Training program
PROGRAM STRUCTURE
PROGRAM DESCRIPTION
BASIC COURSES (MANDATORY)
Capstone projects
SELECTIVED COURSES
No. 
Modules  Credit (ECTS) 
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  Optional 

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 
PROGRAM DESCRIPTION
BASIC COURSES (MANDATORY)
 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)
 Philosophy  Vietnamese Culture
 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, RadonNikodym. Applications in optimal control for portfolio management.
 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 realworld 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)
 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 goodnessoffit. Third part is using models to conduct simulations.
 Content: Applications to market risk, Credit risk, Securitization risk, Counterparty risk, Operational risk, Liquidity risk.
 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.
 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 LongstaffSchwartz, HullWhite and BraceGatarekMusiella interest rates simulation.
Capstone projects
 Objective: Aiming to learners to be familiar with realworld 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.
SELECTIVED COURSES
 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.
 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).
 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 ISLM, credit channel, DSGE models. Methods of solving DSGE through EulerLagrange an Blanchardization.
 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, BoxJenkins time series analysis, ARIMA model, estimation and diagnosis, metadiagnosis and forecasting, intervention analysis, autoregressive models, model and forecast Evaluation... (TBD)
 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)
 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)
 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 (ER Model, relational model, normalization, SQL), datawarehouse and OLAP, securrity and integrity, data connection, storage and access, new trends for big data...