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Fundamentos de probabilidad y aplicaciones

Mario Castillo, Astrid Bernal, and Daniela Angulo

Actualmente la probabilidad se ha convertido en una disciplina fundamental para científicos, ingenieros, economistas y administradores. La probabilidad es una poderosa herramienta, pero es, ante todo, una forma de pensar. Tanto en el mundo de los negocios, como en el campo de la salud y en las ciencias sociales, entre otros, cada vez es más relevante el entendimiento de los fenómenos y situaciones de naturaleza probabilística (no determinística), y de desarrollar modelos basados en el análisis de datos para cuantificar el riesgo con el propósito de tomar mejores decisiones. Es entonces importante entender los conceptos básicos sobre los cuales se establecen los pilares de esta disciplina, los cuales permitirán más adelante convertir la probabilidad en una herramienta básica para la conceptualización y la solución de problemas reales.

Read more

Actualmente la probabilidad se ha convertido en una disciplina fundamental para científicos, ingenieros, economistas y administradores. La probabilidad es una poderosa herramienta, pero es, ante todo, una forma de pensar. Tanto en el mundo de los negocios, como en el campo de la salud y en las ciencias sociales, entre otros, cada vez es más relevante el entendimiento de los fenómenos y situaciones de naturaleza probabilística (no determinística), y de desarrollar modelos basados en el análisis de datos para cuantificar el riesgo con el propósito de tomar mejores decisiones. Es entonces importante entender los conceptos básicos sobre los cuales se establecen los pilares de esta disciplina, los cuales permitirán más adelante convertir la probabilidad en una herramienta básica para la conceptualización y la solución de problemas reales.

El curso tiene como objetivo principal que los participantes adquieran una formación sólida en los conceptos más importantes de probabilidad, y sus aplicaciones. Se hace especial énfasis en que los participantes logren una adecuada comprensión y utilización de los modelos de naturaleza probabilística en la solución de problemas de la vida real que comportan riesgo e incertidumbre.

El curso tiene un buen balance entre el rigor en el tema y la presentación del contenido de una forma simple, con base en ejemplos sencillos basado en situaciones reales, y cuenta con videos y ejercicios orientados a facilitar la comprensión de los conceptos que se cubren.

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What's inside

Syllabus

Introducción
Conceptos básicos de probabilidad
Bienvenidos al primer módulo de este curso, aqui trataremos conceptos básicos de probabilidad, eventos y sus propiedades, probabilidad condicional, árboles de probabilidad y técnicas de conteo. Tendrá una duración de dos semanas.
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Variables aleatorias discretas
En este segundo módulo nos ocuparemos de estudiar variables aleatorias y distribuciones de probabilidad discretas, así como de los conceptos de valor esperado y varianza para este tipo de variables. En particular, nos ocuparemos de conocer en detalle algunas de las distribuciones discretas de mayor aplicación, tales como las distribuciones de Bernoulli, Binomial, Geométrica, Binomial Negativa y de Poisson. Este módulo también tendrá una duración de dos semanas.
Variables Aleatorias Continuas
En este módulo cubriremos variables aleatorias y distribuciones de probabilidad continuas, así como los conceptos de valor esperado y varianza para este tipo de variables. En particular, nos ocuparemos de conocer en detalle algunas de las distribuciones continuas de mayor aplicación, tales como las distribuciones Uniforme, Exponencial y Normal. Como los módulos anteriores, este módulo también tendrá una duración de 2 semanas.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Desarrolla habilidades en la cuantificación del riesgo y la toma de decisiones bajo incertidumbre
Ofrece una comprensión sólida de los conceptos fundamentales de probabilidad
Explora aplicaciones de la probabilidad en diversas áreas, como negocios, salud y ciencias sociales
Presenta contenido de forma simple y clara, facilitando la comprensión
Cubre tanto variables aleatorias discretas como continuas
Impartido por instructores expertos en probabilidad

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Reviews summary

Probabilidad y aplicaciones

Este curso es muy bien recibido por los estudiantes, que elogian a los instructores, los materiales del curso, el ritmo y la claridad de las lecciones. Muchos estudiantes comentaron que este curso es un excelente repaso o introducción a los conceptos básicos de probabilidad.
Ritmo pausado y comprensible
"Los profesores explican de manera agradable..."
"Hace que veas todo desde otra perspectiva..."
"Excelente curso, lleno de herramientas claves..."
Excelente introducción a los conceptos básicos
"El curso es muy últil para aprender nociones básicas..."
"Aunque hay algunos conceptos que no cubren..."
"Recomiendo a las personas que no tengan mucho conocimiento..."
Materiales de apoyo y ejemplos prácticos
"Excelente curso. Aprendí cosas nuevas..."
"Los casos de aplicación son interesantes y muy didácticos"
"El material de estudio es óptimo."
Profesores expertos
"Excelente curso para aprender la probabilidad..."
"Los docentes cuentan con un buen manejo de los temas..."
"Recomiendo a las personas que no tengan mucho conocimiento..."

