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Data Analytics

Philosophy

The ESSEC PhD program in Data Analytics, launched in 2022, provides doctoral training with advanced courses and research opportunities in econometrics, machine learning, risk analysis, statistics and related fields. The program aims at preparing the next generation of experts in data analytics, equipped with a modern skill set for the analysis of high-dimensional complex data and for the quantification of emerging risks. These skills are paramount to the evaluation and the execution of high quality decision making. With the guidance of ESSEC faculty at the forefront of research in Data Analytics and with the support of an extensive network of international collaborators, students are prepared for outstanding careers in academia, in public organizations or in data-intensive industries. Students and faculty in this program hold themselves to the highest standards of integrity and scholarship, and are deeply committed to fostering an environment that encourages creativity, diversity and collegiality.

Overview of the program

The program starts with a preparation period with compulsory and elective courses, where students discover and consolidate fundamental skills in modern Data Analytics. Compulsory courses include core econometrics and statistics, big data analytics, stochastic modeling, machine learning, optimization, Python/R programming and decision theory. Elective courses can be chosen among a large catalog at ESSEC as well as in other graduate programs. The course roadmap can be personalized in coordination with the students’ advisors and the program coordinator.

Research starts from the first year through research apprenticeship modules. Research activities intensify over the years as students focus on their dissertation under the guidance of one or multiple faculty members. The program prepares PhD candidates to make significant contributions to their fields, to publish articles describing these advances and to present them at international conferences.

The program supports the students’ participation in seminars and workshops by developing their communication skills and providing networking opportunities at and outside of ESSEC.

Subfields

Data Analytics research aims at developing mathematical, statistical and computer methods and tools to solve business problems. The approach may involve e.g. stochastic modeling, empirical testing of theories, risk analysis and management, stochastic processes, econometrics and forecasting.

This subfield is hosted by the IDS department and the CREAR research center.

Recent research interests of our faculty include:
- Agent based models of learning
- Assessment of Climate Change
- Extreme Value Theory
- Multivariate and high-dimensional Statistics
- Predictive Path Modeling
- Probability
- Risk analysis & management

The courses in the DA specialization cover the following topics: advanced statistics, big data analytics, forecasting, introductory and advanced econometrics, quantitative risk management, statistical modelling, stochastic processes, time series analysis.

Several softwares and programming languages are also presented to the students in the courses.

The PhD students in this DA specialization should be able at the end of the program to combine two skills :
a) designing and developing new models and methods for statistics, stochastic modeling and econometrics with proven analytical properties, and
b) applying tools and devloping methods for applications in economics, finance, marketing, operations, risk analysis and management, or business as a whole.

Faculty

The core faculty in Data Analytics at ESSEC is an international team across the Cergy/Paris and the Singapore campuses. We are hosted by the Department of Information Systems, Decision Sciences and Statistics, and the list of faculty specialized in Applied Probability, Econometrics, Statistics.

In their research, our faculty contributes to active topics in applied probability, Bayesian inference, computational statistics and simulation methods, econometric theory, extreme value theory, forecasting, high-dimensional statistics, machine learning. Our main themes of research are described here.

Our team regularly publishes in the best journals in statistics, econometrics and applied probability, and is well connected, through on-going collaborations and partnerships, to other leading experts in France and worldwide. We host seminar series at which renowned researchers present their latest works (see eco-stat-seminars and WGRisk-seminars). We are part of active research groups such as the CREAR - Center of Research in Econo-finance and Actuarial sciences on Risk and the Metalab for Data, Technology and Society.

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Guillaume CHEVILLON is Professor in the Department of Information Systems, Decision Science & Statistics and holds a D.Phil., in Economics from the University of Oxford. His research interests lie in time series econometrics and forecasting, with a special interest in Macroeconomics and Finance.

Vincenzo ESPOSITO VINZI is Professor in the Department of Information Systems, Decision Science & Statistics and holds a Ph.D., in Computational Statistics from the University of Naples. His research interests focus on multivariate statistics, structural equation modeling, PLS regression and path modeling, with business and industry-oriented applications. He is the President of the International Society for Business and Industrial Statistics and the Chairman of the European Board of Directors of the International Association for Statistical Computing. He is an Associate Editor of Computational Statistics and Data Analysis, Computational Statistics, Advances in Data Analysis and Classification and Statistical Methods & Applications. He is the Editor-in-Chief of the "Handbook of Partial Least Squares: Concepts, Methods and Applications" by Springer.

Olga KLOPP is Associate Professor in Statistics at ESSEC Business School. She holds a Ph.D. in Mathematics & Applied Mathematics (National University of Mexico) and an Habilitation degree (HDR) in high-dimensional Statistics (Nanterre University, Paris X). Olga’s research focus is in high-dimensional statistics, and especially, in matrix completion and network models. She is a member of the ESSEC risk research center CREAR and of CREST, as well as associated editor of Bernoulli.

Marie KRATZ is Professor in the Department of Information Systems, Decision Science & Statistics and holds a Doctorate in Applied Mathematics (UPMC Paris 6 & Center for Stochastic Processes, UNC Chapel Hill, USA). Her research addresses a range of topics in applied probability, mathematical statistics and actuarial mathematics, with a focus on extreme value theory, risk analysis and Gaussian processes. She is the director of CREAR, the Center on Risk at ESSEC, launched with the collaboration of the insurance industry and international academic partners. She is also President of the group “Banque, Finance, Assurance” at the French Society of Statistics.

Jeroen V.K. ROMBOUTS is Professor in the Department of Information Systems, Decision Science & Statistics and holds a Ph.D., in Economics from CORE, Universite Catholique de Louvain. His research interests focus on econometrics, finance, time series, nonparametric statistics and Bayesian inference with forecasting applications in the field of macroeconomics and finance. He is elected member of the International Statistical Institute and serves on the editorial board of International Journal of Forecasting, and Computational Statistics and Data Analysis.

Recent placement of Alumni

The concentration in Data Analytics has just been created, but its faculty have a strong record in the supervision of PhD students, who went on to become faculty at Bocconi University in the Marketing department, at University College of Dublin in the Economics department, at Purdue University in the Statistics department, at Universidad de las Fuerzas Armadas in Ecuador and ESPRIT School of Engineering, Tunis. Our former students received awards or prizes such as the Savage Award (finalists) by the International Society for Bayesian Analysis, the Laplace Award by the American Statistical Association, or the Prix des Sciences du Risque. Outside of academia our former students have started their careers at the European Central Bank, in national institutes, or in the financial and technological industries.

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Huali Wu | PhD in Economics (4th year)

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