This course provides an introduction to computer systems and scientific computing environments to enable effective use of computational and data resources. The course assumes no prior computing experience and is broken into three component modules:

• UNIX/Linux fundamentals
• Computer system architectures and applications in scientific computing
• Introduction to scientific programming in Python 3
  
  

Credits: Three | Fall

This course is a computer science-intensive program intended as a survey of algorithms—that is, computational methods used to solve appropriately defined problems, and their implementation on modern scientific computing hardware.

In this course, we use Python 3 as the core programming tool.

Prerequisites required.

Credits: Three | Spring

This course is designed to train students, staff, and faculty in commonly used methods to organize, mine, and learn from data sets, especially those that are complex and large (big data). These methods include basic data concepts, classification, clustering, network inference and analysis and outlier/anomaly detection. Students in teams will also be expected to conceive a relevant project at the beginning of the course and present their approach and results at the end.

Prerequisites required
Credits: Three | Spring

Fundamentals in Biomedical Sciences offers a practical and comprehensive overview of the most fundamental topics necessary for modern Biomedical Scientists. We focus on key concepts, their supporting experimental evidence, and their application in contemporary research and clinical science.

The Course is divided into 4 units, each worth 2 credits.

• Biochemistry and Molecular Biology
  Genetics, DNA and RNA regulation, Protein processing
• Pharmacology
  Receptor theory, Structure-based drug design, Pharmacokinetics
• Cell and Developmental Biology
  Cellular signaling, Cytoskeleton, Developmental Biology
• Neuroscience
  Neurophysiology, Neuroanatomy, Plasticity

Students in the MSDSAI program choose 3 of the 4 units, taken in the fall semester of Year 1.

Credits: Six | Fall

This course is required for all first-year graduate students, following National Institutes of Health (NIH) mandates. Topics include:

• Research misconduct
• Experimental design and data management practices
• Mentor and trainee responsibilities; collaborative research
• Conflicts of interest; intellectual property
• The protection of human subjects
• The welfare of laboratory animals
• Publication, authorship, and peer review
• The grant process and fiduciary responsibility

Each session is a 45-minute lecture with 15 minutes of discussion.
Credits: Half credit | Fall

This course is required for all first-year graduate students. Topics include:

• Experimental design
• Rigor at the bench
• Validation of biological and chemical reagents
• Animal and human studies
• The importance and design of statistics
• Data collection, storage and open science
• Preparation of data for publication
• Review of NIH clearinghouse and discussion

Credits: Half credit | Spring