Introduction

If you’re interested in data analysis and interpretation, then this is the data science course for you.

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Enhanced throughput: Almost all recently manufactured laptops and desktops include multiple core CPUs. With R, it is very easy to obtain faster turnaround times for analyses by distributing tasks among the cores for concurrent execution. We will discuss how to use Bioconductor to simplify parallel computing for efficient, fault-tolerant, and reproducible high-performance analyses. This will be illustrated with common multicore architectures and Amazon’s EC2 infrastructure.  

Enhanced interactivity: New approaches to programming with R and Bioconductor allow researchers to use the web browser as a highly dynamic interface for data interrogation and visualization. We will discuss how to create interactive reports that enable us to move beyond static tables and one-off graphics so that our analysis outputs can be transformed and explored in real time.

Enhanced reproducibility: New methods of virtualization of software environments, exemplified by the Docker ecosystem, are useful for achieving reproducible distributed analyses. The Docker Hub includes a considerable number of container images useful for important Bioconductor-based workflows, and we will illustrate how to use and extend these for sharable and reproducible analysis.

What you'll learn:

  • Parallel Computing
  • Interactive Graphics
  • Reproducible distributed analysis

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

The Data Analysis for Life Sciences courses are self-paced and include:

  1. Statistics and R for the Life Sciences
  2. Introduction to Linear Models and Matrix Algebra
  3. Statistical Inference and Modeling for High-throughput Experiments
  4. High-Dimensional Data Analysis
  5. Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 
  6. High-performance computing for reproducible genomics
  7. Case studies in functional genomics

 

Meet The Faculty

Rafael Irizarry

Rafael Irizarry

Professor of Biostatistics, Harvard School of Public Health

Dr. Irizarry received his bachelor’s in mathematics in 1993 from the University of Puerto Rico and his Ph.D. in statistics in 1998 from the University of California, Berkeley. He joined the faculty of the Department of Biostatistics in the Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. Dr. Irizarry has worked on the analysis and pre-processing of microarray, next-generation sequencing, and genomic data, and is currently interested translational work, developing diagnostic tools and discovering biomarkers. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data.

Michael Love

Michael Love

Postdoctoral Fellow, T.H. Chan School of Public Health

Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health. Dr. Love received his bachelor’s in mathematics in 2005 from Stanford University, his master’s in statistics in 2010 from Stanford University, and his Ph.D. in Computational Biology in 2013 from the Freie Universität Berlin. Dr. Love uses statistical models to infer biologically meaningful patterns from high-throughput sequencing data, and develops open-source statistical software for the Bioconductor Project.

Vincent Carey

Vincent Carey

Associate Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School

Vincent Carey is Associate Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School. As a Fulbright Specialist and as an invited lecturer, he has given short courses in statistical genomics on four continents. He was an inaugural faculty member in the Cold Spring Harbor Laboratory Summer Course on statistical analysis of genome-scale data, and is former Editor-in-Chief of The R Journal. He is Scientific Director of Bioinformatics in the National Institute of Allergy and Infectious Diseases Immune Tolerance Network, and is a member of the Scientific Advisory Board of the Vaccine and Immunology Statistical Center of the Collaboration for AIDS Vaccine Discovery. Vince is a co-founder of the Bioconductor project.

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