Students in the biomedical science programs in SGS have the opportunity to take computational classes regardless of their specific discipline. This aligns with the goal of the NIH that each graduate program provides training opportunities in addition to their technical courses that equip trainees with quantitative/computational approaches.
Computational Biology Certificate (CBC) for Rutgers PhD Students
Program code 032 (12 credit certificate completed over 1-2 years)
Computational biology uses computer simulations, mathematical modeling and data analysis to understand biological systems and interactions. While computational biology is the used by many researchers in academia and industry, Ph.D. students are never systematically taught these skills and how to apply them to their research. The goal of this 12-credit Certificate is to train biomedical Ph.D. students in Computational Biology. Upon its successful completion of this degree, the Certificate will be added onto the transcript in addition to the Ph.D. degree.
Eligibility: PhD candidates in biomedical sciences affiliated with the following programs who are in their second year or higher of graduate school are eligible to apply for the certificates: Biochemistry, Biomedical Engineering, Cell and Developmental Biology, Chemical and Biochemical Engineering, Chemistry and Chemical Biology, Electrical Engineering, Endocrinology and Animal Biosciences, Exposure Science, Medicinal Chemistry, Microbial Biology, Microbiology and Molecular genetics, Neuroscience, Nursing, Nutritional Sciences, Pharmacology, Pharmaceutical Sciences, Physiology and Integrative Biology, Psychology, Public Health, Quantitative Biomedicine and Toxicology.
Approval must be obtained by the PI and the Program Director. The 12 credits from the certificate courses cannot be used towards the minimum research credit requirements or the course credit requirements for a student’s PhD program but can be part of the 72 credits they need to reach their total number of credits required for a PhD degree. Pre-requisites for the certificate include statistics and linear algebra. Applications will be open every summer and announced by email.
View the Computational Biology curriculum and the application form
DataCarpentry or Software Carpentry Workshops
Every year in early January, we offer a two-day boot camp. Data Carpentry and Software Carpentry workshops are for any researcher who has data they want to analyze, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills, and tools for working more effectively with data. The focus of this workshop will be working with data and data management & analysis. They typically cover cover metadata organization in spreadsheets, data organization, connecting to and using cloud computing, the command line for sequence quality control, and bioinformatics workflows.
View more information about the DataCarpentry Genomics Workshop
Computational Genomics course
16:761:505 (Three credits) Spring Semester
The purpose of this course is the application of computational methods to large-scale data (e.g., genome wide association data, RNAseq data) for genetic traits to localize trait genes. Also, we study additional big data to evaluate whether the localized gene or genes is (are) causal for the trait. Finally, we develop computational methods that incorporate deviations from homogeneity (misclassification errors, genetic heterogeneity, deviations from independence).
View the syllabus of the Computational Genomics Course
Bioinformatics course
16:765:585 (Three credits) Spring Semester
This course is designed to introduce experimental biologists to bioinformatics concepts, principles, and techniques within the framework of basic shell scripting and web-based databases/tools. Prior to starting class, students are expected to know how to work in a command-line environment and have a basic understanding of programming/scripting. The course includes a brief introduction to working with UNIX/LINUX systems, writing Python scripts, and automating/using existing applications for the analysis of large datasets. All work will be done in a live development environment.
View the syllabus of the BioInformatics Course
Python Methodologies
16:137:552 (3 credits) Fall, Spring, and Summer
This course acts as an introduction to computer programming with the Python programming language. The basics of imperative programming will be covered as well as selected areas of computer science, object-oriented programming, and data structures. Computer programming is about problem-solving so we will begin to think about how to solve problems in discrete steps as computers do. After the beginning of the course, when we have our sea legs, we will begin to introduce ideas from Data Science and use what we have learned about computer programming and problem-solving in this area.
View the Python Methodologies syllabus
Python for Research Bootcamp mini-course
16:695:621 (1 credit) Fall
This course is specifically designed for students who have no programming background, or learned to program but not in Python. This course will teach basic Python programming using the Jupyter notebook platform. Skills learned in this course will include using the Jupyter notebook platform, and use of variables, multiple data types, functions, conditional statements, math and Boolean operations, programming with loops, and input/output. Students will use their own laptops, but no software needs to be installed.