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Бакалавриат 2019/2020

Введение в машинное обучение в финансах

Лучший по критерию «Новизна полученных знаний»
Статус: Курс по выбору (Экономика)
Направление: 38.03.01. Экономика
Когда читается: 4-й курс, 1 модуль
Формат изучения: с онлайн-курсом
Язык: английский
Кредиты: 4

Course Syllabus

Abstract

The aim of the course is to apply main financial concepts and make students acquainted with machine learning techniques relevant for finance. Python is a general-purpose programming language that is becoming ever more popular for data science. The course focuses on Python specifically for data science. The course is about ways to import, store and manipulate data, and helpful data science tools to conducting data analyses. The course is intended for students with basic background in finance, statistical methods. Experience in programming is not required, but advantageous. The learning process is facilitated with DataCamp platform.
Learning Objectives

Learning Objectives

  • At the end of the course, students should be able to write short scripts to import, prepare and analyze financial data for making decisions.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know the main data types, their methods and attributes. Know how to import, clean and merge datasets.
  • Know time series models.
  • Know ML techniques and how to use them in Python.
  • Know how to work in Jupyter Notebook.
Course Contents

Course Contents

  • Introduction to Python for Finance
    1. Lists and Arrays. Introduction to basics in Python, including how to name variables and various data types in Python. NumPy and Matplotlib packages. https://www.datacamp.com/courses/intro-to-python-for-finance 2. DataFrames. Using of pandas to import and inspect a variety of datasets. Building DataFrames and the intrinsic data visualization capabilities of pandas. https://www.datacamp.com/courses/pandas-foundations 3. Importing, cleaning and merging data. Importing, cleaning and combining data from Excel workbook sheets into a pandas DataFrame. Grouping data, summarizing information for categories, and visualizing the result using subplots and heatmaps. https://www.datacamp.com/courses/importing-managing-financial-data-in-python
  • Statistical Methods in Python
    4. Statistical Thinking in Python. The principles of statistical inference. Graphical exploratory data analysis, quantitative exploratory data analysis, statistical inference for discrete and continuous variables. https://www.datacamp.com/courses/statistical-thinking-in-python-part-1 5. Introduction to Time Series Analysis in Python. Correlation and autocorrelation, autoregressive (AR) models, moving average (MA) and ARMA models in Python. https://www.datacamp.com/courses/introduction-to-time-series-analysis-in-python
  • Machine learning in Python
    6. Supervised Learning with scikit-learn. Building predictive models, tuning their parameters, and determining how well they will perform with unseen data. Scikit-learn library for machine learning in Python. https://www.datacamp.com/courses/supervised-learning-with-scikit-learn 7. Machine Learning for Finance in Python. Calculation of technical indicators from historical stock data, the historical stock data analysis. Linear models, xgboost models, and neural network models. Decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. https://www.datacamp.com/courses/machine-learning-for-finance-in-python
  • Environment for scientific programming in Python
    8. Jupiter Notebook as an environment for scientific programming in Python, its structure and features.
Assessment Elements

Assessment Elements

  • non-blocking Self-study work
  • non-blocking Exam
    Final student assessment is a project that is performed in a team of no more than 2 people. Each team uses provided dataset and apply one or a combination of the learnt methods of data analysis in Python. As a result of the project each team write down the report and prepare working file. The grade for the exam includes the grade for the report, grade for the working file and the grade for answering questions.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.5 * Exam + 0.5 * Self-study work
Bibliography

Bibliography

Recommended Core Bibliography

  • Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081

Recommended Additional Bibliography

  • Seemon Thomas. (2014). Basic Statistics. [N.p.]: Alpha Science Internation Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1663598