Training program



  Modules Credit
Required /
Optional Course
1 M1 -S1
Probability & Statistics 8 Required
2 Stochastic Calculus 8
3 Risk Analysis 8
1 Leadership Development 6 Optional
2 Machine Learning and Data Mining 1 6
3 Linear Algebra and Optimization 6
4 Database and Information Systems 6
1 M1 - S2
Philosophy 4  
1 Models for Risk Management 8 Required
2 Derivative Pricing in Practice 8
3 Basel 2 and 3 regulations 8
4 Capstone Projects 6
1 Time series analytics and forecasting 6 Optional
2 Decision Analysis 6
3 Machine Learning and Data Mining  2 6  
1 M2 - S1
Introduction to Scientific Research Method 8 Required
1 Macroeconomics 6  
2 Leadership Development 6
3 Machine Learning and Data Mining 6
4 Linear Algebra and Optimization 6
5 Database and Information Systems 6
1 M2 - S2
Thesis 30  


  1. Probabilityand Statistics
  • Objective: The aim of this course is to provide the basic mathematical concepts for study in data analytics. The students are expected to acquire basic conceptsand methods of mathematics for data analytics and skills of using popular software of data analytics.
  • Content: Basic concepts of probability and statistics, probability distributions, estimation and hypothesis testing, ANOVA, regression analysis, analysis of categorical data... (TBD)
Suggested textbook: [1] Johnson R.A., Bhattacharyya G.K. Statistics-Principles and Methods, John Wiley & Sons, Inc., Fourth Edition. [2] Montgomery D.C., Runger G.C., Applied Statistics and Probability for Engineers, Wiley, Fourth Edition.
  1. Philosophy - Vietnamese Culture
  2. Stochastic Calculus
  • Objective: The aim of this course is to provide the understanding of stochastic calculus concepts for solving differential equations in random variables. Students must develop insights on the similarities and differences with ordinary differential equations.
  • Content: Basic concepts of stochastic analysis , Brownian motion, density transform and stochastic process change, Girsanov, Radon-Nikodym. Applications in optimal control for portfolio management.   
Suggested textbook: Stochastic Differential Equations : An introduction with Applications, Bernt Oksendal,  Universitext  (2010).
  1. Risk analytics
  • Objective: Introduction to models and quantitative methods for risk analytics, especially risks in finance and market… The learners, on the one hand, study methods of risk analytics and on the other hand, practice the analysis of risks using real-world datasets.
  • Content: Financial products explained, fundamentals of quantitative finance, market risk analytics, counterparty credit risk analytics, liquidity risk management, risk data aggregation and reporting... (TBD)
Suggested textbook: Raghurami Reddy Etukuru, Enterprise Risk Analytics for Capital Market. Proactive and Real-Time Risk Management, 2014.
  1. Models for risk management
  • Objective : Acquisition of the capacity to apply Risk analytics theory to concretely build models of simple to advanced risks. First part is parametrization of models from data through different regression techniques, Bayesian inference or using Likelihood Maximization principle. Second part is tests of goodness-of-fit. Third part is using models to conduct simulations. 
  • Content: Applications to market risk, Credit risk, Securitization risk, Counterparty risk, Operational risk, Liquidity risk.
Suggested textbook : [1] Mathematics and Statistics for Financial Risk Management, Michael B. Miller, Wiley (2013)  [2] Credit Risk Modelling using VBA and Excel, G. Loeffler & P. Posch, Wiley Finance (2011)
  1. Basel 2 and 3 regulations
  • Objective: Presentation of the Basel 2 and 3 Accords, their Pillar I, II and III. The organizational means for controlling and auditing banks. The legal prerequisites to applying formulae of regulatory capital calculation and the obligations that are attached.
  • Content : starting from the legal framework applied by the central banks signatories of the Basel Accords, the student will learn the mathematical formulae applicable for each of the 6 domains of risk : market, credit, securitization, operational, liquidity and systemic.
Suggested textbook : (free downloadable)The Basel Committee Banking Supervision series of Explanatory Notes
  1. Derivative pricing in practice
  • Objective: train students in methods topraticepricingderivative products for markets on stocks, interest rates, commodities, up to baskets of hybrid products of second and later generations.
  • Content: parameter values extraction from databases, use of kernel density distributions, simulation algorithms around Monte Carlo and different methods used for discrepancies reduction. Programming is done using VBA, C++ or Java. Applications on vanilla products, but also on American options using Longstaff-Schwartz, Hull-White and Brace-Gatarek-Musiella interest rates simulation.  
