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Machine Learning and its Application for Finance

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

Преподаватель

Программа дисциплины

Аннотация

During this practically oriented data science module students will learn how machine learning uses computers to run predictive models. The main principal is to explore existing data to build new knowledge, forecast future behaviour, anticipate outcomes and trends. Explore theory and practice, and work with tools like Python to solve advanced data science problems.
Цель освоения дисциплины

Цель освоения дисциплины

  • Make students able to collect, store, process and analyze data automatically with the use of scripting languages.
  • Make students able to develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques.
  • Make students able to solve economic, financial and managerial problems using best practices of data analysis using modern computational tools.
  • Make students able to identify the data needed for addressing the financial and business objectives.
Результаты освоения дисциплины

Результаты освоения дисциплины

  • Collect, store, process and analyze data automatically with the use of scripting languages; develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
  • Students should know how to: use ICT solutions in solving real-life problems, work together with other team members, develop personal knowledge and skills.
  • Choose methods adequately corresponding to the objectives of a research project
  • Able to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of finance
  • Planning and beginning to perform a research project requires an open and innovative mindset.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Data Analysis in MS Excel
    1.1. Manipulating with Data in Excel (Import, Formats, VLOOKUPs) 1.2. Text & Financial functions + PivotTables in Excel 1.3. Financial Models in Excel (OLS + Forecasting)
  • Introduction to Python
    ∙ Scripting languages itself and Graphical User Interface (GUI) ∙ Reading developers’ documentation (packages, libraries, forums) ∙ Code iterations (loops) ∙ Writing function
  • Managing Datasets in Python
    2.2.1. Data Sources ∙ Minable Data examples (text, data tables, time-series, images, etc) ∙ *.csv-format: separators (delimiters) and encoding 2.2.2. Data Structures ∙ Data formats (types) in Python ∙ Data arrangement (matrices, lists, data frames) 2.2.3. Data Processing ∙ Cleaning noisy data ∙ Merging and reorganizing data ∙ Concatenating strings ∙ Date formats ∘ Regular expressions & Encoding issues
  • Data Visualisation
    ∙ Types of graphics ∙ Exploratory data analysis
  • Getting Data from Web
    ∙ Reading, uploading and saving data ∙ Code debugging ∙ Basic HTML syntax ∙ Special formats of data *.xml and *.json ∘ Working with Application Programming Interfaces (APIs)
  • Machine Learning Algorithms in Finance
    3.1. Supervised Learning 3.1.1. Regression Algorithms 3.1.2. Classification Algorithms 3.2. Unsupervised Learning
Элементы контроля

Элементы контроля

  • Lab (неблокирующий)
    Lab in Excel
  • Homework (неблокирующий)
    Homework in Python
  • Lab (неблокирующий)
    Lab in Python
  • Project (неблокирующий)
    Project in Python
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (1 модуль)
    0.25 * Homework + 0.25 * Lab + 0.25 * Lab + 0.25 * Project
Список литературы

Список литературы

Рекомендуемая основная литература

  • Danielle Stein Fairhurst (2015). Using Excel for Business Analysis
  • Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)

Рекомендуемая дополнительная литература

  • Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.