The courses CS 725: Introduction to Relational Databases, MATH 764: Linear Regression, and the pathway course options provide foundational knowledge in data science and analytics, with a focus on databases, statistical modeling, and data-driven decision-making. CS 725 introduces students to relational databases, covering the fundamental concepts of database management, including data modeling, querying with SQL, and the structure of relational databases. This course equips students with the technical skills necessary for handling structured data and understanding how relational databases are designed and operated. MATH 764 focuses on linear regression, a core technique in statistics and data analysis. Students learn how to build and interpret linear regression models, which are widely used to understand relationships between variables and make predictions based on data. This course serves as a crucial building block for more advanced statistical modeling techniques. The pathway course options allow students to specialize further. MATH 765 offers a deep dive into model diagnostics, where students learn how to assess the performance and validity of statistical models and apply corrective measures when needed. Alternatively, CS 726 focuses on relational database design, where students gain practical skills in designing and structuring databases to optimize data retrieval and storage. Together, these courses provide a comprehensive foundation for students interested in data management, statistical analysis, and building robust data models for real-world applications.

This set of courses provides a comprehensive exploration of relational database systems, statistical modeling, and data analysis techniques. The curriculum emphasizes the practical aspects of database management, from design to implementation, ensuring that students acquire the skills needed to handle complex data structures. The relational database design course focuses on the principles of structuring databases efficiently, covering topics such as data normalization, integrity constraints, and query optimization. This foundation is extended in the implementation and application course, which equips students with hands-on experience in creating, managing, and optimizing relational databases for real-world applications. These courses collectively prepare students to design, implement, and manage relational databases, which are essential in modern data-driven environments. In addition, the curriculum includes a course on statistical modeling, which delves into variable selection, model validation, and nonlinear regression techniques. This course provides students with the knowledge to build and validate predictive models, addressing complex relationships between variables and ensuring the robustness of the models through appropriate validation techniques. The integration of database management and advanced statistical methods provides a holistic educational experience, ensuring that students are well-prepared to solve real-world data challenges and make data-driven decisions across various industries.

This collection of courses offers a comprehensive curriculum that blends advanced statistical techniques, data science, and big data technologies, providing students with a well-rounded skill set in data analysis, machine learning, and public engagement. The course on statistical learning introduces students to the foundations of machine learning, focusing on how algorithms can be applied to identify patterns in data and make predictions. Big data technologies explores the tools and frameworks used to manage and analyze vast amounts of data, equipping students with the expertise to work with complex datasets in a variety of industries. Public engagement for scientists bridges the gap between scientific research and the public, teaching students how to communicate technical concepts effectively to diverse audiences. The data preparation and analysis course emphasizes the critical skills needed for cleaning, transforming, and preparing data for analysis, ensuring that students can work with real-world data that may be messy or incomplete. For those interested in time series data, the introduction to time series course covers methods for analyzing temporal data, with applications in finance, economics, and environmental sciences. Deep learning introduces cutting-edge techniques in artificial intelligence, focusing on neural networks and their applications to problems like image and speech recognition. Finally, the Bayesian computational statistics course delves into Bayesian methods, a powerful framework for statistical inference that is widely used in machine learning and data science. Together, these courses provide students with a robust understanding of both the theory and practical skills needed to excel in data science and related fields.

Capstone Experience (6 credits)