Educational Programs and Short Courses
- Data Matters: Data Matters™ is a week-long series of one and two-day courses aimed at students and professionals in business, research, and government. The short course series is sponsored by the Odum Institute for Research in Social Science at UNC-Chapel Hill, the National Consortium for Data Science, and RENCI. Data Matters gives students the chance to learn about a wide range of topics in data science, analytics, visualization, curation, and more from expert instructors. Registration is now open; reserve your spot now!
- Davis Library Research Hub Events & Short Courses: Davis Library is one of three Research Hub locations across the University Libraries. It offers free workshops on R and Python in partnership with the Odum Institute for Social Sciences.
- Certificate in Applied Data Science: The Certificate in Applied Data Science (CADS) is designed to equip students with the knowledge and skills to succeed in the modern workforce. Drawing on the data science expertise of the UNC Information and Library Science (SILS), the program offers focused training with a strong emphasis on practical workplace applications, including a practicum in which students will complete a project in a real-world setting.
- CHIP (Carolina Health Informatics Program): The Carolina Health Informatics Program (CHIP) is an interdisciplinary research and training program that plays a key role in fulfilling UNC-Chapel Hill’s commitment to improving human health through health informatics research, data sharing, development, and education.
- Minor in Data Science: The Data Science Minor at Carolina is a new multidisciplinary program that has been designed to introduce students from any discipline to data science methods and applications, while simultaneously providing opportunities to explore its complex interactions with modern society. To achieve these goals, the minor is structured to allow students to choose their coursework from many different departments, encouraging them to explore the use of data science within their main field of study.
- Undergraduate Quantitative Track in Biology: The quantitative track Bachelor of Science program is designed for students with a strong interest in a multidisciplinary approach to biological problems in preparation for graduate study in biological or health sciences.
- Masters in Data Science & Analytics: The MS program in Data Science and Analytics offers students a rigorous program of training in the areas of statistics, optimization, stochastic modeling, and probability. The program is designed to be flexible enough to accommodate students with different technical backgrounds and subject matter interests, and it allows students to pursue a variety of coursework in theory, methodology, computation, and applications.
- PhD in Bioinformatics & Computational Biology: Modern biology is being greatly enriched by an infusion of ideas from computational and mathematical fields, including computer science, information science, mathematics, operations research and statistics. In turn, biological problems are motivating innovations in these computational sciences. There is a high demand for scientists who can bridge these disciplines.
- PhD in Biostatistics: The program provides advanced, research-oriented training in theory and methodology of biostatistics to prepare individuals for careers in academia, government and industry.
- BIOS 735 – Introduction to Statistical Computing: This course teaches important concepts and skills for statistical software development using case studies. After this course, students will have an understanding of the process of statistical software development, knowledge of existing resources for software development, and the ability to produce reliable and efficient statistical software.
- INLS 625 – Information Analytics: The data explosion experienced by computerization of every aspect of our lives from social media to internet of things requires a deeper look at information analytics. The course introduces proven and emerging analytical techniques that can be used to deal with mountains of mostly unstructured data. We will look at several analytical paradigms from Predictive Modeling to Data Mining, Text Analytics to Web Analytics, Statistical Analysis to novel paradigms in Map Reduce and Storm. Knowledge of programming is essential.
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