Suggested textbook : Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability),  Glasserman P., Springer (2010)
Capstone projects
  • Objective: Aiming to learners to be familiar with real-world data analytics, including basic skills for research and application, determine a concrete problem and solve it by data analysis methods, literature investigation and write the research proposal for the next step.
  • Content: Methodology for scientific research, collection and analysis of literature and related work, problem forrmulation, writing a research proposal, carrying out the above content for a data analytics problem.
  1. Leadership development: Analysis to Action
  • Objective: Experiment and discover for self the elements of leaderships that lead the students to being a leader exercising effectively leadership as self natural expression. Awareness and recognition of related contexts to a situation one has to deal with will contribute consequently to the interpretation of data aiming at fulfilling the expectations of all parties involved in the situation.
  • Content: Experiment and discover for self (i) the foundations of leadership, (ii) the aspects of contextual framework of leadership, and (iii) the ontological constraints that limit one’s observation, expression and interpretation of data.
Suggested textbook:  W. Erhard, M. Jensen, S. Zaffron, K. Granger. Being a Leader and the Effective Exercise of Leadership: An Ontological/ Phenomenological Model.  Harvard Business School NOM 09-038, 2015. ( ). See more at:
  1. Linear Algebra
  • Objective: The aim of this course is to provide the basic concepts and computation in linear algebra used for study in data analytics. The students are expected to acquire basic conceptsand methods of linear algebra for data as well as skills of using tools.
  • Content: Basic concepts of linear algebra including vector spaces, matrix calculation, least squares solutions, orthogonalization, properties of determinants, eigenvalues and eigenvectors, symmetric matrices and posititve definite matrices, linear transformations... (TBD). 
Suggested textbook: Gilbert Strang, Introduction to Linear Algebra, Fourth Edition
  1.  Macro Economics
  • Objective: To provide concepts and methods from fundamentals to advanced notions in macroeconomic theories. Students will master building simple models along main theories coming from the different schools of thought and learn to apply them in concrete forecasting exercises.
  • Content: Origins and evolutions of the different schools of thinking along with macroeconomic  events that caused upheavals in successive theories. Fundamental ideas from Pareto, Keynes, Friedman, Lucas, Hansens, and the resulting New Neoclassical Synthesis. Practice on IS-LM, credit channel, DSGE models. Methods of solving DSGE through Euler-Lagrange an Blanchardization.
Suggested textbook: Macroeconomic theory, Michael Wickens, Princeton University Press, Second edition.
  1.  Time series analytics and forecasting
  • Objective: To provide the fundamental theory of time series analysis and forecasting. The learners also practice the methods on time series datasets.
  • Content: Extrapolative and decomposition models, Box-Jenkins time series analysis, ARIMA model, estimation and diagnosis, metadiagnosis and forecasting, intervention analysis, autoregressive models, model and forecast Evaluation... (TBD)
Suggested textbook: (1) Robert Yaffee, Monnie McGee, An introduction to time series analysis and Forecasting, (2) Peter J. Brockwell, Richard A. Davis, Introduction to Time Series and Forecasting, 2nd Edition 2010, Springer.
  1.  Decision analysis
  • Objective: The aim of this course is to provide the fundamental ideas and methods about decision analysis. The learners are aimed to understand the key methods of this field and their practical application as well.
  • Content: Introduction to decison analysis,uncertainty decision modeling, preference decision modeling, decision support systems... (TBD)
Suggested textbook: Robert T. Clemen and Terence Reilly, Making Hard Decisions with DecisionTools, Third Edition, 2013.
  1.  Machine learning and Data mining
  • Objective: The aim of this course is to provide the basic concepts and methods of machine learning and data mining as key tools of data analytics. The students are expected to acquire basic concepts, methods and skills offormulating the problems, using software to analyze the data and interpret the results.
  • Content: Basic concepts and methods of machine learning and data mining, including data preprocesing, methods for analyzing labelled and unlabelled data, evaluation of learned knowledge, text and web mining... (TBD)
Suggested textbook:J. Han and M. Kamber, Data Mining: Concepts and Techniques, Third Edition.
  1.  Databases and Information systems
  • Objective: The aim of this course is to provide the basic concepts and content about databases and information systems for study in data analytics. The students are expected to acquire basic concepts, methods and skills of organizing, accessing and exploiting data/information systems.
  • Content: Basic concepts of databases and information systems such as data models (E-R Model, relational model, normalization, SQL), datawarehouse and OLAP, securrity and integrity, data connection, storage and  access, new trends for big data...