Master
2024/2025
Fundamentals of R for Financiers
Type:
Elective course (Financial Engineering)
Area of studies:
Finance and Credit
Delivered by:
Master's Programmes Curriculum Support
Where:
Faculty of Economic Sciences
When:
2 year, 1 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Nikolay I. Berzon
Master’s programme:
Financial Engineering
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
“Fundamentals of R for Financiers” is intended for students to gain a general understanding of the R software environment, its capabilities for statistical data processing and creation of graphs, the ability to solve various financial problems in this environment, as well as the development of skills in creating publications with calculations and graphs in one environment
Learning Objectives
- Compliance with the realities of the market and the conditions for developing the career of a “cool” financial analyst – R/Python – standard tools (you need to be able to use it, like email); – A range of tasks requiring advanced analysis of specific data and process automation (independent solution of small, typical, local problems); – Ability to formulate a need for a software tool and explain it to a technical specialist without a “translator”
Expected Learning Outcomes
- • solve practical problems using R;
- • carry out statistical calculations in R without using Excel;
- • create typographic-level graphics;
Course Contents
- Topic 1. Introductory lesson.
- Topic 2. Basic data structures: vectors, matrices, lists, dataframes.
- Topic 3. Introduction to the quantmod, PerformanceAnalytics, TTR libraries.
- Topic 4. Graphics in R: basic and ggplot
- Topic 5. Quarto markup language
Bibliography
Recommended Core Bibliography
- Practical data science with R, Zumel, N., 2014
- R in action: Data analysis and graphics with R, Kabacoff, R.I., 2015
- Zumel, N. V. (DE-588)1055925899, (DE-627)792891783, (DE-576)41194200X, aut. (2020). Practical data science with R Nina Zumel and John Mount ; foreword by Jeremy Howard and Rachel Thomas.
Recommended Additional Bibliography
- Ghisellini F., Chang B.Y. Behavioral economics. New York, NY: Springer Berlin Heidelberg, 2018. Retrieved from https://link.springer.com/book/10.1007/978-3-319-75205-1, https://doi.org/10.1007/978-3-319-75205-1
- Landers, C. S. (2017). The Digital Divide : Issues, Recommendations and Research. Hauppauge, New York: Nova Science Publishers, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1530459