Бакалавриат
2026/2027
Анализ данных и основы машинного обучения
Статус:
Курс обязательный (Программные системы и автоматизация процессов разработки)
Где читается:
Факультет социально-экономических и компьютерных наук
Когда читается:
3-й курс, 1-3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Городилов Алексей Юрьевич
Язык:
английский
Кредиты:
8
Контактные часы:
92
Course Syllabus
Abstract
Understanding, interpreting, and presenting data is a crucial skill in today's world. By completing the "Data Analysis and Machine Learning Fundamentals" course, students will explore modern data analysis methods for research and gain practical skills in using the Python language for working with data. Students will be able to prepare data for subsequent work, select the appropriate analysis method depending on the data type and research task, analyze the data, interpret the results, and present them visually. The course focuses on machine learning methods and technologies. It utilizes specialized Python libraries (numpy, scipy, pandas, sklearn).
Learning Objectives
- Developing skills in using software tools (using the Python programming language and its libraries as an example) for preprocessing, analysis, and visualization of various data.
- Developing students' skills in applying machine learning methods using modern approaches, technologies, and tools (using the Python programming language and its libraries as an example).
Expected Learning Outcomes
- Demonstrate the ability to work in different software environments for data analysis and to explain the choice of software.
- Able to identify the suitable metric for a machine learning system
- Understand basic principles of machine learning, be able to implement the algorithms in practice
- Be able to apply data analysis tools to real-life problems.
- Be able to train machine learning algorithms for the task of classification, clustering and regression
- Analyze data with machine learning tools
- Build features suitable for the selected machine learning models
- Chooses appropriate machine learning method to solve a particular problem.
- Master the art of combining different machine learning models and learn how to ensemble.
- Process tabular data
- Be able to use the scikit-learn library to train machine learning models.
- Know how to validate and interpret machine learning models.
- Compare machine learning models. Identify the advantages and disadvantages of using solutions based on them
- Prepares data for machine learning algorithms
- Construct machine learning models on the proposed data sets in Python
- Be able to differentiate and correctly apply most common approaches to ensemble learning (random forests, gradient boosting, stacking, blending, etc.) as well as to explain their benefits and limitations
- Apply clustering algorithms in machine learning and evaluate clustering results using appropriate techniques.
- Know the basic concepts and paradigms of machine learning, the differences between ML and traditional programming.
- Explain the principles of recommender systems and their application in real products
- Be able to apply classical machine learning methods to solve classification and regression problems
Course Contents
- Tools and libraries for data analysis
- Machine Learning Basics
- Modern machine learning technologies
Assessment Elements
- Practical exercises on the basics of data analysis and visualization
- Practical exercises on classical machine learning models
- Practical exercises on advanced machine learning models and ensemble models
- Exam
Interim Assessment
- 2026/2027 2nd module0.6 * Practical exercises on classical machine learning models + 0.4 * Practical exercises on the basics of data analysis and visualization
- 2026/2027 3rd module0.7 * Practical exercises on advanced machine learning models and ensemble models + 0.3 * Exam
Bibliography
Recommended Core Bibliography
- A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62
- BOKER, A. (2019). Machine Learning & Analytics: The Modern Key to a Positive Customer Experience. Design: Retail, 31(3), 42.
- Introduction to Statistics and Data Analysis, With Exercises, Solutions and Applications in R, Christian Heumann, Michael Schomaker, Shalabh, Springer Nature Switzerland AG 2022, 978-3-031-11833-3, published: 30 January 2023
- Mehryar Mohri, Afshin Rostamizadeh, & Ameet Talwalkar. (2018). Foundations of Machine Learning, Second Edition. The MIT Press.
Recommended Additional 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
- 9781800206571 - Serg Masís - Interpretable Machine Learning with Python : Learn to Build Interpretable High-performance Models with Hands-on Real-world Examples - 2021 - Packt Publishing - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2901980 - nlebk - 2901980
- Chris Albon. (2018). Machine Learning with Python Cookbook : Practical Solutions From Preprocessing to Deep Learning: Vol. First edition. O’Reilly Media.
- Iyoob, I. (2019). Data science vs. operations research: A comparison: Machine learning is more popular today yet it still includes OR algorithms. ISE: Industrial & Systems Engineering at Work, 51(12), 42. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=f5h&AN=139715696
- Jason Bell. (2020). Machine Learning : Hands-On for Developers and Technical Professionals: Vol. Second edition. Wiley.
- 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
- S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.
- Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293
- Taieb, D. (2018). Data Analysis with Python : A Modern Approach. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993344