Data Science

Overview courses

• Introduction to Data Science

  This course provides an overview of the core concepts of data science, including data collection, preprocessing, and exploratory data analysis. Students will gain hands-on experience with fundamental programming tools. The course covers the ethical considerations of data handling and introduces basic visualization techniques to communicate insights effectively.

• Statistical Methods for Data Analysis

  This course explores essential statistical techniques used in data analysis. Topics include probability distributions, hypothesis testing, confidence intervals, and regression analysis. Students will learn to apply statistical methods to real-world datasets, interpret results, and assess the reliability of conclusions drawn from data.

• Predictive Analytics and Machine Learning

  This course focuses on predictive modeling techniques, covering supervised and unsupervised learning algorithms. Students will explore classification, clustering, and regression models, including decision trees, neural networks, and support vector machines. Practical assignments will allow students to train, evaluate, and optimize machine learning models.

• Optimization and Artificial Intelligence

  This course delves into optimization techniques and AI methodologies that drive modern decision-making. Topics include linear and nonlinear optimization, constraint satisfaction problems, and reinforcement learning. Students will learn how AI-powered systems improve efficiency in various applications, from logistics to finance and healthcare.

• Capstone Project in Data Science

  The final course challenges students to apply their acquired knowledge to a real-world problem. Working individually or in teams, students will define a research question, gather and analyze data, and develop a data-driven solution. The course emphasizes problem-solving, project management, and effective communication of findings through written reports and presentations.