• Introduction to Data Science In Economics
• Control Structures and Functions
• Vectorized Computation, Data Aggregation and Data Visualization (Part 1)
• Vectorized Computation, Data Aggregation and Data Visualization (Part 2)
• Introduction to Statistical Learning
• Large Sample Properties of OLS
• Classification
• Resampling Methods
• Linear Model Selection and Regularization
• Nonparametric Estimation
• Tree Based Methods
• Unsupervised Learning
• Introduction to Text Analysis: From Text to Data (Part 1)
• Introduction to Text Analysis: From Text to Data (Part 2)

Assessment Elements

• Final Exam
  In order to get a passing grade for the course, the student must sit (all parts) of the examination.
• assignment 3
• assignment 4
• assignment 1
• assignment 2

Interim Assessment

• 2025/2026 4th module
  0.1 * assignment 3 + 0.1 * assignment 4 + 0.1 * assignment 2 + 0.6 * Final Exam + 0.1 * assignment 1

Bibliography

Recommended Core Bibliography

• Lutz, M. (2008). Learning Python (Vol. 3rd ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415392

Recommended Additional Bibliography

• 9781491962992 - Bengfort, Benjamin; Bilbro, Rebecca; Ojeda, Tony - Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning - 2018 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1827695 - nlebk - 1827695

Authors

• Lagios Nikolas