This course studies the process of conducting surveys. Topics that will be discussed include survey design, sampling and nonsampling errors, simple random sampling, stratified sampling, systematic sampling, cluster sampling, ratio estimators, regression estimators, determination of optimal sample size, bias in survey sampling and modern techniques of survey sampling.

This course studies the analysis of time series, that is temporal stochastic processes. Topics covered include: stochastic processes, weak and strong stationarity. Autoregressive and moving average based models for stationary and non-stationary time series. Trend and seasonal behaviour, sample autocorrelation function and sample partial autocorrelation function. Parameter estimations, model identification, prediction. ARMA, ARIMA and SARIMA models. Properties, estimation and examples. ARCH and GARCH models for volatility.

This course studies topics from multivariate statistical analysis. Topics covered include: random vectors, measures of center and variation in multivariate moments. Multivariate normal distribution. Tests for normality. Estimation of the mean vector and the variance analysis, independence, multivariate –covariance matrix. Wishart and Hotelling distributions. Statistical inference. Union – Intersection Test. Confidence regions. Multivariate analysis of variance and multivariate regression analysis. Least squares method and Wilks distribution. Analysis of covariance. Principal components, Factor analysis, Discriminant analysis, Cluster analysis. The R statistical programming language will be used for applying the introduced methods in a range of Data Science problems.

This course introduces Bayesian Statistics, an intuitive approach to Statistics allowing for better accounting of uncertainty. Topics include: subjective probability, Bayes rule, prior and posterior distributions, conjugate and non-informative priors, point-wise estimation and credible intervals, hypothesis testing, introduction to Bayesian decision analysis, introduction to empirical Bayes analysis, introduction to Markov chain Monte Carlo techniques. The course will make use of R statistical programming language for the implementation of algorithms for extracting information from the posterior and for the application of the introduced methods in a range of Data Science problems.

This course studies the interplay between Statistics and Computation, an area of vital importance in computer-age Statistics. Topics covered include: multiple regression, Cholesky decomposition, diagnostics and collinearity, principal components and eigenvalue problems. Nonlinear statistical methods: Maximum likelihood estimation, Newton-Raphson and related methods, multivariate data and the Newton Raphson method, optimization techniques (unconditional and under constraints) EM algorithm. Numerical Integration and Approximation: Newton-Coates method, spline interpolation, Monte Carlo integration, general approximation methods. Probability Density Estimation: Histogram, linear and non-linear smoothing, splines. Bootstrap.

This course explores advanced and emerging topics in the field of statistics, providing students with an in-depth understanding of specialized areas not typically covered in standard statistical curricula. Each offering of the course will focus on a specific theme, which may vary depending on current trends, faculty expertise, and student interest.

Possible topics based on the current faculty include, but are not limited to Machine Learning Methods, Advanced Time Series Methods, Network Analysis, Bayesian Nonparametric Statistics and Applications, Statistical Methods in Genomics and Bioinformatics, Nonparametric and Semiparametric Methods, Spatial Statistics, Causal Inference and Survey Sampling (with a focus on privacy).

Introduction to Data visualization, Web development, Javascript, Data driven documents (D3.js), Interaction, filtering, aggregation, Perception, cognition, Designing visualizations (UI/UX), Text visualization, Graphs, Tabular data viz Music viz, Introduction to scientific visualization, Storytelling with data / data journalism, Creative coding.

The purpose of this course is to introduce Business Analytics students to complex forms of thinking, which, ultimately, lead to reflective judgment, in the effort to cope effectively with real-world problems, especially ill-structured ones. The course will explore the features of complex thinking, provide the tools for developing it, and suggest ways in which complex thinking leads to reflective judgment.

