Biomedical Data Simplified
Interest in Data Science is growing in many industries as data becomes a key part of business, finance, and today, biomedical research and medicine in general. Biologists, clinicians and lab technicians can set themselves apart, advance research and become more efficient in their day-to-day activities by leveraging data. However, biomedical data of the future is high-throughput, molecular-level data. This data is non-trivial to process, analyze and interpret. High-throughput data like genomics, transcriptomics, metabolomics and even structural chemical data is poised to revolutionize our understanding of health and disease.
To address this challenge, most courses focus on developing technical skills like coding and statistics, but those can be intimidating for someone that is just getting started. That’s why we created a course that leverages user-friendly tools and modern techniques to help you take advantage of the data revolution happening in the world of biomedicine. In just 1 month, you will learn about high-throughput biomedica datasets, projects in oncology and neurodegenerative diseases and perform your own in-depth analysis to discover biological processes driving disease and creating your own gene signatures associated with disease onset and progression. At the same time, you will understand the workflow of a data scientist that has to leverage infrastructural solutions like the T-BioInfo platform and user-friendly coding environments like R studio. Key concepts in advanced analysis from exploration to machine learning will become clear in the context of their use in biomedical research and discovery.
The course will rely on materials developed by SIPRI.io as a part of their data science training and the BioML series of courses developed by Pine Biotech for the Georgetown Medical Center Systems Medicine Program.
About The Instructors
Dr. Anand Lakshmanan
Founder & CEO - SIRPI
Dr. Anand Lakshmanan has helped academic institutions and corporations deep dive into their data and make better decisions. He has 17+ years experience in academia and industry. He has designed experiments and analyzed data in areas across domains.
Most recently he worked at Apple in Cupertino, California as an Antenna Design Engineer for 6 years. He has published or co-published several papers and holds 7 patents. He believes that all scientists and professionals need to be data-savvy to make decisions and persuade actions.
Co-Founder & CEO - Pine Biotech
Elia Brodsky is the co-founder and CEO of Pine Biotech, a startup focused on machine learning for multi-omics data. The team under his management is collaborating with researchers at top US Academic Institutions and pharmaceutical companies. In 2017, Pine Biotech launched an online training portal designed to provide project-based bioinformatics training in multi-omics data.