Managing financial risk involves integrating financial judgment, data, and quantitative models to assess scenarios, anticipate impacts, and support decisions in real market contexts.
La Diploma in Quantitative Analysis and Machine Learning Applied to Financial Risk It proposes an applied training that articulates quantitative analysis and finance, aimed at professionals who seek to improve the quality of their decisions, strengthen their analyses and work with risk with greater precision and foundation.
Develop a quantitative perspective to understand, model, and anticipate financial risk.
This diploma program is designed for professionals who work, or want to work, at the intersection of data, finance, and decision-making, and who feel that today they need more than isolated tools to analyze scenarios, assess risks, and substantiate their decisions.
It is aimed at both finance professionals seeking to improve the quality and soundness of their decisionsLike Data profiles that see financial risk as a concrete opportunity for specialization and professional growth.
Whether your starting point is finance or the world of data, the program is designed to help you integrate both languages and use them in an applied way in real-world contexts.
This diploma program was designed for professionals seeking to take a leap forward in how they analyze, decide, and justify their decisions, bringing quantitative analysis to real-world contexts of financial risk.
It's not about learning more tools, but about raising the quality of the analysis, gaining professional solidity and expanding the scope of action in contexts where uncertainty is the rule.
Choosing this diploma means choosing training with academic backing, an applied approach, and a professional perspective aligned with the real challenges of financial analysis.
Our differentiators are:
Real integration between finance and data science: A proposal designed from the intersection between both fields, which articulates financial criteria and quantitative analysis to address real problems of risk and decision-making.
Teachers with real experience in the sector: Professors with experience in banking, financial markets, quantitative risk and data science, who combine professional practice and academic training.
Approach applied with real market data: I work with real information from the financial market and methodologies geared towards solving specific problems of risk analysis and management.
Academic backing from a leading institution: We are proud to have a solid track record in education in finance and data science.
Modeling and analyzing financial risk using real market data, integrating financial criteria and quantitative techniques.
Working with financial and market information in Python, applying key tools for data analysis, modeling, and exploration.
Build and evaluate statistical and machine learning models focused on financial analysis and risk measurement.
Interpreting results and translating them into decisions, Connecting the world of data with financial and strategic language.
Designing scenarios and evaluating impacts to anticipate risks and improve the quality of decision-making in real-world contexts.
Addressing specific financial risk problems with an applied, replicable methodology aligned with real professional situations.
This tour allows you to take criteria, tools and transferable capabilities to your professional practice, whether you make financial decisions, develop analytical models, or work as a liaison between finance and data science.
The program is organized into progressive modules that combine financial fundamentals, quantitative models and advanced machine learning techniques, with a focus on the analysis and management of financial risk.
Presentation of the diploma program, its objectives, content, faculty, and working methods. Introduction to the Final Integrative Project and the program's applied approach.
Analysis of the main types of financial risk, measurement of risk exposure and understanding of market factors and volatility as a basis for decision making.
Application of classic risk models such as GARCH, Monte Carlo simulation and VaR (parametric and historical), with a focus on their practical use and limitations.
Introduction to derivative instruments, valuation of options using the Black-Scholes-Merton model, analysis of Greeks and study of the implied volatility surface.
Use of supervised models such as Random Forest and XGBoost for risk problems. Attribute engineering, anomaly detection, and explainability tools (SHAP) applied to financial data.
Application of neural networks (ANN and LSTM) to the analysis of financial series, comparing their performance with traditional quantitative models.
Development of an applied case study on financial risk using real data. The project includes an analysis notebook, a technical report, and a final integrative presentation.
During the diploma course you will work with key tools and languages for financial risk analysis and modeling:
The program begins on August 31th, 2026 and it has a duration of 3 monthsThe modality is 100% onlineThe course includes live classes, recorded materials, and a hybrid or in-person closing session. The sessions are held on Mondays and Thursdays from 19 to 21 pm.
The methodology combines weekly live classes with recorded materials for self-paced study, guided exercises in Google Colab, and technical forums. Everything is designed to enable you to apply your knowledge to real-world financial risk problems, consolidating what you've learned in a... Final Integrative Project evaluated in a personalized manner.
La Universidad Austral has exclusive enrollment benefits regarding current values. These include: corporate agreementsSouthern Community, distance benefits, early registration, among others.
They are subject to quotaFor more information, please consult with the executive in charge.
Actuary and PhD in Economics (UBA). Specialist in risk management, financial modeling, and machine learning applied to finance. Director and postgraduate lecturer with extensive experience in banking, capital markets, and the development of quantitative models for strategic decision-making.
Master's and Bachelor's degrees in Statistics, specializing in data analysis and research. Experience in interpreting information for strategic decision-making. Executive Director of the Regional Master's Program in Data Science Rosario de Universidad Austral.
Industrial Engineer and MSc. in Data Science. Senior Business Planning Analyst at Pampa Energía and Professor of Time Series Analysis at the Universidad Australwith experience in Deep Learning and data modeling for financial decisions.
Professor in the Universidad AustralSpecialized in Machine Learning and data analysis applied to financial risks. Extensive experience in algorithm development, numerical optimization, and cloud-based software platforms, combining technical, analytical, and business knowledge for predictive modeling and data-driven decision-making.
Bachelor of Statistics (UNR), highest average in his cohort, and currently pursuing a Master's degree in Data Science (Universidad AustralUniversity lecturer and data analyst, with experience in statistical inference and applied research on technological adoption in Argentine agriculture.
Data Scientist and Master in Applied Statistics, with over 15 years of experience in statistical modeling, time series, and forecasting. Postgraduate lecturer and consultant, specializing in quantitative analysis applied to business and decision-making in public and private organizations.
Specialist in economic and financial risk management, with over 20 years of experience in the supervision and control of financial institutions. He works as a trainer and postgraduate lecturer, providing a regulatory and applied perspective on comprehensive risk management in the financial system.
Industrial Engineer, Master in Finance and Applied Statistics, specializing in data analysis and financial modeling. Expert in quantitative finance and algorithmic trading, with experience in model design and automation in local and international markets.
Actuary and data analyst with extensive experience in risk analysis, statistical modeling, and the development of indicators for financial decision-making. University professor of time series and predictive methods applied to finance, with a background in banking, insurance, and data science.
Industrial Engineer and PhD in Operations Research, specializing in optimization, simulation, and machine learning applied to complex business and industry problems. Extensive experience in international consulting and postgraduate teaching in data science, operations research, and artificial intelligence.
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