Thinking becomes complex when it embraces uncertainty, accommodates paradox and allows for the ineffable; is dynamic and flexible; holistic, critical, and reflexive. Judgment becomes reflective when it is evidence-based and context-sensitive, while seeking unifying principles in the service of a good purpose. In this course, reflective judgment will be taken to be tantamount to practical wisdom (Aristotle’s phronesis). Developing reflective judgment, one needs to cope with paradoxes; reduce noise; grow a holistic and dynamic understanding of problems; get a proper grasp of intuition and insight, heuristics and biases; be in tune with one’s emotions; and sharpen critical thinking skills in ways that enable the disclosive framing of problems, namely, bringing to surface unspoken assumptions, hidden values, and the ethical dimension. Against a background of big data and AI, the course will explore the critical role of embodied knowledge, perception, and ethical clarity for reflective judgment.

This course examines the project management process with a focus on business analytics techniques to overcome the pitfalls and obstacles that frequently occur during a typical project. Designed for business leaders responsible for implementing projects, as well as beginning and intermediate project managers. Includes topics on planning and scheduling issues, costing and budgeting, staffing and organizing, project management methodologies, and the use of data to inform the project manager’s decisions throughout the project’s lifecycle. During the course, computer software dealing with project management will also be presented.

Topics include: how to model the formation of social and economic networks; understand and measure certain patterns of real-world networks; identify, quantify and model how opinions, fads, political movements and diseases spread through interconnected systems and measure the robustness and fragility of them. We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions.

In more detail the course will include: Repetition of Statistical Definitions, Background and Network Elements, Networking, Social Networking & Behavioral Contagion, Project Management Networks, Economic complexity, Visualization of Networks.

This course provides a framework for understanding and applying prescriptive analytics (primarily optimization modeling) in various practical settings, preparing students for advanced roles in data-driven and analytical decision making roles. Prescriptive analytics is the branch of data science that examines methods and tools for direct support in quantitative decision-making problems. It builds on insights from descriptive and exploratory analysis, and utilizes forecasts from predictive analysis to build integrative models that generate actionable recommendations to achieve desired goals. Thus, its emphasis is on identifying, among the set of feasible solutions (those conforming to functional requirements and constraints) those that optimize specific decision objectives. It is widely used in many application domains such as production, supply chain management, finance, healthcare, etc., to enhance decision-making effectiveness and improve operational efficiency. This course provides comprehensive exploration of different forms of optimization models and associated solution techniques, emphasizing their applications in practical cases.

This course aims to teach students how Large Language Models (LLMs) can improve data analytics and decision-making in different fields. The course covers the basics of LLMs, focusing on how LLMs like Transformer models work and their applications in real-world scenarios. Students will learn to use LLMs effectively, understand their benefits and challenges, and explore their ethical implications. Through case studies and discussions, the course helps students develop critical thinking skills to responsibly implement and evaluate LLMs in various business functions, including Marketing and Sales, Customer Service, and HR.

Enterprises, organizations and individuals are creating, collecting, and using massive amount of structured and unstructured data with the goal to convert the information into knowledge, to improve the quality and the efficiency of their decision-making process, and to better position themselves to the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of the business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does.

The course presents the theory of financial decisions and corporate policy. It covers discounted cash flow and contemporary methods of capital budgeting (comparison of techniques, relevant cash flows, projects with different lives, optimal timing, constraints, inflation), risk and uncertainty, mean-variance portfolio choice, capital asset pricing models and arbitrage pricing theory, efficient markets, capital structure and dividend policy, basic option pricing, corporate restructuring and mergers and acquisitions.

This course explores advanced and emerging topics in the field of statistics, providing students with an in-depth understanding of specialized areas not typically covered in standard statistical curricula. Each offering of the course will focus on a specific theme, which may vary depending on current trends, faculty expertise, and student interest.

Possible topics based on the current faculty include, but are not limited to Marketing and Web Analytics, AB Testing, Data Governance, Data Management and Warehousing, Business Intelligence, Ethics and Data Privacy, Human Resources Analytics and Soft skills and Communication for Business Analysts.