M.S. in Financial Technology and Analytics and Graduate Management Program Dual Degree Master's Program

This course is designed to teach students the nature and availability of the financial data available at Stevens. The focus of the course will be on equity, futures, FX, options, swaps, CDS’s, interest rate swaps etc. They will learn to how use a Bloomberg terminal. As part of the course the students will be certified in the 4 areas that Bloomberg offers certification. We will cover the Thomson–Reuters Tick history data and basics of using this data. The course also introduces basics of applied statistics. Bloomberg terminal access will be required for any student taking the course on the web.

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance.

This course is a primer on Python (language syntax, data structures, basic data processing, Python functions, modules and classes). The remainder of the course covers open source Python tools relevant to solving financial programming problems. The lecture, supporting examples, and practical applications are intertwined. The content will be delivered in a fully equipped financial computing laboratory where the students are immersed in case studies of real life applications. There will be reading assignments of the corresponding chapters in the textbook and additional materials will be provided.

This course provides an overview of issues and trends in data quality, data storage, data scrubbing and data flows. Topics include data abstractions and integration, enterprise-level data issues, data management issues, similarity and distances, clustering methods, classification methods, text mining, and time series. Furthermore, the Hadoop-based programming framework for big data issues will be introduced, along with any governance and policy issues. 
Corequisite: FE 513

The course provides a practical introduction to SQL databases and Hadoop cluster systems as available in the Hanlon Financial Systems Lab. Students will receive hands on instruction about setting up and working with databases. Most of the software will be introduced using case studies or demonstrations, followed by a lecture of related fundamental knowledge. The course covers SQL, NoSQL, and database management systems. The course will cover accessing databases using API.

Effective visualization of complex data allows for useful insights, more effective communication and better decision-making. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 and CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs.

Introduction to information theory: the thermodynamic approach of Shannon and Brillouin. Data conditioning, model dissection, extrapolation, and other issues in building industrial strength data-driven models. Pattern recognition-based modeling and data mining: theory and algorithmic structure of clustering, classification, feature extraction, Radial Basis Functions, and other data mining techniques. Non-linear data-driven model building through pattern identification and knowledge extraction. Adaptive learning systems and genetic algorithms. Case studies emphasizing financial applications: handling financial, economic, market, and demographic data; and time series analysis and leading indicator identification.

This course deals with financial technology underlying activities of markets, institutions and participants. The overriding purpose is to develop end-to-end business decision making data analytics tools along with enterprise level systems thinking. Statistical learning algorithms will be connected to financial objects identification and authentication along with the appropriate databases to create enterprise level financial services analytics systems.

This course introduces the modern portfolio theory and optimal portfolio selection using optimization techniques such as linear programming. Topics include contingent investment decisions, deferral options, combination options and mergers and acquisitions. The course then focuses on financial risk management with emphasis on Value-at-Risk (VAR) methods using general and parametric distributions and VAR as a risk measure. Real world scenarios are studied.

This course is designed for FE students undertaking a research or a project in financial engineering either individually or as a group. The project may be suggested either by faculty members or industry senior managers associated with your internship, as well as any internship that a student may receive through this course. The goal of this course is to involve students in developing research skills, communication skills while keeping their interest in result-oriented techniques. The ability to work on a research-oriented project in a group environment and train their professional presentation and scientific writing skills lead to a competitive graduate who is ready to lead in the work place.

Electives available to be taken at SPJIMR toward the GMP Degree