• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Master 2019/2020

Machine Learning and its Application for Finance

Category 'Best Course for Career Development'
Category 'Best Course for New Knowledge and Skills'
Type: Elective course (Finance)
Area of studies: Finance and Credit
When: 2 year, 1 module
Mode of studies: Full time
Instructors: Andrei Ternikov
Master’s programme: Finance
Language: English
ECTS credits: 4

Course Syllabus

Abstract

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.
Learning Objectives

Learning Objectives

  • 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.
Expected Learning Outcomes

Expected Learning Outcomes

  • 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.
Course Contents

Course Contents

  • 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
Assessment Elements

Assessment Elements

  • non-blocking Lab
    Lab in Excel
  • non-blocking Homework
    Homework in Python
  • non-blocking Lab
    Lab in Python
  • non-blocking Project
    Project in Python
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.25 * Homework + 0.25 * Lab + 0.25 * Lab + 0.25 * Project
Bibliography

Bibliography

Recommended Core Bibliography

  • 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)

Recommended Additional Bibliography

  • 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.