Data science is the creation and application of powerful new methods to collect, curate, analyze, and make discoveries from large-scale data. Data are simply pieces of information, values or variables that can be used to describe a person, object, or other thing. In the digital era, human beings create data round the clock, through mediums such as smart phones, the internet, and social media. We generate over 2.5 quintillion bytes of data every day, and that number is constantly increasing. In fact, over 90% of data has been collected over the past five years. When learning about data science, one often hears the term 'big data.' Big data refers to data sets (collections of data) with massive amounts of complex information that are difficult to manipulate and understand using traditional data processing methods. Data science focuses on the concepts, methods, and applications for extracting meaning from big data and has become an emerging discipline in the 21st century.
At the University of Rochester, researchers employ data science to further everything from health analytics and cognitive science to business and artificial intelligence, and are using data science to develop new methods, tools, and infrastructures to better understand the world around them.
In 2023, a new track in Genomics was approved by the New York State Department of Education for the masters of science (MS) in data science program.
The curriculum will be available to any student in the data science MS program and will be required for Genomic Intensive Data Science Research, Education and Mentorship (GIDS-REM) fellows. The genomics data science MS curriculum follows a specific timeline designed to maximize the goals for GIDS-REM fellows. Students will take core data science (DSCC) MS courses and genomics courses in fall and spring during a 19-month period. Supplemental workshops and seminars complement the program. Internships are guaranteed for GIDS-REM fellows and optional for other students.
The curriculum, which totals 33 credits, covers theoretical and applied aspects of data science and genomics. This educational program includes core components to develop competencies in fundamental data science concepts, computational biology and genomics as well as a sequence of workshops and seminars that aim to teach the most important applied bioinformatics workflow used by faculty in our institution. The combination of fundamental and applied training that fellows receive in their first two semesters aims to ensure fellows are ready to be productive for an internship/research assistantship in the summer of year one.
