M.S. in Bioinformatics
Academic Advisor: https://computing.njit.edu/advising
Degree Requirements
A minimum of 30 credits is required for the degree, excluding bridge courses. The graduate curriculum consists of five core courses and additional elective courses, with an optional thesis (six credits) or research project (three credits).
Students with non-computing STEM background may be accepted and required to take the following bridge courses (CS 506 may count toward the credits required for the MS degree):
| Code | Title | Credits |
|---|---|---|
| Bridge Courses | ||
| CS 280 | Programming Language Concepts | 3 |
| CS 332 | Principles of Operating Systems | 3 |
| CS 505 | Programming, Data Structures, and Algorithms | 3 |
| CS 506 | Foundations of Computer Science | 3 |
| Total Credits | 12 | |
Curriculum
| Code | Title | Credits |
|---|---|---|
| Core Courses | 6 | |
| CS 636 | Data Analytics with R Program | 3 |
| MATH 663 | Introduction to Biostatistics | 3 |
| Select at least three from the following | at least 9 credits | |
Core Electives | ||
| Introduction to Big Data | ||
| Machine Learning | ||
| Approaches to Quantitative Analysis in the Life Sciences | ||
| Stat Methods in Data Science | ||
| Advanced Statistical Learning | ||
| Prin of Bioscience Processing | ||
| Critical Thinking for the Life Sciences | ||
| Cell Biology: Methods & Appl | ||
| Molecular Bio Of Eukaryotes | ||
| Cell Molec Dev | ||
| Select remaining courses from the following: | ||
NJIT Electives | ||
| Neural Engineering | ||
| Biomechanics of Human Structure and Motion | ||
| Advanced Physical Chemistry | ||
| Biochemistry | ||
| Data Management System Design | ||
| Advanced Database System Design | ||
| Image Processing and Analysis | ||
| Data Mining | ||
| Artificial Intelligence | ||
| Deep Learning | ||
| Computer Vision | ||
| Applications of Database Systems | ||
| Advanced Machine Learning | ||
| Pattern Recognition and Applications | ||
| Information Retrieval | ||
| Digital Signal and Data Processing | ||
| Random Signal Analysis | ||
| Analytical Computational Neuroscience | ||
| Systems Computational Neuroscience | ||
| Foundations of Mathematical Biology | ||
| Regression Analysis Methods | ||
| Clinical Trials Design and Analysis | ||
| Survival Analysis | ||
| Probability Distributions | ||
| Statistical Inference | ||
| Graduate Capstone Project (Counting towards the elective credits requires the program director’s prior approval. In addition, it needs to be completed with an external partner (industry, lab, or government), or with a faculty only if the same faculty is not the student’s MS project or MS thesis advisor.) | ||
Rutgers-Newark Electives | ||
| Cell Biology: Methods & Appl | ||
| Molecular Bio Of Eukaryotes | ||
| Microbial Ecology | ||
| Topics in Cell Biology | ||
| Biology Of Cancer | ||
| Pharmacology | ||
| Total Credits | 12 | |
