Biomathematical Sciences Program

The goal of this program is to create educational structures that help Five College students become scientifically multilingual in fields of life and quantitative sciences by providing the means for each student to trace their own intentional pathway across the disciplines.

The daunting complexities of biological phenomena from neurological development to ecosystem carbon fluxes require development of new modeling and analytic approaches. Sorting through potential mechanisms and patterns to develop testable hypotheses requires collaboration between life science investigators and mathematicians, statisticians, and computer scientists.

Cutting edge life science research increasingly involves such collaborations, but researchers are too often stymied by the different languages of their disciplines. The goal of this program is to create educational structures that help Five College students become scientifically multilingual in fields of life and quantitative sciences by providing the means for each student to trace their own intentional pathway across the disciplines.

The Five College Certificate in Biomathematical Sciences is currently available to students at Amherst, Hampshire, Mount Holyoke and Smith Colleges, and courses in the program are available to all students enrolled at one of the five campuses through the Five College Course Interchange.

On This Page

Nicole DelRosso working in a lab

Engaged Alumni

Learn how Hampshire College alum Nicole DelRosso engaged with Biomathematical Sciences while a student at the Five Colleges!

dorothy wrinch in the smith mobile

Dorothy Wrinch: The First Biomathematician in the Valley

The Four College Biomathematics Program, begun in 2011, allows the colleges of the Pioneer Valley to work together across many scientific disciplines. This, however, is not the first biomathematics collaboration in the college consortium. The first experiment with intercollege cooperation in any field began 70 years earlier, with the appointment of Dorothy Wrinch in 1941 to teach courses on molecular biology at Amherst, Mount Holyoke, and Smith colleges. In her words, the effort was “the first of its kind to be given in any center of higher education.”

Certificate Requirements

The Five College Certificate in Biomathematical Sciences is currently available to students enrolled at Amherst, Hampshire, Mount Holyoke and Smith Colleges.

A minimum of six courses and a research experience are required. In addition to an introductory and capstone biomathematical sciences courses, students are expected to take courses complementing their major. Students with majors in the quantitative sciences (mathematics, statistics, computer science, engineering, and physics) are expected to take courses in the life sciences (biology, neuroscience, biochemistry, chemistry, etc.) and students with majors in the life sciences are expected to take courses in the quantitative sciences for the certificate. A biomathematical science research experience is also an essential component of the certificate, to provide an experience outside the classroom.

In summary, to qualify for the Five College Certificate in Biomathematical Sciences, students must complete the following:

  • One gateway course: an entry level biomath course to introduce current research questions and foundational skills
  • 4 courses in the life sciences if you are majoring in a quantitative science, or 4 courses in the quantitative sciences if you are majoring in a life science
  • A capstone course in biomathematical or biostatistical methods or an honors thesis in a biomathematical sciences topic
  • A research experience of one summer (or equivalent) with a team of life and mathematical science mentors

The gateway course should contain some basic programming skills and examples in biology. Courses that students have used include Frontiers in Biomathematics (Smith, IDP 170), Modeling in the Sciences (Smith, MTH 205), and Mathematical Modeling (Amherst, Math 140). At least 2 of the 4 courses that complement the major should be upper level courses. Hybrid courses, e.g., computational biology, bioinformatics, biostatistics, can count toward either life sciences or quantitative sciences. To explore the certificate program, please contact your campus certificate advisor or the current program director.

Courses

Spring 2022 Biomathematics Courses

01
4.00

Lee Spector

MW 01:30 PM-02:50 PM

Amherst College
COSC-452-01-2122S

SCCEA331

lspector@amherst.edu

Evolutionary computation techniques harness the mechanisms of biological evolution, including mutation, recombination, and selection, to build software systems that solve difficult problems or shed light on the nature of evolutionary processes. In this course students will explore several evolutionary computation techniques and apply them to problems of their choosing. The technique of genetic programming, in which populations of executable programs evolve through natural selection, will be emphasized.

Requisite: COSC 112. Limited to 20 students. Preference given to Computer Science majors. Spring semester: Professor Spector.

