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2 votes Free Closed [?] Life Sciences Biology

The advent of computers transformed science.  Large, complicated datasets that once took researchers years to manually analyze could suddenly be analyzed within a week using computer software.  Nowadays, scientists can use computers to produce several hypotheses as to how a particular phenomenon works, create computer models using the parameters of each hypothesis, input data, and see which hypothetical model produces an output that most closely mirrors reality. Computational biology refers to the use of computers to automate data analysis or model hypotheses in the field of biology.  With computational biology, researchers apply mathematics to biological phenomena, use computer programming and algorithms to artificially create or model the phenomena, and draw from statistics in order to interpret the findings.  In this course, you will learn the basic principles and procedures of computational biology.  You will also learn various ways in which you can apply computational biology to molecular and cell…

Starts : 2002-09-01
13 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Graduate Health Sciences and Technology MIT OpenCourseWare

This course introduces abstraction as an important mechanism for problem decomposition and solution formulation in the biomedical domain, and examines computer representation, storage, retrieval, and manipulation of biomedical data. As part of the course, we will briefly examine the effect of programming paradigm choice on problem-solving approaches, and introduce data structures and algorithms. We will also examine knowledge representation schemes for capturing biomedical domain complexity and principles of data modeling for efficient storage and retrieval. The final project involves building a medical information system that encompasses the different concepts taught in the course.

Computer science basics covered in the first part of the course are integral to understanding topics covered in the latter part, and for completing the assigned homework.

Starts : 2009-09-01
9 votes
MIT OpenCourseWare (OCW) Free Life Sciences Brain and Cognitive Sciences MIT OpenCourseWare Undergraduate

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Starts : 2006-09-01
11 votes
MIT OpenCourseWare (OCW) Free Engineering Chemical Engineering Graduate MIT OpenCourseWare

Numerical methods for solving problems arising in heat and mass transfer, fluid mechanics, chemical reaction engineering, and molecular simulation. Topics: numerical linear algebra, solution of nonlinear algebraic equations and ordinary differential equations, solution of partial differential equations (e.g. Navier-Stokes), numerical methods in molecular simulation (dynamics, geometry optimization). All methods are presented within the context of chemical engineering problems. Familiarity with structured programming is assumed. The examples will use MATLAB®.


The instructor would like to thank Robert Ashcraft, Sandeep Sharma, David Weingeist, and Nikolay Zaborenko for their work in preparing materials for this course site.

Starts : 2016-10-04
No votes
edX Free Closed [?] Life Sciences English Biology & Life Sciences EdX MITx

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. For the last few years, 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. Participants with programming experience will find some weeks easier than students with only biology experience, while those with a biology background should find the week on genetics easier. We recommend that learners try to complete each week to find what interests them the most.

Workshop Content Creators and Residential Leaders

Gregory Hale, Michael Goard, Ph.D., Ben Stinson, Kunle Demuren, Sara Gosline, Ph.D., Glenna Foight, Leyla Isik, Samir El-Boustani, Ph.D., Gerald Pho, and Rajeev Rikhye

Residential Outreach Workshop Organizer and Creator

Mandana Sassanfar, Ph.D.

This workshop includes activities on the following biological topics: population biology, biochemical equilibrium and kinetics, molecular modeling of enzymes, visual neuroscience, genetics, gene expression and development, and genomics. The tools and programming languages include MATLAB, PyMOL, StarGenetics, Python, and R. This course does not require learners to download MATLAB. All MATLAB activities run and are graded within the edX platform. We do recommend that participants download a few other free tools for the activities so that they learn how to use the same tools and programs that scientists use.


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