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.
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.
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.
