Analítica financiera
This course is part of Analítica de Datos en Finanzas.
This comprehensive course introduces financial analytics methods for organizations needing data-driven financial decision-making in risk scenarios. It begins with an overview of how data transformation creates value, differentiating between data types to identify suitable models and techniques. Students will explore financial time series components and error metrics for model selection. The curriculum covers machine learning concepts and common financial models, including neural networks for predicting financial series behavior. The course also examines key financial risk metrics and their relationship with asset returns, applying descriptive and predictive models through econometric and machine learning approaches. Designed for professionals from various disciplines seeking to develop analytical skills in finance, the course requires intermediate knowledge of probability and statistics.
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What you'll learn
Understand basic concepts and importance of analytical statistical methods in financial decision-making
Use descriptive and predictive methods in financial asset time series using econometric and machine learning techniques
Identify risk assessment techniques and models to understand the risk-return relationship
Analyze statistical moments of financial series to describe general behavior
Apply neural networks to predict financial time series behavior
Implement ARCH and GARCH models for conditional volatility
Skills you'll gain
This course includes:
3.2 Hours PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course on financial analytics equips students with essential skills for data-driven financial decision-making. The curriculum progresses through four key modules: introduction to financial analytics and data transformation, time series analysis with neural networks for forecasting, risk-return relationship analysis using statistical moments and asset valuation models, and conditional volatility with market risk assessment. Students learn to apply descriptive and predictive models using econometric and machine learning approaches, particularly for understanding financial time series and risk metrics. Designed for professionals with intermediate knowledge of probability and statistics, the course emphasizes practical applications through hands-on exercises using R and R-Studio.
Introducción a la Analítica Financiera
Module 1 · 3 Hours to complete
Introducción a las Series Financieras
Module 2 · 5 Hours to complete
Introducción al Riesgo y su relación con el Rendimiento Activos
Module 3 · 3 Hours to complete
La Volatilidad Condicional y Riesgo Mercado
Module 4 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Analítica de Datos en Finanzas
Instructor
Assistant Professor
Adriana Abrego Pérez is an Assistant Professor in the Industrial Engineering Department at Universidad de los Andes. She is a member of the Probability and Statistics research group at the university.Originally from Mexico City, she is an engineer from Universidad Autónoma Metropolitana. She holds a Master's degree in Industrial Engineering and another in Quality and Productivity from Tecnológico de Monterrey, as well as a Ph.D. in Financial Sciences from EGADE Business School at Tecnológico de Monterrey. Professor Abrego has experience in project management for organizational improvement. In Mexico, she collaborated in the liaison center of the engineering school, developing analytical and quality projects in companies of the Jalisco technology cluster, including those in the technology, design, and manufacturing sectors.
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