Rice University offers a professional, non-thesis Master of Data Science (MDS) degree designed to equip interdisciplinary professionals with the skills to tackle real-world problems using data. The program emphasizes a blend of computational and statistical foundations, core data science methods, and specialized knowledge. Graduates will be prepared to collect, evaluate, interpret, and communicate data effectively for decision-making across various industries. The MDS program is taught by world-class faculty and provides both on-campus and online learning options, ensuring students receive the same high-quality education regardless of their chosen format. The curriculum is structured to provide a comprehensive understanding of data science, including core courses, specialization options, electives, and a capstone project. Students can choose to specialize in areas like business analytics, machine learning, or image processing (the latter currently only for on-campus students). Elective choices allow for further customization, with options in ethics, cybersecurity, and security/privacy. The program's learning outcomes focus on developing proficiency in the computational and statistical underpinnings of data science, applying core methods, solving complex problems with data, and communicating findings to diverse audiences. This interdisciplinary program is ideal for professionals seeking to enhance their data science expertise and apply it to their fields. The flexibility of online and on-campus delivery caters to different learning preferences and professional commitments. Graduates are prepared for in-demand roles in sectors ranging from science and healthcare to energy and manufacturing, where data-driven insights are increasingly critical.
The Master of Data Science (MDS) program requires a minimum of 31 credit hours, featuring a rigorous blend of courses designed to build essential skills for effective data-driven decision-making. The curriculum includes core foundational courses, opportunities for specialization, elective choices for further customization, and a culminating capstone project.
Graduates of the Master of Data Science program are prepared for a wide range of in-demand and high-paying roles across numerous industries. The skills acquired enable them to contribute significantly to fields that are increasingly reliant on data-driven insights.
Applicants must apply to either the online or on-campus program and will be explicitly admitted to one or the other. Supporting information for specific program requirements beyond a bachelor's degree is not detailed on the provided pages.
The provided fee pages relate to finance programs and do not contain specific tuition or living cost information for the Master of Data Science program. Prospective students should consult the official Rice University admissions or the specific MDS program website for detailed fee structures.
The Master of Data Science (MDS) program requires a minimum of 31 credit hours. The curriculum includes core foundational courses, specialization options, electives, and a capstone project.
Students can choose to specialize in Business Analytics or Machine Learning. The Image Processing specialization is currently only available for on-campus students.
Graduates are prepared for roles such as Data Scientist, Data Analyst, Machine Learning Engineer, Business Intelligence Analyst, Data Engineer, and Quantitative Analyst across various industries.
You must apply directly to either the Online MDS or the On-Campus MDS program, as admission is granted to one specific format. Prepare application materials such as transcripts, letters of recommendation, a statement of purpose, and a resume/CV.
Specific tuition and living cost information for the Master of Data Science program is not provided. Rice University charges tuition per credit hour, and the MDS program requires 31 credit hours.
The provided information mentions the Templeton Foundation Academic Cross Training Fellowship, which is intended for recent tenured philosophers and theologians to gain skills in empirical science and is not directly for Master's students in Data Science.