Continuous professional development program in Data Science

Connect data science with business value creation

Data allows us to understand the past and anticipate the future. Finance allows us to turn that knowledge into strategic decisions.

Through this program, you will develop a comprehensive vision to interpret the economic impact of projects, understand how value is generated in organizations, and strengthen your ability to participate in decision-making processes with a business-oriented perspective.

Why doing this course?

• Understand how organizations create value.
• Incorporate financial tools to evaluate projects and opportunities.
• Boost the impact of your analysis on decision-making.
• Connect the language of data with the language of business.
• Develop a strategic vision to lead evidence-based initiatives.

AI Agents are redefining the way we work, innovate, and make decisions.
Incorporate the necessary tools and knowledge to understand how they work and how to implement them in real-world contexts.

Why doing this course?

• Understand how intelligent agents make decisions through Markov Decision Processes (MDP), one of the fundamental frameworks of Reinforcement Learning.
• Identify when to use prediction or control strategiesUnderstanding its advantages, limitations, and applications in different scenarios.
• Put theory into practice by implementing Reinforcement Learning algorithms such as Q-Learning and SARSA using Python and specialized libraries.
• Explore current applications of Deep Reinforcement Learningused in areas such as robotics, automation, process optimization and intelligent systems.
• Develop technical skills to analyze and evaluate resultsexperimenting with practical cases that reflect real industry challenges.

In the last decade, its impact has grown remarkably in areas such as robotics, recommendation systems, finance, video games and logistics optimization, also consolidating itself in commercial applications beyond the academic field.

This course offers a balanced combination of theoretical foundations and practical application in Python, preparing participants to understand the essential mechanisms of RL and apply them effectively to real-world problems.

At the end of the course, you will be able to:

• Understanding the conceptual structure of Markov Decision Processes (MDPs) as
  formal framework for sequential decision problems.
• Distinguish between prediction and control methods, identifying their characteristics, advantages and
  operational limitations.
• Implement classic RL algorithms (Q-Learning, SARSA) using standard libraries in
  Python
• Recognize contemporary applications of deep RL and their implications in various
  industrial sectors.
• Analyze practical cases through computational experimentation, interpreting results with
  appropriate technical criteria