Computational Biology and Biostatistics, Master of Science

Program Overview

St. John's University's Master of Science in Computational Biology and Biostatistics equips students with expertise in both computer science and biology, enabling them to analyze and interpret complex biological data. The program emphasizes ethical considerations and prepares graduates for careers in data analysis, bioinformatics, and healthcare, addressing global challenges and underprivileged populations.

Program Outline

Degree Overview:

The Master of Science degree in Computational Biology and Biostatistics at St John's University aims to address the increasing demand for data analysts, data curators, database developers, statisticians, mathematical modelers, bioinformaticians, and software developers with training in both computer science and biology. The program educates students in utilizing computational methods and algorithms to represent and simulate biological systems and interpret extensive experimental data. Graduates are equipped to tackle urgent global challenges like food shortage, climate change, and emerging diseases. In line with St. John's mission, the program emphasizes addressing issues disproportionately affecting the world's underprivileged populations. In addition to gaining a comprehensive understanding of biological structures and processes, students develop a critical consciousness and ethical perspective, preparing them for roles in leadership and service on local, national, and international levels.

Outline:

Required Courses:

(18 credits)

• BIO 207 Biochemistry (3 credits)
• BIO 208 Molecular Biology (3 credits)
• BIO 209 Bioinformatics (3 credits)
• BIO 212 Cell Biology (3 credits)
• MTH 161 Introduction to Probability (3 credits)
• MTH 163 Statistical Modeling (3 credits)
• MTH 165 Introduction to Computing with Applications (3 credits)
• MTH 209 Linear Algebra I (3 credits)
• MTH 240 Computational Biology (3 credits)

Elective Courses:

(9 credits) Students must select three elective courses from the following list:

• BIO 210 Practical Genomics/Transcriptomics (3 credits)
• BIO 236 Microbial/Molecular Genetics (3 credits)
• BIO 248 Laboratory Techniques and Applications I (3 credits)
• BIO 250 Topics in Immunology (3 credits)
• BIO 299 Scientific Literacy and Integrity (3 credits)
• CUS 610 Data Mining and Predictive Modeling I (3 credits)
• CUS 615 Data Mining and Predictive Modeling II (3 credits)
• HCI 520 Medical and Health Informatics (3 credits)
• HCI 525 Applied Healthcare Analytics (3 credits)
• MPH/PAS 252 Biostatistics (3 credits)
• MTH 167 Mathematical Modeling I (3 credits)
• MTH 172 Operations Research I (3 credits)
• MTH 180 Computer Algorithms (3 credits)
• MTH 222 Machine Learning (3 credits)
• MTH 242 Artificial Intelligence (3 credits)

Comprehensive Examination:

(9 credits)

• CBB 105 Comprehensive Examination (0 credits)
• Students must pass a comprehensive examination to complete the program.

Assessment:

While the context does not provide specifics on assessment methods and criteria, it can be inferred that students will be assessed through a combination of assignments, examinations, presentations, and potentially research projects. However, it is likely that the program utilizes a diverse range of teaching methods, including lectures, discussions, group projects, and laboratory work. The faculty in the program are expected to possess expertise in their respective fields and have a commitment to student learning and engagement.

Careers:

The program prepares graduates for various career paths in the fields of biology, computer science, and healthcare. Graduates may find employment as:

• Data analysts
• Data curators
• Database developers
• Statisticians
• Mathematical modelers
• Bioinformatics
• Software developers
• Scientists
• Researchers
• Healthcare professionals
• Science educators
Furthermore, graduates will be well-positioned to pursue doctoral studies in related fields.