Quantitative Methods in Science:
Data play intimate roles in all aspects of life, personal and professional, from individual online purchases to business and science. The deluge of data generated from many sources, or so-called “Big Data”, present challenges in analyzing them, e,g, identifying patterns, let alone translating those patterns into meaningful information and understanding. The knowledge required to analyze data in the sciences are drawn from multiple disciplines, including: mathematics, statistics, information sciences, and computer science.
The objective of this course is to introduce students to the different questions that can and are being explored in labs today using quantitative methods and analysis. We will engage students in quantitative applications using Python to:
learn how to extract meaning from data using quantitative methods
apply quantitative methods to datasets across scientific fields, whether in biology or physics, for example
learn about how computing is being used to detect patterns in large datasets that individual analysts would be less likely to identify and then seek ways to explain those patterns using quantitative methods.
Areas of Application
Student will apply the fundamental rules of Newtonian physics to satellite positioning, military applications, etc. Students may also investigate astrophysics, which is inherently quantitative in nature and replete with large datasets. This approach will offer students a more applications-based sense and feel for physics, itself.
To expose students to other fields in which data analysis is crucial, we will investigate the natural sciences, as well. To borrow from the ongoing research in a Research One, NIH-funded lab in which one of the Innovation Institute’s instructor’s works, students will:
Take existing tissue (e.g., sheep or mouse brain or cow or mouse heart)
Learn how to undertake basic histological work
Perform a quantification of cell types and/or cell development
Using quantitative methods students will quantify the numbers of cells in a heart or axons in a piece of brain, etc. They might compare or develop algorithms to try to identify variables that explain some aspect of their data. In these instances, students will generate as well as analyze data.
Another quantitative-based project will involve comparisons between the effectiveness of different student-designed bacterial growth media using time-lapse photography, which would result in a large data collection component.
Two other potential projects of relevance include how to apply quantitative algorithms to analyze already-existing large datasets or protein folding, which lends well to large data analysis. There are unlimited applications, and in this course students and instructors will together identify their focus projects.
*Basic programming language understanding is required to take this course. Being fluent in a given programming language is not a prerequisite. Basic understanding of genome science and Newtonian mechanics are strongly encouraged.