Activities

Coming soon We're preparing activities for Fundamentos de probabilidad y aplicaciones. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Fundamentos de probabilidad y aplicaciones will develop knowledge and skills that may be useful to these careers:
Risk Manager
Statisticians with a probability background are often hired into the role of Risk Manager to help handle and mitigate risk. This course teaches the fundamentals of probability that are necessary to succeed in this role. The course also includes concepts like decision-making under uncertainty, which is a crucial skill in risk management. Additionally, the course discusses the basics of probabilistic models, which can be used to analyze risk in various applications.
Business Analyst
Business Analysts often use statistics and probability to make sense of data trends. This course will equip them with the fundamental knowledge of probability essential in this field. The topics covered in this course, such as probability distributions and expected value, are key components of business intelligence and data analysis used by Business Analysts to help improve business outcomes.
Data Analyst
Data Analysts use probability and statistics to help understand patterns, make predictions, and draw conclusions from data. This course will lay the foundation for probability, providing a solid foundation for data analysis. Concepts such as probability distributions and expected values are covered in this course and are essential for the data analysis process.
Financial Analyst
Probability and statistics are extensively used in finance to assess risk and make informed investment decisions. This course will help build a solid foundation in probability and its application in practical financial problems. The course will also equip learners with the knowledge to use probabilistic models, a vital tool in modern finance.
Quantitative Analyst
Many Quantitative Analysts leverage techniques from probability and statistics to build rigorous mathematical models to support decision-making in the financial industry. The probability knowledge provided by this course will be essential for Quantitative Analysts, as well as the ability to think probabilistically to build analytical frameworks to solve business problems.
Biostatistician
For Biostatisticians interested in modeling biological data, the principles of probability are critical. This course will provide the foundation to understand the probabilistic models used to analyze biological data, including distributions like the Poisson, binomial, and normal distributions. The course also covers topics like hypothesis testing and confidence intervals, essential for statistical inference in biology.
Actuary
Actuaries often work with insurance companies and leverage probability concepts to assess risk, particularly in insurance and financial contexts. This course will lay the groundwork in probability, and while it does not cover specific actuarial models, it provides a vital foundation for the probabilistic thinking required for actuarial work.
Machine Learning Engineer
Machine Learning Engineers utilize probability to create and enhance machine learning models. This course will develop the probability foundation necessary for this role. Topics such as probability distributions and expected value are key components of machine learning algorithms and models used by Machine Learning Engineers to train and evaluate models.
Data Scientist
Data Scientists analyze data to help discover patterns and insights. Probability is a core concept used in statistical modeling, which is essential for Data Science. This course will provide the foundation in probability and its applications to prepare for a career in Data Science.
Operations Research Analyst
In the field of Operations Research, probability is key in analyzing complex decision-making problems under uncertainty. This course provides a solid understanding of the fundamentals of probability, preparing learners to apply probabilistic models to solve real-world problems, as is often required of Operations Research Analysts.
Statistician
Statisticians rely on probability theory to collect, analyze, interpret, and present data. It is a foundational course for aspiring Statisticians, as it covers core concepts such as probability distributions and hypothesis testing.
Software Developer
Software Developers may find this course helpful as probability is used in various software applications, such as simulations, probabilistic programming, and artificial intelligence. This course will provide a strong foundation in probability concepts, giving Software Developers an edge in these areas.
Market Researcher
Market Researchers use probability and statistics to gather and analyze market data. This course can be beneficial as it will give Market Researchers a good foundation in probability concepts, such as sampling and hypothesis testing, which are essential for conducting effective market research.
Economist
Understanding probability is important for Economists, as it provides a framework for analyzing economic phenomena and making predictions. This course will introduce the fundamental probability concepts relevant to Economics.
Epidemiologist
Epidemiologists often use probability and statistics to study the distribution and patterns of health-related states or events in defined populations. This course will help build a strong foundation in the use of probability distributions in modeling disease incidence, prevalence, and risk.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Fundamentos de probabilidad y aplicaciones.
Este libro es un texto clásico que cubre los conceptos fundamentales de probabilidad y estadística, con un estilo claro y accesible.
Classic reference on probability theory and mathematical statistics, providing a comprehensive and in-depth treatment of these subjects.
Provides a rigorous and comprehensive treatment of statistical inference, including topics such as point estimation, hypothesis testing, and confidence intervals.
Provides an introduction to probabilistic graphical models, which are powerful tools for representing and reasoning about complex probabilistic relationships.
Is specifically designed for students in the life sciences, providing a practical introduction to statistical methods used in biological and medical research.
Provides a more in-depth and mathematical treatment of probability and statistics, including topics such as statistical inference, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to Bayesian analysis, a powerful statistical method that allows for the incorporation of prior knowledge and uncertainty.
Provides a thorough treatment of longitudinal data analysis, which is commonly used in the analysis of data collected over time.

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