Whether you are a student, basic scientist, researcher, clinician, or librarian, this course is designed to help you understand, analyze, and interpret biomedical big data.
An intermediate-level course offered by GeorgetownX
"Demystifying Biomedical Big Data: A User's Guide" is designed to empower professionals and students in the biomedical field with limited bioinformatics experience. This comprehensive course aims to bridge the gap between the ever-growing volume of biomedical data and the ability to effectively analyze and interpret it for scientific discoveries and patient care.
While not strictly required, a background in biology (high school or undergraduate level) is suggested. Students are encouraged to review relevant courses in biology and life sciences, including genetics, genomics, DNA/RNA/Protein structure and function, transcription, and translation.
Each week includes lectures, demonstrations, and hands-on exercises to reinforce learning and practical application of the concepts covered.
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Drugs are biologically active compounds. Drug discovery and drug development rely on methods for measuring biological activity. The term “activity” is quite general. In practice, a substance’s biological activity can refer to how a substance affects a biological pathway, cell-level responses, safety risks, how long a substance resides within in a system, as well as a substance’s effects in an organism (called pharmacodynamics). The design of methods for measuring activity is a critical aspect of drug discovery. The design of robust and informative methods can accelerate the drug discovery and development process.
'Statistical and Probabilistic Foundations of AI' provides an accessible overview of the mathematics and statistics behind fundamental concepts of data science, machine learning, and artificial intelligence. It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. It provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level.
The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
In this course, you will develop a working knowledge of linear relationship data in healthcare and practice using R statistical programming to analyze this data. This course covers the concepts of correlation and linear relationships, ordinary least squares (OLS) linear regression, diagnostic tests for OLS linear regression, and dummy variables.
This course is designed as a basic introduction to the complex world of research methods and provides both theoretical and practical information for students to give them a foundation for carrying out research in a wide range of disciplines using quantitative, qualitative, and mixed methods.
