Please choose one pathway for admission. Both pathways are ultimately required to meet degree requirements:

Master core fundamentals across the data science landscape through this comprehensive foundation course that serves as a gateway to specialized paths. This specialization delivers practical exposure to real-world data science applications, ethical considerations, and data security principles, allowing students to apply structured analytical processes to actual datasets while gaining enough insight into each subfield to make informed decisions about future specializations – all while learning essential data protection practices through a cybersecurity lens.

The curriculum offers a robust foundation for students seeking expertise in data science, with a particular focus on data mining, machine learning, statistical modeling, and database management. The Data Mining Foundations and Practice course provides students with essential knowledge on the principles and techniques of mining data to uncover hidden patterns and insights, a critical skill in data-driven decision-making. The Machine Learning: Theory and Hands-on Practice with Python Specialization delves into both the theoretical aspects and practical applications of machine learning using Python, offering students hands-on experience with algorithms, data preprocessing, and model evaluation. Complementing this, the Statistical Modeling for Data Science Applications Specialization equips students with the tools and methodologies needed to apply statistical models to real-world data science problems, enhancing their ability to analyze and interpret complex data. Lastly, the Databases for Data Scientists Specialization focuses on database management systems and provides students with the necessary skills to design, implement, and query databases, an essential component of working with large data sets. Together, these courses provide a comprehensive and practical skill set that prepares students to excel in the rapidly growing field of data science.

The Data Science Electives offer a comprehensive range of specialized courses designed to enhance students' technical expertise in areas essential for modern data science applications. These courses cover a variety of topics, starting with the fundamentals of high-performance and parallel computing, crucial for managing large-scale data processing tasks. Additionally, courses like Managing, Describing, and Analyzing Data, as well as Stability and Capability in Quality Improvement, equip students with the skills to analyze and ensure the quality of datasets. Measurement Systems Analysis further strengthens these abilities by focusing on techniques to assess and improve data collection processes. For students interested in more advanced topics, Deep Learning Applications for Computer Vision explores the use of deep learning techniques to solve complex problems in image and video analysis. Students can also expand their skill set in communication with the Effective Communication: Writing, Design, and Presentation Specialization, which emphasizes clear and engaging ways to present data insights. The electives also include specialized courses in Regression and Classification, as well as various software architecture-focused courses tailored to big data applications, providing a robust foundation for managing and analyzing large datasets. In addition, courses like Supervised and Unsupervised Text Classification for Marketing Analytics, along with Network Analysis for Marketing Analytics, delve into specialized methods for text and network data analysis in the marketing domain. For students seeking electives outside the core data science curriculum, a maximum of 6 credits can be earned in areas such as Financial Forecasting and Reporting, Agile Project Management, and Verification and Synthesis of Autonomous Systems. These additional courses further enrich the learning experience, allowing students to apply data science principles in diverse business and technical contexts, from product cost analysis to advanced algorithms and project management strategies. This combination of technical, analytical, and business-focused electives prepares students for a wide array of career opportunities in data science and related fields.