The new course offering at Marquette University (FIN 4931: FinTech Topics) completed week 1 and students are already running R code to scrap real-time, unstructured big data from Google!
Articles appear almost daily talking about the increased number of financial firms that are utilizing data science and machine learning systems to support their research and trading activities.
During the first week of FINA 4931, Dr. Krause’s students were introduced to R – an open-source programming language used by many in industry and the academic community.
Dr. Krause said, “The libraries and tools available in R – and with the use of R Studio – we have been able to get up and running with big data analytics within the first few class meetings. R is not only ideal for statistical analysis, it also has strong supporting packaging that make the collection of data fast and easy.”
The course will spend considerable time on data analytics (utilizing real-time Google, Twitter and Facebook data), but it will also include modules on: Artificial Intelligence, Machine Learning, Blockchain, and Cybersecurity.
Krause stated, “This course will likely grow and become embedded within the classes I teach at Marquette in the AIM program. The advances within the FinTech industry are amazing and the trend will only continue. The financial services sectors, including investments, insurance and banking will change dramatically because of the huge among of digital data they employ and the need to better serve their clients (including digital natives, millennials, and Gen Z). These are very exciting times.”
“Within the next several weeks, it is our goal for the students to be able to apply R programming language and essential data science techniques to begin to understand how to solve complex finance problems,” Dr. Krause concluded.
“The students will begin to see how the content within the course can be effectively applied in areas such as: sentiment analysis, advanced econometric and time series analysis, risk management and reporting, real-time access and analysis of financial and economic data, credit evaluation, and machine learning algorithms.”