Master of Science in Data Science and AI from EU Global

The module focuses on developing statistical thinking to set a solid foundation for rest of the modules. It involves introduction to the statistical concepts and tools widely used for Data Analysis and helps in effective decision making. You will be working upon multiple case studies, exercises to develop critical thinking and data analytical approach.

Mathematics for Data Science builds essential math skills for data analysis, statistics, and machine learning. It combines theory and practical applications to help students confidently tackle real-world data problems and kickstart their journey into machine learning and AI.

Python for Data Analytics equips students with a strong foundation in Python programming, starting from basic scripting to advanced data science libraries. Students will explore powerful Python libraries to solve real-world problems in data analysis and visualization.

Data Storytelling teaches students how to turn data into compelling narratives. They will learn to source, analyze, and visualize datasets, uncover meaningful insights, and communicate findings effectively across visual, written, and oral formats. The module helps students with the tools and skills to present data-driven stories tailored to different audiences.

Machine Learning & AI offers in-depth coverage of core concepts in supervised and unsupervised learning, along with popular algorithms and real-world applications. The module balances theory with hands-on practice, enabling students to design intelligent solutions using machine learning and deep learning techniques across diverse domains.

Machine Learning with Python familiarizes students with key algorithms in regression, classification, and clustering. Through real-world datasets, students will build predictive models and data segmentation. They will also learn how to structure machine learning solutions as deployable data products. By module end, students will be able to implement basic ML algorithms in Python and generate actionable insights.

This module is designed to provide an in-depth understanding of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), two fundamental architectures in the field of deep learning. Students will gain hands-on experience in designing, implementing, and optimising these neural network types for various applications, including image recognition, natural language processing, and sequential data analysis.

Master computer vision and image recognition algorithms. Build real world applications by mastering computer vision and pattern or object recognition techniques.

Natural Language Processing offers a comprehensive introduction to modern NLP techniques, focusing on practical tools and workflows using Python. Students will learn to manage and analyze textual data across domains, gaining hands-on experience through individual projects. The course blends theory with practical application, equipping students to perform meaningful linguistic analysis using cutting-edge NLP methods.

The broad rise of large information stockpiling needs has driven the birth of databases generally alluded to as NoSQL information bases. This course will investigate the sources of NoSQL information bases and the qualities that recognize them from customary data set administration frameworks. Central ideas of NoSQL information bases will be introduced.

Data Warehousing & Management equips students with the essential skills to design data warehouses and build data integration workflows using tools like Oracle, MySQL, or PostgreSQL. The module covers key concepts such as architectures, multidimensional models, and organizational practices, blending hands-on design with real-world business intelligence needs.

Research Methodology provides students with essential skills to design, conduct, and present research effectively. Covering both qualitative and quantitative methods, the course teaches how to frame research questions, collect and analyze data, and write research proposals. It emphasizes ethical practices and systematic scientific writing, preparing students for academic and professional research across disciplines

Capstone Consulting Project & Master Thesis is the final milestone for students specializing in Data Science and AI. Through an industry-based consulting project, students apply their knowledge to solve real-world challenges in teams. The course culminates in the submission of a Master Thesis, showcasing their expertise and practical impact.

This program requires submission of Master Thesis.