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Tanya L. Leise

TTH 03:00 PM-04:20 PM; M 03:00 PM-03:50 PM

Amherst College
MATH-140-01-2122S

SMUD014; SMUD014

tleise@amherst.edu

Mathematical modeling is the process of translating a real world problem into a mathematical expression, analyzing it using mathematical tools and numerical simulations, and then interpreting the results in the context of the original problem. Discussion of basic modeling principles and case studies will be followed by several projects from areas including biology and the social sciences (e.g., flocking and schooling behavior, disease spread in populations, generation of artificial societies). This course has no requisites; projects will be tailored to each student’s level of mathematical preparation. Four class hours per week, with occasional in-class computer labs.

Limited to 24 students. Spring semester. Professor Leise.

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Ryan P. McShane

TTH 01:00 PM-02:20 PM

Amherst College
STAT-225-01-2122S

SCCEA331

rmcshane@amherst.edu

This course is an introduction to nonparametric and distribution-free statistical procedures and techniques. These methods rely heavily on counting and ranking techniques and will be explored through both theoretical and applied perspectives. One- and two-sample procedures will provide students with alternatives to traditional parametric procedures, such as the t-test. We will also investigate correlation and regression in a nonparametric setting. A variety of other topics may be explored in the nonparametric setting including resampling techniques (for example, bootstrapping), categorical data and contingency tables, density estimation, and the one-way and two-way layouts for analysis of variance. The course will emphasize data analysis (with appropriate use of statistical software) and the intuitive nature of nonparametric statistics.

Requisite: STAT 111 or STAT 135. Limited to 24 students.  Professor McShane. 

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Pamela B. Matheson

MWF 09:00 AM-09:50 AM

Amherst College
STAT-230-01-2122S

WEBS102

pmatheson@amherst.edu

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Amy S. Wagaman

TTH 08:30 AM-09:50 AM

Amherst College
STAT-231-01-2122S

WEBS102

awagaman@amherst.edu

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

Instructor Permission: Permission is required for interchange registration during all registration periods.

02
4.00

Amy S. Wagaman

TTH 11:30 AM-12:50 PM

Amherst College
STAT-231-02-2122S

WEBS102

awagaman@amherst.edu

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Justin Baumann

TTH 01:45PM-03:00PM

Mount Holyoke College
116782

Kendade G06

jbaumann@mtholyoke.edu
The statistics sections of biology articles have become so technical and jargon-filled that many biologists feel intimidated into skipping them or blindly accepting the stated results. But how can we ask relevant questions or push the boundaries of knowledge if we skip these sections? Using lectures, data collection, and hands-on analysis in R, this course will connect statistics to biology to help students develop a gut instinct for experimental design and analysis. We will explore sampling bias and data visualization and review methods and assumptions for the most common approaches with examples from current biological literature and our own data.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
0.00

Justin Baumann

M 01:30PM-04:20PM

Mount Holyoke College
116783

Kendade G06

jbaumann@mtholyoke.edu

02
0.00

Justin Baumann

W 01:30PM-04:20PM

Mount Holyoke College
116784

Kendade G06

jbaumann@mtholyoke.edu

01
4.00

Maria Gomez

MW 11:30AM-12:45PM

Mount Holyoke College
116401

Clapp Laboratory 206

magomez@mtholyoke.edu
Chemists have always been interested in understanding patterns in their data. The scientific method uses observations to create theories and models to understand physical phenomena. Data science algorithms allow us to find unexpected patterns in chemical data. New chemical theories can be developed using a combination of data from either experiment or simulation, algorithms and physical insight. This class uses the case method providing three challenge problems to find hidden chemical rules from large chemical data sets through algorithms and physical insight. There will be lectures on the physical/chemical problems, the data sets, and the possible algorithms to consider before the teams of students tackle these problems. The teams will write papers on their findings and use the peer review process to improve their papers.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Shruti Biswal

MW 01:45PM-03:00PM

Mount Holyoke College
116934

Clapp Laboratory 218

sbiswal@mtholyoke.edu
Have you ever used Google's image search tool and wondered how the search results were found? Why is it so difficult for a computer to "see" as we do? Computer scientists are actively researching how to approach this challenge of "computer vision." This course will introduce the fundamentals of applied computing using computer vision as a motivating theme. Students will learn foundations of programming (in the Python programming language) before working with computational tools more independently.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Melody Su

