Bioinformatics and Computational Biology Other Available Graduate Programs PhD in Statistics(traditional) Master of Arts in Statistics MS in Medical Statistics Program for the Degree of PhD in Statistics with Concentration in Bioinformatics and Computational Biology The new BCB concentration is designed to educate the next generation of biostatisticians with the knowledge required to address critical scientific and public health questions, and in particular, equip them with the skills necessary to both develop and use quantitative and computational methodologies and tools to manage, analyze and integrate massive amounts of complex biomedical data. Students will learn core statistical methods and obtain training in data analysis methodologies and computational skills and techniques necessary for handling “Big Data” in the biomedical and public health sciences. In addition to this training in core methods, the program also places great emphasis on cross-training: 1) training students with quantitative/computational science backgrounds to enhance their understanding of biological questions and biological interpretation; and 2) training students with biomedical science backgrounds to proficiently use bioinformatics and computational methods and tools to address scientific questions. The doctoral program in Statistics with BCB Concentration is administered by the School of Medicine and Dentistry, and hence School of Medicine and Dentistry regulations apply. Entering PhD students need undergraduate preparation in mathematics, computer science, and/or biology, including mathematical analysis (advanced calculus) and linear algebra, a year of probability and statistics, basic computer science and biology. Normally, doctoral students are initially considered MA candidates; this non-thesis degree can be completed in three or four semesters or, in some cases, in one calendar year. PhD studies consist of additional specialized courses, seminars, lab rotations and supervised research leading to a dissertation. There is no foreign language requirement. Computer expertise is developed in the program. All MA/PhD students take a comprehensive examination at the beginning of the second year. PhD students take another written examination at the beginning of the third year. Both examinations will cover material in the areas of probability, inference, data analysis, and bioinformatics and computational biology. After beginning research on a dissertation topic, PhD students take an oral qualifying examination, consisting largely of a presentation of a thesis proposal to a faculty committee, the student's Thesis Committee. Upon completion of the dissertation, doctoral candidates present their work at a public lecture followed by an oral defense of the dissertation before the Thesis Committee. Prior to completing degrees, most students have some publications underway, including some work related to their dissertation research, possibly other methodological work done in collaboration with other members of the faculty, and often some applied papers with scientific researchers in other fields. In general, the PhD program requires a minimum of four years of study, with five years of study being more common. Course work is concentrated in several areas—inference, data analysis, bioinformatics and computational biology. Beginning students should expect to spend all of their first year, most of their second year and some of their third year taking formal courses. At the same time, students in the BCB concentration are expected to take both web lab and dry (computational) lab rotations for the first 2 years until selecting one or two thesis advisors (ideally one computational/ statistics advisor and one wet-lab advisor). The balance of time is spent on reading and research. Students entering with advanced training in statistics, bioinformatics and computational biology may transfer credits at the discretion of their advisor. A typical program for an entering student without previous training is as follows: Year 1: Fall BST 401 Probability Theory BST 411 Statistical Inference BST 464 Applied Linear Regression BST 496 Wet/Dry Lab Rotation IND 501 Ethics in Research (1 credit) Year 1: Spring BST 413 Bayesian Inference BST 426 Linear Models BST 432 Introduction to Bioinformatics BST 496 Wet/Dry Lab Rotation Year 1: Summer BST 477 Introduction to Statistical Software I (0 credits) BST 478 Introduction to Statistical Software II (0 credits) Year 2: Fall BST 433 Introduction to Computational Systems Biology BST 479 Generalized Linear Models BST 496 Wet/Dry Lab Rotation (1 credit) ELECTIVE Year 2: Spring BST 431 Introduction to Computational Biology BST 513 Analysis of Longitudinal and Dependent Data BST 496 Wet/Dry Lab Rotation (1 credit) ELECTIVE Year 3: Fall BST 514 Survival Analysis BST 591 PhD Reading ELECTIVE Year 3: Spring BST 435 Bioinformatics Databases and Applications BST 591 PhD Reading ELECTIVE Year 4+ Mostly reading and research, with some 400-level and 500-level courses. Notes: Training in the use of statistical software (BST 477, BST 478) is offered during the first six weeks of the summer as a computing rotation (no formal credit).