Provides knowledge of fundamental statistical models and numerical optimizations of machine learning, including deep learning, with applications in computer vision, natural language processing, and intelligent user interaction.

Covers basics of database systems and data mining methods for extracting insight from structured datasets and unstructured data.

Focuses on fundamentals of interactive computing that promote synergy between computer and user, including data presentation and manipulation for public understanding.

This course focuses on maximizing computational performance through parallel programming techniques, teaching students to harness the power of multi-core CPU and many-core GPU architectures. Students will develop expertise in specialized programming languages, compilers, and libraries optimized for parallel processing across different platforms and applications.

This course covers essential software engineering principles, encompassing both functional and object-oriented approaches to analysis and design. Students will master the complete software development lifecycle, from initial specifications through implementation, testing, and maintenance, with particular emphasis on managing large-scale enterprise applications and codebases.

This course explores the core principles of numerical analysis and their practical applications in scientific and engineering simulations, demonstrating how these mathematical techniques power diverse real-world applications, from generating immersive video game environments to enabling advanced medical simulations.

This course explores the intersection of privacy and security in social networks, teaching students to detect and mitigate privacy vulnerabilities while implementing advanced protective measures through the application of machine learning algorithms and cryptographic principles.

The course teaches students to develop distributed computing systems, including cloud platforms and Internet of Things networks, with hands-on experience building applications that leverage major cloud services like Amazon Web Services and Microsoft Azure to create scalable, distributed solutions.

This course focuses on mastering cloud computing technologies and infrastructure necessary for processing and analyzing big data, teaching students to develop applications that effectively leverage cloud platforms to support data mining and knowledge extraction from large-scale datasets.

This course teaches students to identify meaningful patterns within structured datasets while also developing techniques for extracting valuable information from unstructured sources, specifically focusing on processing and analyzing natural language text documents.

Master the art of transforming raw data into compelling visual narratives through hands-on experience with industry-standard tools. This comprehensive coursework equips you with skills in crafting clear, impactful data presentations using Tableau for intuitive database visualization, while also diving into D3.js to create interactive web-based stories that bring your data to life through dynamic, narrative-driven visualizations.

Gain practical expertise in applying machine learning techniques to real-world challenges through a hands-on, tool-based curriculum. This application-focused coursework explores how to leverage ML algorithms across diverse domains, from processing visual data and natural language to analyzing geographical information and audio signals, providing you with concrete experience in implementing solutions for computer vision, NLP, GPS applications, and sound processing systems.

The curriculum for the MCS and MCS-DS degree tracks offers an advanced and comprehensive exploration of key concepts and techniques in data science and computing. Students in both tracks will engage in a deep dive into the theory and practice of data cleaning, learning essential skills for preparing and refining data for analysis. Scientific visualization is another core area, where students will gain expertise in the creation and interpretation of graphical representations of complex data sets, making it easier to derive meaningful insights. The curriculum also includes foundational courses in data curation, which focus on the principles and best practices of managing, storing, and organizing data for long-term usability. Additionally, students will study practical statistical learning, an important field for applying statistical methods to solve real-world problems, and advanced Bayesian modeling, which covers probabilistic modeling techniques critical for uncertain data analysis. The program also offers specialized courses, such as deep learning for healthcare, which focuses on applying AI to healthcare data, and cloud computing, which prepares students for working with large-scale, distributed systems. Finally, the program culminates in capstone courses in data mining and cloud computing, allowing students to apply their learned skills in practical, hands-on projects that demonstrate their ability to tackle complex challenges in the fields of data science and computing.