M.S. in Financial Technology & Analytics and M.S. in Statistics Dual Degree Master's Program

The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc.

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.

This course deals with risk management concepts in financial systems. Topics include identifying sources of risk in financial systems, classification of events, probability of undesirable events, risk and uncertainty, risk in games and gambling, risk and insurance, hedging and the use of derivatives, the use of Bayesian analysis to process incomplete information, portfolio beta and diversification, active management of risk/return profile of financial enterprises, propagation of risk, and risk metrics.

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.

In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR.

Prerequisite: BIA 652 or MGT 700 or FA 541

A minimum of six credit hours is required for the thesis. Hours and credits to be arranged. You will need both an advisor and a reader to complete this course; interested students should contact their academic advisor for complete details.

Probability and Statistical Inference (7.5 ECTS)

Upon successful completion of the course, students will be able to handle issues related to: probability and distribution theory, principles of sufficiency and likelihood, and statistical inference with emphasis on the presentation of analytical methods of finding and evaluating: point estimators, interval estimators and hypothesis tests (using the Frequentist and the Bayesian approaches).

Computational Statistics (7.5 ECTS)

Upon completion of the course the students will be able to use the computer for statistical inference, simulate various phenomena and stochastic models based on different distributions, solve statistical problems that involve numerical methods with the use of computer, and write R code for all the above.

Generalized Linear Models (7.5 ECTS)

Upon successful completion of the course, students are expected to understand if the nature of their data allows application of a generalized linear model (knowledge and understanding). They should also be able to define the appropriate generalized linear model to the data at hand (application). They should be able to fit this model and interpret the results of analysis (skill). Finally, they should be able to explain to scientists of other disciplines the results of their analysis (synthesis).

Data Analysis (7.5 ECTS)

Upon completion of the course, students will be able to manipulate and analyze data in R, perform basic hypothesis tests, build and interpret regression models, and write statistical reports in a professional way.

Select 4 out of 5 courses

Biostatistics (4 ECTS)

After successfully completing the course, students will be able to recognize the appropriate study design in a medical study, and use appropriate measures and statistical methods to help the health scientist in deriving sensible conclusions.

Epidemic Models (4 ECTS)

At the end of the course, the student will have a basic understanding of disease transmission and the relevant factors which affect it. Disease control techniques and parameter estimation will be possible, including the estimation of the relevant function(al)s such as the disease reproductive rate and the vaccination coverage.

Advanced Methods in Survey Sampling (3.5 ECTS)

Upon completion of the course, the students will be able to identify the type of the statistical problem in real survey sampling situations, as well as to choose and apply in any case the appropriate methodology. Furthermore, they will be able to evaluate the quality of the results of the chosen methodology.

Statistical Process Control (3.5 ECTS)

After successfully completing the course, students will acquire the skills needed to deal with problems improving the quality of products or services using statistical methods.

Topics in Applied Statistics: Statistical Genetics - Bioinformatics (3.5 ECTS)

After completing the course, the students will know the basic statistical challenges in bioinformatics, properly deal with large scale hypothesis testing, learn many novel statistical ideas and methods developed in the last 20 years, such as hybridizations of Bayesian and frequentist data analysis, put their hands on many different types of data that modern biologists have to deal with including microarrays, RNA-Seq, chip-Seq and single cell measurements, and know how to apply the relevant methods using R and Bioconductor.

Select 4 out of 5 courses

Bayesian Models in Statistics (4 ECTS)

Upon completion of the course, students will be able to understand the basic theory and philosophy of Bayesian Statistics, understand the basic notions of Bayesian computation, analyze data using WinBUGS, build models (glm and hierarchical) in WinBUGS, and perform Bayesian variable selection using WinBUGS and BAS package in R.

Statistical Learning (4 ECTS)

Upon completion of the course, students will have the knowledge and the skills to implement statistical methods aiming to deal with the problem of data dimension reduction, classification and clustering. They will be able to interpret the results and assess the methodologies' performance.

Statistics for Big Data (3.5 ECTS)

Τhe target of the course is to show the needs and challenges produced by the big data era and how statistical methodologies can handle them. After completing the course, students will be able to understand the new challenges and problems due to the abundance of data, apply new techniques defined to handle problems with big data, and implement the new methods using R.

Advanced Stochastic Processes (3.5 ECTS)

Upon completion of the course, students will know the notion of martingale, which plays an
important role in financial and actuarial applications. They will also be familiar with the Optional Stopping Theorem and Applications and the notion of Brownian Motion, with stochastic calculus and with stochastic differential equations (with applications in various scientific fields).

Topics in Computational Statistics - Applied Stochastic Modeling (3.5 ECTS)

Upon completion of the course, students will appreciate the importance of computing for modern statistical analysis, appreciate the breadth and importance of modern statistical methods, and be able to describe a number of practical areas where statistical modeling is of importance. Students will also have an appreciation of how probability models may be formulated for atypical data sets, have a good understanding of how likelihood-based classical procedures operate in practice, and have experience of running a wide range of modern statistical procedures through running computer programs in R.

Select 4 of 5 courses

Probability Theory (4 ECTS)

After completing the course, students should be able to construct Probability Spaces, to calculate Εxpected (Mean) Value of a random variable by Lebesgue integration with respect to the induced probability distribution on the Borel real line, to discern among different notions of Stochastic Convergence and to implement them properly via pertinent significant theorems (e.g. monotone and dominated convergence theorems, laws of large numbers, central limit theorems) applied in both probability theory as well as in mathematical statistics.

​Time Series Analysis (4 ECTS)

After completing the course, students will understand in depth the concepts, models and methods described in the syllabus: capability to respond to relevant theoretical questions and exercises. The ability to apply the methods taught in real data
analysis.

Stochastic Modeling in Finance (3.5 ECTS)

After completing the course, students will be familiar with the use and construction of stochastic models for finance, as well as with the necessary analytic and computational methods which are used in finance and risk management both in academic as well as in real business environments.

Financial Econometrics (3.5 ECTS)

The aim of this class is to provide students with advanced statistical and econometric skills required to analyze empirical problems in finance. After successfully completing the course, students will be able to interpret the concepts of return and risk in financial markets, model the expected returns of financial assets, model the variances and covariances/correlations of financial returns, use advanced econometric tools to analyze models used in financial applications, forecast financial returns, assess the performance of portfolio managers, understand modern portfolio theory, solve mean-variance optimization problems, and estimate the risk of financial assets.

Topics in Stochastics - Stochastic Models in Operations Research (3.5 ECTS)

After successfully completing the course, students will be able to compute various quantities of interest as the stationary probabilities of a stochastic process, the mean number of customers in a queueing systems. They will also be able to find the optimal policy for the control of a stochastic system and compute the long-run expected average cost of a renewal process.

The DT aims to give the opportunity to the student to develop and apply research methodologies in real data to topics of scientific interest related to the studies of the MSc program. The goal is for the student to delve deeper into the topic under consideration, study the existing literature, develop critical thinking by formulating appropriate research hypotheses, collect and analyze appropriate empirical data and acquire skills of investigation and derivation of substantiated conclusions.