Courses

Degree Credit Requirement

  • 30 course credits total either thesis or non-thesis options.
    • Thesis option: 14 credits of required courses, 6 credits of thesis courses, and 10 credits of elective courses.
    • Non-thesis option: 14 credits of required courses, and 16 credits of elective courses.
       
  • Please note that international students must maintain a full-time status with a minimum of 9 credits per semester. All graduate students are required to maintain a minimum cumulative grade-point average (GPA) of 3.0 in all coursework as per CCAS Policy. 
     
  • Normally, only 6000-level courses (or higher) are counted toward the requirements for the graduate degree. For a lower level course to be counted towards graduation, the instructor must approve and add significant coursework/assignments to bring the course to 6000 graduate level for that student.
     

The program includes Core, Elective, and Investigative components, three sections, with the following courses
 

Section A Core (Required) Courses

BIOC 6221 - Proteins, Pathways and Human Health
  • Course Credits: 4 
  • Course Offering: Fall
  • Course Director(s): Manjari Dimri
  • Course Prerequisites: CHEM 2151 and CHEM 2152
     
  • Course Description: This course focuses on the relationship between protein structure and function, and connections between metabolic pathways and human diseases. By the end of this course, you will be able to understand fundamental concepts, principles, and metabolic pathways in the context of biochemistry research, applications of biochemistry knowledge to healthy and disease states, and how knowledge of biochemistry is used for the development of strategies and agents to maintain human health and to treat and prevent diseases.
BIOC 6223 - Bioinformatics
  • Course Credits: 2 
  • Course Offering: Fall
  • Course Director(s): Raja Mazumder, Jonathon Keeney 
  • Course Prerequisites: None
     
  • Course Description: This course will introduce students to explore 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
  • Course Credits: 1 
  • Course Offering: Spring
  • Course Director(s): Jiyoung Lee                                                                    
  • Course Prerequisites: None, Graduate Students only
     
  • Course Description: The course aims to help students develop an overall understanding of the principles of verbal communication in science including accepted presentation techniques, listening skills, critical analysis of scientific presentations, and participation in scientific discussions. Students also participate in other departmental seminars for further exposure to topics presented by external speakers. Students also get an opportunity to prepare a scientific paper/review of their choice and present it to their colleagues. Students are provided formal feedback on their presentation by department faculty and peers.
BIOC 6228 - Research Essentials and Bioscience Careers
  • Course Credits: 1
  • Course Offering: Fall, Spring, Summer
  • Course Director(s): Manjari Dimri                                                                    
  • Course Prerequisites: None
     
  • Course Description: The course provides information and skills required in bioscience-related careers including basic research essentials such as writing, and ethical scientific conduct. Students will gain knowledge in the use of library databases and other available resources for scientific research. Students will gain career-related information through a series of interactive discussion-based sessions by experts with diverse careers in academic research, the pharmaceutical industry, biotechnology, research administration, scientific writing, and technology transfer. They will further acquire career advice and planning with resumes and cover letters, network and communicate with experts at the Career Center, and develop individual career development plans tailored to their career goals. 
BIOC 6230 - New Technologies in Scientific Research
  • Course Credits: 2 
  • Course Offering: Fall
  • Course Director(s): Brett Shook                                                                    
  • Course Prerequisites: None
     
  • Course Description: The main purpose of this course is to introduce students to new technologies for conducting meaningful scientific inquiry and research. Over the past decade, new technologies have evolved and are now considered essential to investigative research. This course will deepen the student’s understanding of the basic concepts of these newer technologies and provide the knowledge necessary to apply these modern methods to solve research problems. Some of these include Mass Spectrometry, CRISPR, ChIP-Seq, RNA sequencing, Nanotechnology, Imaging (confocal, live, and intravital), Metabolomics, Flow cytometry, CyTOF, Electron microscopy, and 3D-organoids. 
BIOC 6243 - Applied Bioinformatics
  • Course Credits: 2 
  • Course Offering: Spring
  • Course Director(s): Robel Kahsay, Jonathan Keeney
  • Course Prerequisites: None
     
