Courses

Degree Credit Requirement

  • 30 course credits total either thesis or non-thesis options.
  • 9 credits per semester to be maintained by International Applicants.
  • Students may enter the program in either the Spring or Fall Semesters.
Section A Core Courses
  • BIOC 6221 – Proteins, Pathways and Human Health (4 credits, fall)
    This course focuses on the relationship between protein structure and functions, and connections between metabolic pathways and human diseases.
     
  • BIOC 6223 – Bioinformatics (2 credits, fall)
    This course explores major biomedical knowledgebases on genes, proteins, glycans, and other biomolecules, their interconnections and data flow, the foundations of bioinformatics analysis, and methods for critically evaluating and extracting knowledge from these resources.
     
  • BIOC 6227 – Biochemistry Seminar (1 credit, spring)
    Current literature in biochemistry. Limited to graduate students in the department.
     
  • BIOC 6228 – Research essentials  (1 credit)
    A variety of topics essential to helping graduate students become successful scientists;including scientific research, ethics guidelines, writing manuscripts, and career information.
     
  • BIOC 6230 – New Technologies in Scientific Research (2 credits, spring)
    This course will focus on new scientific technologies that are an essential part of modern investigative research.
     
  • BIOC 6243 – Applied Bioinformatics (2 credits, spring)
    This course addresses modern biomedical big data from its generation to its analysis and interpretation within the context of regulatory sciences and industry needs.
     
  • BIOC 6246 – Advanced Genomic Data Analysis: from Bulk to Single Cells (3 credits, spring)
    This course addresses theoretical and practical training in high-throughput genomic data analysis, focusing on single-cell genomics. Hands-on sessions and real-world projects provide essential skills in bioinformatics tools and workflows for analyzing complex datasets.
     

Upon completion of the entire Core Section A, students must pass a written examination on the material covered in these courses.

Section B Elective Courses

Electives need to be pre-approved by the Bioinformatics Program Director. Some courses may require prerequisites. The electives can be chosen to emphasize either a biological or computer science focus. Additional elective options can be found in the GW Bulletin.

  • BISC 6210 – Phylogenetic Systematics (4 credits)
    A rigorous and up-to-date treatment of the theory and methods of systematic, including phylogenetic inference and its applications in evolutional biology.
     
  • BIOC 6222 – Biochemical Genetics and Medicine (3 credits, spring)
    Topics covered will include gene expression and regulation, epigenetics, genetic alterations and genome stability.
     
  • BIOC 6237 – Proteomics and Biomarker Discovery (2 credits, spring)
    This course introduces common proteomics technologies, the types of data they generate, their significance for systems-level analysis, and their role in advancing biomarker discovery.
     
  • CSCI 3212 – Algorithms (4 credits)
    Concepts in design and analysis of algorithms, data structures, and problem-solving techniques: hashing, heaps, trees, graph algorithms, searching, sorting, dynamic programming, greedy algorithms, divide and conquer, backtracking, combinatorial optimization techniques and NP-completeness.
     
  • CSCI 3221 – Programming languages (3 credits)
    Programming language and software design fundamentals. Writing programs in a non-procedural programming language. Closures; procedures and data abstraction; object-oriented, procedural and declarative programming; continuation compilation and interpretation and syntactic extension.
     
  • DATS 6101 – Introduction to Data Science (3 credits)
    This course introduce students to several core data science concepts. Its teaches students how to program in Python and R, the advantages of Python over R and vice-versa. 
     
  • DATS 6103 – Introduction to Data Mining (3 credits)
    This course is a survey of concepts, principles, and techniques in data mining, including classification, association, and cluster analyses. Students learn to apply data mining methods to real-world problems with minimal rigorous mathematical understanding of the underpinnings of the methods. The course helps build a good foundation for taking advanced courses in the data science and for applying the basic techniques to practical problems. Data based examples and exercises using R, Python, and other tools are integrated into class activities.
     
  • DATS 6401 – Visualization of Complex Data (3 credits)
    This course introduces representation methods and visualization techniques for complex data, drawing on insights from cognitive science and graphic design. Its teaches students Google API Visualization Tools, Tableau, d3.js.
     
  • DNSC 6279 – Data Mining (3 credits)
    How organizations make better use of the increasing amounts of data they collect and how they convert data into information that is useful for managerial decision making. Examination of several data mining and data analysis methods and tools for exploring and analyzing data sets. State-of-the-art software tools for developing novel applications.
     
  • DNSC 6211 – Programming for Analytics (3 credits)
    Accessing, preparation, handling, and processing data that differ in variety, volume, and velocity. The ability to handle and process data is a core capability in the context of any analytics position in the industry. Development of a theoretical grounding in emerging paradigms like schema-less data. The programming environments that will be typically employed include Python and R.
     
  • PUBH 6277 – Public Health Genomics (3 credits)
    Molecular technology and its impact on public health practice and discourse in the post-genomic era; the use of genomics to solve or help alleviate public health challenges.
     
  • GENO 8232 – Introduction to computational biology as an interdisciplinary science in the 21st century, incl. the algorithmic and statistical principles of bioinformatics, as well as practical trainings in processing, modeling and analyzing multi-omics datasets. Restricted to Graduate students in biomedical sciences or related fields. Prerequisites: BIOC 6223, GENO 6223 or BIOC 6240.
Section C Research Courses

Thesis Option:

  • BIOC 6998-6999 – Thesis (6 credits, fall/spring)
    To be arranged by student and designated faculty member.
  • Electives (7 credits)

Non-Thesis Option:

  • Participation in a project under investigation in the department or one in a related field suggested by the student and approved by the program director. Content differs each time course is offered; may be repeated for credit. Laboratory fee.