T 01:45PM-03:00PM;TH 01:45PM-04:20PM

Mount Holyoke College
116900

Prospect Hall 037;Prospect Hall 039

msu@mtholyoke.edu
This intermediate-level course presents a hands-on introduction to robotics. Each student will construct and modify a robot controlled by an Arduino-like microcontroller. Topics include kinematics, inverse kinematics, control-theory, sensors, mechatronics, and motion planning. Material will be delivered through one weekly lecture and one weekly guided laboratory. Assignments include a lab-preparatory homework, guided lab sessions, and out-of-class projects that build upon the in-class sessions. Students have access to the Fimbel Maker and Innovation lab for fabricating and demonstrating their robots.
Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Melody Su

TTH 11:30AM-12:45PM

Mount Holyoke College
116890

Clapp Laboratory 206

msu@mtholyoke.edu
How does Neflix learn what movies a person likes? How do computers read handwritten addresses on packages, or detect faces in images? Machine learning is the practice of programming computers to learn and improve through experience, and it is becoming pervasive in technology and science. This course will cover the mathematical underpinnings, algorithms, and practices that enable a computer to learn. Topics will include supervised learning, unsupervised learning, evaluation methodology, and Bayesian probabilistic modeling. Students will learn to program in MATLAB or Python and apply course skills to solve real world prediction and pattern recognition problems. Programming Intensive.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Tori Day

MWF 08:30AM-09:45AM

Mount Holyoke College
116879

Clapp Laboratory 407

day22v@mtholyoke.edu
Topics include differential and integral calculus of functions of several variables.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Tori Day

MWF 10:00AM-11:15AM

Mount Holyoke College
116881

Clapp Laboratory 407

day22v@mtholyoke.edu
Topics include differential and integral calculus of functions of several variables.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Samantha Kirk

MWF 08:30AM-09:45AM

Mount Holyoke College
116888

Clapp Laboratory 401

skirk@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Chassidy Bozeman

MWF 11:30AM-12:45PM

Mount Holyoke College
116889

Art 221

cbozeman@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

03
4.00

Lidia Mrad

MW 03:15PM-04:30PM;F 03:15PM-04:05PM

Mount Holyoke College
116891

Art 219;Art 219

lmrad@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Timothy Chumley

TTH 11:30AM-12:45PM

Mount Holyoke College
116897

Clapp Laboratory 407

tchumley@mtholyoke.edu
Dynamical systems are mathematical models that evolve with time -- for example, the population of a species in an ecosystem or the price of a financial asset. This course will focus on discrete-time models where one iterates a single variable function and follows the evolution of points in its domain. Our aim will be to study the qualitative, long-term behavior of these models by developing mathematical theory and doing simulation. Topics will include periodicity, bifurcations, chaos, fractals, and computation.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Pramesh Subedi

MWF 10:00AM-11:15AM

Mount Holyoke College
116907

Clapp Laboratory 402

psubedi@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Laurie Tupper

MWF 01:45PM-03:00PM

Mount Holyoke College
116909

Clapp Laboratory 206

ltupper@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

03
4.00

Pramesh Subedi

MWF 03:15PM-04:30PM

Mount Holyoke College
116911

Clapp Laboratory 407

psubedi@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Laurie Tupper

MWF 11:30AM-12:45PM

Mount Holyoke College
116913

Clapp Laboratory 407

ltupper@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Marie Ozanne

MWF 01:45PM-03:00PM

Mount Holyoke College
116916

Clapp Laboratory 402

mozanne@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Amy Nussbaum

TTH 10:00AM-11:15AM

Mount Holyoke College
116918

Clapp Laboratory 402

anussbau@mtholyoke.edu
The methods taught in traditional statistics courses are based on assumptions that are often not satisfied by real data sets. In this course we will learn about approaches that require fewer assumptions, known as nonparametric methods. After taking this course, students will be able to examine assumptions for different approaches to statistical inference, compare nonparametric statistical tests such as sign and Wilcoxon tests to their parametric equivalents, and implement non-parametric approaches using R. In addition, the course will incorporate computational techniques for statistical analysis, including simulation, permutation tests, and bootstrapping.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Ileana Streinu

