This college-level Biostatistics course will teach you the skills required for success in future analytical studies in biology.
Welcome to our comprehensive undergraduate-level Biostatistics course, designed to equip you with essential skills in statistical analysis and study design within the field of biology. This course offers a unique blend of theoretical knowledge and practical application, providing you with a solid foundation in biostatistical research that is highly valued in healthcare and various biology-related industries.
This course is designed as an introductory-level program, and there are no specific prerequisites. However, a basic understanding of biology and mathematics would be beneficial.
By mastering these skills, you'll be well-equipped to tackle complex biological problems, contribute to groundbreaking research, and make data-driven decisions in healthcare and biological sciences. This course not only fulfills undergraduate program requirements but also lays a strong foundation for future career prospects in the rapidly growing field of biostatistics.
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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.
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.
