Big Data and Machine Learning with Applications to Economics and Finance

2021/2022

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

International College of Economics and Finance

Course type:

Elective course

When:

2 year, 1, 2 module

Instructors

Zimin, Stepan

Slonimczyk, Fabian

Course Syllabus

Abstract

Big Data and Machine Learning (M.Sc. level) is an advanced elective course designed for masters students at ICEF. The course is open to all second year M.Sc. students. Basic knowledge of the Python programming language is strongly advised but not required. Students without Python knowledge will be expected to exert additional effort during the first few weeks of the course to catch up. The course is taught in English. The course has three broad sections: I. Building skills using Python libraries to solve common problems in the analysis offinancial data. II. Learning how to mine the web, social media, and other big data sources in searchfor data. III. Designing and implementing machine learning models.

Learning Objectives

• The main objective of the course is to endow students with fundamental skills related to data mining and analytics, as well as with designing and implementing machine learning predictive models.

Expected Learning Outcomes

• - Analyze multiple data sources
• - Apply clustering and anomaly detection methods
• - Be able to code simple algorithms using Python
• - Be able to setup a neural network
• - Convert text into input for machine learning algorithms
• - Find solutions to optimization problems using Python
• - Present data graphically
• - Train a ML regression. Make predictions
• - Train an ML classifier. Make predictions
• - Use data structures to store and transform data
• - Use Python to solve simple analytical tasks
• - Use web applications API to obtain data

Course Contents

• Introduction to Python
• Python’s Scientific Stack: NumPy, Pandas, and SciPy
• Data Visualization
• Financial and Other Applications
• Mathematical tools and numerical calculus
• Big Data
• Introduction to Data Mining
• Mining the Social Web
• Textual Analysis
• Machine Learning Classification Methods
• Machine Learning Regression Methods
• Neural Networks. Forecasting Stock and Commodity Prices

Assessment Elements

• project proposal
• intermediate report
• a final report and presentation
• attendance and participation
• home assignments
• exam
  In order to get a passing grade, students must obtain an exam grade of 25/100 or higher. Online format.

Interim Assessment

• 2021/2022 2nd module
  0.3 * exam + 0.05 * project proposal + 0.3 * a final report and presentation + 0.05 * attendance and participation + 0.1 * intermediate report + 0.2 * home assignments

Bibliography

Recommended Core Bibliography

• Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1486117
• Han, J., Kamber, M., Pei, J. Data Mining: Concepts and Techniques, Third Edition. – Morgan Kaufmann Publishers, 2011. – 740 pp.
• Hilpisch, Y. (2014). Python for Finance : Analyze Big Financial Data (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=925360
• Kirk, M. (2015). Thoughtful Machine Learning with Python : A Test-Driven Approach. Sebastopol: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1455642
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Recommended Additional Bibliography

• Chatterjee, S., & Krystyanczuk, M. (2017). Python Social Media Analytics. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1565635
• Russell, M. A., & Klassen, M. (2018). Mining the Social Web : Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1951213
• Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293
• Squire, M. (2016). Mastering Data Mining with Python – Find Patterns Hidden in Your Data. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1343887
• Weiming, J. M. (2019). Mastering Python for Finance : Implement Advanced State-of-the-art Financial Statistical Applications Using Python, 2nd Edition (Vol. Second edition). Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2116431
• Yan, Y. (2017). Python for Finance - Second Edition (Vol. Second edition). Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1547029