Program Overview

Bioinformatics (MS)

Overview

The Bioinformatics Master of Science program at NYU Tandon School of Engineering is designed to provide students with strong skills in molecular biology and big data analysis. The program focuses on developing solutions to critical challenges throughout medicine and the life sciences by utilizing genomic information and next-generation sequence analysis tools.

Program Description

Revolutionary changes are taking place in how we interpret health and treat disease. With extraordinary advances in both gene sequencing and machine learning, the bioinformatics field is expanding exponentially and creating a myriad of opportunities for professionals with in-depth knowledge of techniques for mastering complex data. In this program, students will build strong skills in molecular biology and big data analysis, develop solutions to critical challenges throughout medicine and the life sciences, and learn to utilize genomic information and next-generation sequence analysis tools.

Taxonomy Codes

• NYSED: 24764
• HEGIS: 1999.20
• CIP: 26.1103

Program Requirements

The program requires the completion of 30 credits, comprised of the following:

• Required Courses:
  • BI-GY 7453: Algorithms and Data Structures for Bioinformatics (3 credits)
  • BI-GY 7653: Next Generation Sequence Analysis for Bioinformatics (3 credits)
  • BI-GY 7663: Problem Solving For Bioinformatics (3 credits)
  • BI-GY 7673: Applied Biostatistics for Bioinformatics (3 credits)
  • BI-GY 7683: Biology and Biotechnology for Bioinformatics (3 credits)
  • BI-GY 7743: Machine Learning and Data Science for Bioinformatics (3 credits)
• Electives:
  • The electives are comprised of a combination of track electives and bioinformatics electives. Each student must complete the requirements of one of the tracks below. A total of 9 elective credits are required.
• Tracks:
  • Each student is required to complete one of the following tracks:
    • Laboratory Science Track:
      • BI-GY 7543: Proteomics for Bioinformatics
    • Translational Science Track:
      • BI-GY 7693: Population Genetics and Evolutionary Biology for Bioinformatics
      • BI-GY 7733: Translational Genomics and Computational Biology
• Bioinformatics Electives:
  • The remaining electives will be chosen from the list below. Students also have the option of taking additional track electives, which will count as bioinformatics electives.
    • BI-GY 7573: Special Topics in “Informatics in Chemical and Biological Sciences”
    • BI-GY 7633: Transcriptomics
    • BI-GY 7753: Bioinformatics Guided Studies
• Bioinformatics Electives from Other NYU Schools:
  • BMIN-GA 3007: Deep Learning for Biomedical Data
  • BMIN-GA 4498: Advanced Integrative Omics
  • INTER-MD: Introduction to Health Informatics
• Capstone:
  • BI-GY 810X: Bioinformatics Capstone 1 (3 credits)

Sample Plan of Study

The sample plan of study is as follows:

• 1st Semester/Term:
  • BI-GY 7453: Algorithms and Data Structures for Bioinformatics (3 credits)
  • BI-GY 7663: Problem Solving For Bioinformatics (3 credits)
• 2nd Semester/Term:
  • BI-GY 7653: Next Generation Sequence Analysis for Bioinformatics (3 credits)
  • BI-GY 7673: Applied Biostatistics for Bioinformatics (3 credits)
• 3rd Semester/Term:
  • BI-GY 7683: Biology and Biotechnology for Bioinformatics (3 credits)
  • BI-GY XXXX: Track Elective (3 credits)
• 4th Semester/Term:
  • BI-GY 7743: Machine Learning and Data Science for Bioinformatics (3 credits)
  • BI-GY XXXX: Track Elective OR Bioinformatics Elective (3 credits)
• 5th Semester/Term:
  • BI-GY 810X: Bioinformatics Capstone (3 credits)
  • BI-GY XXXX: Bioinformatics Elective (3 credits)

Learning Outcomes

Upon successful completion of the program, graduates will:

1. Perform Big Data of Omics datasets. This includes scalable applications.
2. Demonstrate command of R & Bioconductor, Python & Biopython, as well as UNIX to process, analyze, and maintain quality control of biological data.
3. Demonstrate command of the fundamentals of Bioinformatics & Computational Biology with Biologist, Computer Scientist, as well as Statisticians through written or verbal communication.
4. Demonstrate understanding of fundamental concepts of mathematics and computer science as they relate to Bioinformatics & Computational Biology.
5. Solid knowledge of Bioinformatics & Computational Biology resources and tools for research and analysis.
6. Application of Next Generation Sequence Analysis, Machine Learning and Translational Genomics in healthcare.

Policies

• NYU Policies: University-wide policies can be found on the New York University Policy pages.
• Tandon Policies: Additional academic policies can be found on the Tandon academic policy page.