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.
- 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
- Choose methods adequately corresponding to the objectives of a research project
- 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
- Planning and beginning to perform a research project requires an open and innovative mindset.
- Students should know how to: use ICT solutions in solving real-life problems, work together with other team members, develop personal knowledge and skills.
- Data Analysis in MS Excel
- Introduction to Python
- Managing Datasets in Python
- Data Visualisation
- Getting Data from Web
- Machine Learning Algorithms in Finance
- 2022/2023 3rd module0.25 * Lab in Python + 0.25 * Lab in MS Excel + 0.25 * Hometask in Python + 0.25 * Project in Python
- 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.