  • Course Description: This course addresses modern biomedical big data from its generation to its analysis and interpretation within the context of regulatory sciences and industry needs. In the first stages, this course discusses the modern technologies generating data, transfer and archival protocols, security and privacy aspects, statistical and computational validity, and algorithmic deficiencies. In the second part, it introduces data types and computational algorithms for massively parallel optimized execution in an enterprise computing environment. The third part is dedicated to High-performance Integrated Virtual Environment (HIVE) computing and designing novel algorithms and apps for existing HIVE infrastructures.
BIOC 6246 - Advanced Genomic Data Analysis: From Bulk NGS to Single-Cell
  • Course Credits: 3 
  • Course Offering: Spring 
  • Course Director(s): Anelia Horvath
  • Course Prerequisites: Basic knowledge of R, R-studio, Linux, command line
     
  • Course Description: 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.

 

Section B Elective Courses

Electives offered by the department (starting with BIOC) need to be pre-approved by the Program Director. Some courses may require prerequisites. Elective courses (other than BIOC) may require approval by the course instructor. The electives can be chosen to emphasize either a biological or computer science focus. Following are some electives popularly chosen by Master’s students in the past. Additional elective options can be found in the GW Bulletin.

BIOC 6222 - Biochemical Genetics and Medicine
  • Course Credits: 3 
  • Course Offering: Spring
  • Course Director(s): Wenge Zhu, Elizabeth Sweeney
  • Course Prerequisites: None
     
  • Course Description: The course covered a wide variety of topics including genes, chromatin, gene expression and regulation, epigenetics, genetic alterations, and genome stability. The topics tie into the understanding of these processes and the alteration of signaling pathways in human diseases such as cancer. The role of various technologies such as Nanotechnology targeted drug therapies, and chemotherapies are also discussed.
BIOC 6237 - Proteomics and Biomarker Discovery 
  • Course Credits: 2 
  • Course Offering: Spring
  • Course Director(s): Raja Mazumder
  • Course Prerequisites: None
     
  • Course Description: This course introduces students to common proteomics technologies, the types of data they generate, their significance for systems-level analysis, and their role in advancing biomarker discovery.
BIOC 6295 - Research
  • Course Credits: Variable (1-12) (may be repeated for credits)
  • Course Offering: Fall, Spring, Summer
  • Course Director(s): Anelia Horvath
  • Course Prerequisites: None 
     
  • Course Description: The graduate research course provides students with hands-on research experience in their field of interest. Research must be conducted under the mentorship of a faculty member. Permission of the instructor is required before enrollment.
BIOC 6298 - Advanced reading
  • Course Credits: Variable (1-6)
  • Course Offering: Fall, Spring, Summer
  • Course Director(s): Anelia Horvath
  • Course Prerequisites: Graduate students only
     
  • Course Description: The course allows students to engage in an in-depth exploration of a topic of interest and review writing under the guidance and mentorship of a faculty member. It is designed to improve critical reading and thinking skills and increase analytical, inferential, and evaluative skills. Students hone their scientific writing skills by writing a comprehensive review on the topic they research.
Other Electives
  • 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 introduces students to several core data science concepts. It 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 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. 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, preparing, 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 – Computational Biology and Bioinformatics: Principles and Practices (3 credits)
    Introduction to computational biology as an interdisciplinary science in the 21st century, incl. the algorithmic and statistical principles of bioinformatics, as well as practical training 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. 
     
  • MICR 6236 Fundamentals of Genomics (2 credits)
    This course provides a broad overview of the goals, methods, and applications for genomics and genomic medicine, with a focus on the discovery of disease processes.

 

Section C Research Courses

Thesis Option

To be arranged by the student and designated faculty member.

BIOC 6998 (3 credits), and BIOC 6999 (3 credits) – Thesis (Total 6 credits, fall/spring). Must be registered sequentially

Non-Thesis Option

Credits fulfilled through various electives and/or research projects (BIOC 6295), and/or Advanced Reading (BIOC 6298).

BIOC 6295 (Research Project) - Students participate in a short research project under investigation within the Biochemistry department or in other GW departments or affiliates in related fields. Approval by the program director is required. BIOC6295 content differs each time a course is offered; and may be repeated for credit.