Now back at NUI Galway, my current teaching load involves courses in introductory computing and in graduate level computational genomics. For old times sake I list the courses I taught whilst being based in NYC.
1352 Computational Genomics and Epigenomics (Einstein)
  • Outline: The purpose of this course is to blend didactic and practical content over a one semester period in such a way so as to give the students both a foundation and hands-on experience of contemporary genomics and epigenomics methodologies. Each topic in the syllabus will be delivered individually by a member of the Einstein faculty whose research expertise matches that topic’s content, and each topic will have both taught and practical components. The students will use their laptops in class to ‘walk through’ these practical sessions. The final examination will consist of a set of genomics/epigenomics based ‘research level’ problems which the students will solve within a set time frame. The goal of this course is to inform and train the student cohort up to an operational level of capability in the current methods and techniques used in contemporary genomics research.
  • Syllabus: Introductory Bioinformatics; Applied statistics using R; Working with Genomes; Statistical principles in genomics; Genomic variation and mutation; Genome-wide association studies; Functional genomics; Epigenomics; Integrative Techniques; Ethical, Legal and Social aspects of genomics.
  • Details: Runs as a Block III course in 2014 - lecture notes etc. available via Angel
Genomics 101 for MSTPs (Einstein)
  • Outline: This course was requested by Dr. Myles Akabas, Director of Einstein's Medical Scientist Training Program with the aim of introducing the incoming 'freshman' class of MSTP students to the science, and informatics, of genomics. Over the 8 week program, genomics concepts are introduced and understood from both a biological and information theory perspective. Extensive use is made of online tools, such as the UCSC Genome Browser, Galaxy and other tools to illustrate the hands on practicality of the domain.
  • Syllabus: Genes and Genomes as information constructs; Genome assembly and gene identification; Genome annotation; Transcription and Regulation; Pathways and Gene Ontologies.
  • Details: Runs as a Block I course in 2013 - lecture notes etc. available via Angel
MAT5270 Data Science: Fundamentals and Applications (Yeshiva)
  • Outline: This course is designed to introduce the statistical and computational fundamentals that form the basis for contemporary data science applications in biomedical science, finance and other cognate 'big data' disciplines. Core components include data exploration, data modeling, the use of data mining technologies and application examples. Course material will be complemented by hands-on programming experience, using the iPython programming environment, to allow the class to gain a 'hands on' experience of data science analytics.
  • Syllabus: Data Examination (univariate/bivariate/multivariation,time-series analysis); Data Modeling (frequentist/Bayesian approaches); Data Mining (clustering, classification); Prediction
  • Details: Runs as a Spring Semester course in 2014 - lecture notes etc. available via Angel.