Прикладной анализ данных и искусственный интеллект в финансах

Магистратура 2025/2026

Статус: Курс по выбору (Финансовый аналитик)

Кто читает: Банковский институт

Где читается: Банковский институт

Когда читается: 1-й курс, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Искяндяров Руслан Рушанович

Язык: английский

Кредиты: 3

Контактные часы: 32

Full Syllabus Ask Question

Abstract

Advanced data skills are among the most in-demand competencies for professionals across various fields, including finance, fintech, e-commerce, public administration, and more. This course immerses students in the principles of data analysis, machine learning, and artificial intelligence. Within the curriculum, students learn the fundamentals of the Python programming language, key methods for processing diverse data types, and the development and application of machine learning models to solve practical challenges in various sectors of the economy.

Learning Objectives

The objectives of this discipline are to equip students with theoretical knowledge and advanced practical skills in methods of collecting, processing, and analyzing diverse data types, as well as building and applying machine learning models.

Expected Learning Outcomes

Set up a working Python development environment
Apply basic Python syntax: define variables, use fundamental data types, perform arithmetic operations.
Use basic containers (lists, tuples, dictionaries) to store and manipulate data.
Using conditionals, loops, functions, and external libraries to solve simple practical problems in Python.
Working with pandas for data manipulation and using visualization tools to support data-driven decision-making.
Use classification algorithms like decision trees and random forests to build models and evaluate their performance with relevant metrics for binary and multiclass problems.
Apply clustering algorithms in machine learning and evaluate clustering results using appropriate techniques.
Apply fundamental NLP techniques including tokenization, lemmatization, stemming, and text vectorization methods such as bag-of-words and TF-IDF

Course Contents

Python Programming Fundamentals, p.1
Python Programming Fundamentals, p.2
Data Analysis in Python: Methods and Applications
Classification Algorithms in Classical Machine Learning: Theory and Practice
Clustering in Classical Machine Learning: Models and Evaluation Techniques
Natural Language Processing (NLP): Foundations and Techniques

Assessment Elements

Final test
Final data analysis
Final data analysis on all course topics
Test #1
Test Python basics
Test #2
Test Machine learning and AI
Project Assignments #1
Project Assignments #1
Project Assignments #2
Project Assignments #2

Interim Assessment

2025/2026 4th module
0.15 * Project Assignments #2 + 0.1 * Test #2 + 0.1 * Test #1 + 0.25 * Final data analysis + 0.25 * Final test + 0.15 * Project Assignments #1

Bibliography

Recommended Core Bibliography

9781789958294 - Raschka, Sebastian; Mirjalili, Vahid - Python Machine Learning : Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow 2, 3rd Edition - 2019 - Packt Publishing - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2329991 - nlebk - 2329991
Aman Kedia, & Mayank Rasu. (2020). Hands-On Python Natural Language Processing : Explore Tools and Techniques to Analyze and Process Text with a View to Building Real-world NLP Applications. Packt Publishing.
Clustering : a data recovery approach, Mirkin, B., 2013
Clustering for data mining : a data recovery approach, Mirkin, B., 2005
Harish Garg. (2018). Mastering Exploratory Analysis with Pandas : Build an End-to-end Data Analysis Workflow with Python. Packt Publishing.
Image analysis, classification, and change detection in remote sensing : with algorithms for Python, Canty, M. J., 2019
Introduction to machine learning, Alpaydin, E., 2020
Introduction to natural language processing, Eisenstein, J., 2019
Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019
Machine learning fundamentals : a concise introduction, Jiang, H., 2021
Mathur, P. (2019). Machine Learning Applications Using Python : Cases Studies From Healthcare, Retail, and Finance. [Berkeley, California]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1982259
Pandas for everyone : Python data analysis, Chen, D. Y., 2023
Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
Schneider, D. I. (2016). An Introduction to Programming Using Python, Global Edition: Vol. Global edition. Pearson.
Sweigart, Al. Automate the boring stuff with Python: practical programming for total beginners. – No Starch Press, 2015. – 505 pp.
Груздев, А. В. Изучаем Pandas / А. В. Груздев, М. Хейдт , перевод с английского А. В. Груздева. — 2-ое изд., испр. и доп. — Москва : ДМК Пресс, 2019. — 700 с. — ISBN 978-5-97060-670-4. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/131693 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

Dipanjan Sarkar. (2019). Text Analytics with Python : A Practitioner’s Guide to Natural Language Processing: Vol. Second edition. Apress.
Mueller, J. (2014). Beginning Programming with Python For Dummies. Hoboken: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=838174
Mueller, J. (2018). Beginning Programming with Python For Dummies (Vol. 2nd edition). Hoboken, NJ: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1689584
Python : исчерпывающее руководство, Бизли, Д. М., 2023
Wei-Meng Lee. 2019. Python Machine Learning. John Wiley & Sons, Incorporated
Wei-Meng Lee. 2019. Python Machine Learning. John Wiley & Sons, Incorporated
Простой Python. Современный стиль программирования, Любанович, Б., 2023

Authors

ELIZAROVA IRINA NIKOLAEVNA
Iskiandiarov Ruslan Rushanovich

Course Syllabus