Quantitative Methods in Science: Data Collection, Analysis and Modeling*
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
apply these methods to datasets to sub disciplines in biology and chemistry
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.
The course will include learning Python as it relates to data analysis and modeling.
Areas of Application
To expose students to current fields in which data analysis is crucial, the course focuses on biology and some chemistry. Learning will be project focused. Some examples of potential projects include the following:
Borrowing from the ongoing research in a nearby Research One, NIH-funded lab, students will have the opportunity to:
Take existing tissue (e.g., sheep or mouse brain or cow or mouse heart)
Engage in basic histological work (learning how to use basic microtomes and stereo-dissection microscopes)
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. This project will include a large data collection component.
Yet another focus area will include how to apply quantitative tools to analyze already-existing large datasets from which there are many to choose (“big data” analysis and machine learning).
*An understanding of computer programming fundamentals are required to take this course. Being fluent in a given programming language is not a prerequisite. Basic understanding of genome science is encouraged.