• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Introduction to Machine Learning for Finance

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
4-й курс, 1 модуль

Преподаватель

Программа дисциплины

Аннотация

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.
Цель освоения дисциплины

Цель освоения дисциплины

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

Результаты освоения дисциплины

  • 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.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • 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.
Элементы контроля

Элементы контроля

  • DataCamp (неблокирующий)
  • Exam (неблокирующий)
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (1 модуль)
    0.5 * DataCamp + 0.5 * Exam
Список литературы

Список литературы

Рекомендуемая основная литература

  • 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

Рекомендуемая дополнительная литература

  • 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