How to Apply
Learn about the admissions process and requirements to apply.
Learn to understand and apply the most cutting-edge technological developments and analytical tools in FinTech. As part of the Rutgers Stackable Business Innovation Program (rSBI), the Financial Data Analytics and FinTech Concentration is stackable with the following master's programs: MS in Quantitative Finance, MBA
This concentration introduces students to the use of financial analytics used by finance practitioners. It provides a strong and rigorous introduction to the use of financial applications in fintech and machine learning.
You can take the course listed below as individual classes or as stackable courses towards the completion of a concentration.
Assuming the students have no prior knowledge in the cryptocurrency and blockchain space, it is important to introduce basic concepts and an overview of the blockchain landscape. Furthermore, the course will explain blockchain and crypto market microstructure concepts, and then we will introduce students to different data sources of both blockchain data and crypto market data. As a blockchain data scientist or a cryptocurrency analyst, you would need to analyze data and understand the present and future value and risk of the blockchain project and/or the cryptocurrency, similar to analyzing any company or financial instrument.
The course has three parts. The first part introduces fundamentals and traditional machine learning techniques including cross validation, regularization, regression trees, ensemble methods, random forests, and gradient boosting. Python libraries scikit-learn (‘sklearn’) and XGBoost will be used. The second part will provide an introduction to Deep Learning. Instead of treating deep neural networks as just another powerful algorithms, we will emphasize what they make possible in financial applications that are difficult or impossible to achieve with earlier methods. Keras and Tensorflow will be used. Cloud computing will also be introduced to facilitate data management and training of these models. The third part of the course is more experiential. Small student teams will work on projects to apply the techniques covered in the course. Projects will use real data and attempt to solve real problems faced in financial industry. The students will have flexibility to choose their topic based on their interest. Applications may focus on asset return predictions, credit risk, mergers, and real estate values among others.
Forecasts of financial variables play a prominent role in financial and business decision-making. This course provides an overview of modern statistical and econometric methods for predicting financial variables and evaluating forecasts. Students will develop an understanding of the basic components of a forecasting model, how to build their own forecasting models, and how to evaluate the performance of forecasting models. We emphasize intuitive understanding of the basic concepts and techniques and practical applications to real-world data. Topics covered include linear projections; modeling and forecasting trend, seasonality and cycles; AR, MA, ARMA, ARIMA, and VAR models; forecasting with fundamentals; conditional forecasting models and scenarios analysis (stress testing); evaluating and combining forecasts; unit roots, cointegration and stochastic trends; smoothing and shrinkage; ARCH, GARCH and volatility forecast; unobserved components models and Kalman filter forecasting; data snooping, bootstrap, and reality check.
The concentration and courses are offered by the Finance & Economics Department
Sample Relevant Careers: Quantitative Financial Analyst, Model Validation Specialist, Data Scientist, Model Risk Review Specialist, Quantitative Risk Associate, Credit Analyst, Financial Engineer, Data Engineer