Course description
Quantitative Biology Workshop
Do you have an interest in biology and quantitative tools? Do you know computational methods but do not realize how they apply to biological problems? Do you know biology but do not understand how scientists really analyze complicated data? 7.QBWx: Quantitative Biology Workshop is designed to give learners exposure to the application of quantitative tools to analyze biological data at an introductory level. The Biology Department of MIT has run this workshop-style course as part of a one-week outreach program for students from other universities. With 7.QBWx, we can give more learners from around the world the chance to discover quantitative biology. We hope that this series of workshops encourages learners to explore new interests and take more biology and computational courses.
We expect that learners from 7.00x Introduction to Biology - The Secret of Life or an equivalent course can complete this workshop-based course without a background in programming. The course content will introduce programming languages but will not teach any one language in a comprehensive manner. The content of each week varies. We want learners to have an introduction to multiple languages and tools to find a topic that they would want to explore more. We recommend that learners try to complete each week to find what interests them the most.
Upcoming start dates
Who should attend?
Prerequisites
7.00x Introduction to Biology or similar (biochemistry, molecular biology, and genetics). Programming experience is not required. An introduction to MATLAB, Python, and R is included.
Course delivery details
This course is offered through Massachusetts Institute of Technology, a partner institute of EdX.
4-8 hours per week
Costs
- Verified Track -$99
- Audit Track - Free
Certification / Credits
What you'll learn
- Apply quantitative methods to biological problems
- Define computational vocabulary
- Write Python, MATLAB, and R code to analyze biological data
- Examine any protein structure in PyMOL
- Analyze how to answer a scientific question through a step-by-step thought process.
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edX
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