The quantitative methods boot camp: teaching quantitative thinking and computing skills to graduate students in the life sciences

Abstract

The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a "boot camp" in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students' engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others.

Fig 1. Overall course experience.

Students were asked after each course to rate their overall experience on a five-point scale (Poor, Fair, Good, Very Good, or Excellent). Diverging stacked bars are centered between Fair and Good. To allow comparison between different course offerings, data for each year was normalized by the total number of respondents. Spring 2012: n = 37, Spring 2013: n = 21, Spring 2014: n = 24, Summer 2012: n = 57, Summer 2013: n = 43.

Fig 2. Increase in self-reported MATLAB programming skills.

In the postcourse survey, students were asked: “Rate your ability to program in MATLAB before the course,” and “Rate your ability to program in MATLAB after the course.” Answers were given on a scale from 0 (novice) to 11 (expert). Upper panel: Summer 2013, lower panel: Spring 2014. Scatter plot: Each student is represented by a circle. The diagonal represents no improvement in skill. Insert: Increase in self-reported skill (after-before). Summer 2013: n = 43, Spring 2014: n = 24.

Fig 3. Self-assessed understanding of concepts and skills.

Data shown is for the Spring 2014 offering of the course. Students were asked to rate their understanding of specific skills on a five-point scale (Poor to Excellent, as above). Skills are listed in the order in which they are introduced at QMBC. n = 24.

Fig 4. Future impact of the course.

Students were asked to rate their agreement with the following three questions: “This course provided a practical base and starting point for using MATLAB in my own work,” “The workshop provided me with a practical base/starting point for analyzing quantitative problems,” and “This course has increased the likelihood I will use quantitative methods in my research.” Rating was on a five-point scale (Strongly disagree to strongly agree). Data shown are pooled responses from the last four offerings of the course (Summer 2012 to Spring 2014). n = 141.