Data Science

GENERAL DESCRIPTION The goal of this course is to provide an overview of stochastic processes, with applications to Data Science in mind. Stochastic processes and probability are important in data science because they can be used to model and analyze a wide range of data sets, from financial data to sensor data. The course will cover three parts: a gentle introduction to combinatorial stochastic processes, Gaussian processes, and probabilistic causality. Programming in R, Matlab or Python is useful, but it is not essential. Programs in R will be used. SPECIFIC OBJECTIVES: 1. Knowledge and understanding: Understand the basics of combinatorial stochastic processes and Gaussian processes, and their applications in data science. Understand the fundamentals of probabilistic causality and be able to apply these concepts to real-world data science problems. 2. Application: Apply stochastic process to real-world data sets, using programming languages such as R, Matlab, or Python. 3. Autonomy of judgement: Analyze the benefits and limitations of different stochastic process models and determine the best model to use for a given data set. 4. Communication: Communicate effectively about stochastic processes, including design constraints, solutions, and potential applications. 5. Learning skills: Develop studies in the field of stochastic processes for data science, including the ability to undertake research in this area.