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
This course focuses on the relationship between protein structure and functions, and connections between metabolic pathways and human diseases.
Topics covered will include gene expression and regulation, epigenetics, genetic alterations and genome stability.
A broad overview of methods and applications of bioinformatics in the life sciences.
Current literature in biochemistry. Limited to graduate students in the department. May be repeated for credit.
Analysis of genome data and practical applications of medical genomics.
Application of proteomics concepts and methods through the use of databases and tools.
Basic principles of NGS technologies, data analysis and interpretation. Research and clinical applications and student-designed projects.
Upon completion of the entire Core Section A, students must pass a written examination on the material covered in these courses.
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 datatypes 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 algorithm and apps for existing HIVE infrastructures.
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
A rigorous and up-to-date treatment of the theory and methods of systematic, including phylogenetic inference and its applications in evolutional biology.
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.
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.
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 and
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.
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
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.
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.
Introduction to statistical techniques and reasoning applicable to the biomedical and related sciences. Properties of basic probability functions: binomial, Poisson and normal. Data analysis, inference and experimental design.
Section C Research Courses
To be arranged by student and designated faculty member.
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.