This module introduces the core concepts and foundations of data science, providing an overview of the field and its applications in business contexts.

This module covers the essential mathematical concepts and techniques required for data science, focusing on linear algebra, calculus, and other mathematical foundations needed for advanced analytics.

This module focuses on statistical methods and probability theory used in data analysis, helping students understand statistical inference, hypothesis testing, and probability distributions.

This module covers techniques for efficient data storage, retrieval, and management, including database systems, data warehousing concepts, and data management best practices.

This module focuses on implementing machine learning algorithms using Python, covering supervised and unsupervised learning techniques for predictive modeling and pattern recognition.

This module teaches techniques for interpreting, visualizing, and analyzing data using R programming language, focusing on creating meaningful visualizations and deriving insights.

This module covers econometric methods and their implementation using R, focusing on statistical models for analyzing economic data and relationships between variables.

This module explores stochastic processes and simulation techniques for modeling systems with random components, providing tools for risk analysis and decision-making under uncertainty.

This module focuses on methods and techniques for analyzing categorical data, including contingency tables, logistic regression, and other approaches specific to non-continuous variables.

This module covers the application of data analytics techniques for business intelligence, focusing on transforming data into actionable insights for strategic decision-making.

This module focuses on technologies and methodologies for storing and analyzing large volumes of data, including distributed computing frameworks and NoSQL databases.

This module explores cutting-edge topics in data science such as text mining, deep learning, IoT, and Industry 4.0, exposing students to the latest developments in the field.

This capstone module involves practical application of data science concepts through real-world projects, allowing students to integrate and apply their learning in solving business problems.