Machine Learning and its Application for Finance
- 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.
- 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.
- Data Analysis in MS Excel1.1. 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 Python2.2.1. 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 Finance3.1. Supervised Learning 3.1.1. Regression Algorithms 3.1.2. Classification Algorithms 3.2. Unsupervised Learning
- LabLab in Excel
- HomeworkHomework in Python
- LabLab in Python
- ProjectProject in Python
- Interim assessment (2 module)0.25 * Homework + 0.25 * Lab + 0.25 * Lab + 0.25 * Project
- 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)
- 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.