M W F 1:20 PM - 2:35 PM

Smith College
CSC-205-01-202203

Ford 345

istreinu@smith.edu
MTH 205-01, CSC 205-01
Offered CSC 205 and MTH 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific topics, organized as case studies, span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems and discrete geometry. This is a project-based course and provides elementary training in programming using Mathematica. Prerequisites: MTH 112 or MTH 114. CSC 111 recommended. Enrollment limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Ileana Streinu

M W F 1:20 PM - 2:35 PM

Smith College
MTH-205-01-202203

Ford 345

istreinu@smith.edu
MTH 205-01, CSC 205-01
Offered CSC 205 and MTH 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific topics, organized as case studies, span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems and discrete geometry. This is a project-based course and provides elementary training in programming using Mathematica. Prerequisites: MTH 112 or MTH 114. CSC 111 recommended. Enrollment limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Luca Capogna

TU TH 2:45 PM - 4:00 PM

Smith College
MTH-364pd-01-202203

Burton 301

lcapogna@smith.edu
Partial differential equations allow us to track how quantities change over multiple variables, e.g. space and time. This course provides an introduction to techniques for analyzing and solving partial differential equations and surveys applications from the sciences and engineering. Specific topics include Fourier series, separation of variables, heat, wave and Laplace’s equations, finite difference numerical methods, and introduction to pattern formations. Prerequisite: MTH 211, MTH 212, and MTH 264 strongly recommended) or MTH 280/281, or permission of the instructor. Prior exposure to computing (using Matlab, Mathematica, Python, etc.) will be helpful.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

Fall 2022 Biomathematics Courses

01
4.00

Josef G. Trapani

TTH 11:30 AM-12:50 PM

Amherst College
BIOL-351-01-2223F
jtrapani@amherst.edu
BIOL-351-01, NEUR-351-01

(Offered as BIOL 351 and NEUR 351) This laboratory course will provide a deeper understanding of the physiological properties of the nervous system. We will address the mechanisms underlying electrical activity in neurons, as well as examine the physiology of synapses; the transduction and integration of sensory information; the function of nerve circuits; the trophic and plastic properties of neurons; and the relationship between neuronal activity and behavior. Laboratories will apply electrophysiological methods to examine neuronal activity and will include experimental design as well as analysis and presentation of collected data. Throughout the course, we will focus on past and current neurophysiology research and how it contributes to the field of neuroscience. Three hours of laboratory work per week.

Requisites: BIOL 191 and CHEM 151; PHYS 117 or 124 is recommended. Limited to one lab section with 18 students. Open to juniors and seniors. Fall semester. Professor Trapani.

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
0.00

Josef G. Trapani

W 02:00 PM-05:00 PM

Amherst College
BIOL-351L-01-2223F
jtrapani@amherst.edu
BIOL-351L-01, NEUR-351L-01

01
4.00

Lee Spector

TTH 02:30 PM-03:50 PM

Amherst College
COSC-247-01-2223F
lspector@amherst.edu

Machine Learning algorithms allow computers to be taught to perform tasks without being explicitly programmed. This course is an introduction to machine learning and data mining. The course will explore supervised, unsupervised, ensemble and reinforcement learning. Topics may include: decision tree learning, rule learning, neural networks, support vector machines, Bayesian learning, clustering, hidden Markov model learning, and/or deep learning. The material of this course has some overlap with that of Computer Science 241, but it is permissible to take both.

Requisite: COSC-211. Fall Semester: Professor Spector. 

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Katherine E. Moore

MWF 10:00 AM-10:50 AM; TH 09:00 AM-09:50 AM

Amherst College
MATH-272-01-2223F
kmoore@amherst.edu

The study of vector spaces over the real and complex numbers, introducing the concepts of subspace, linear independence, basis, and dimension; systems of linear equations and their solution by Gaussian elimination; matrix operations; linear transformations and their representations by matrices; eigenvalues and eigenvectors; and inner product spaces. This course will feature both proofs and applications, with special attention paid to applied topics such as least squares and singular value decomposition. Four class hours per week, with occasional in-class computer labs.

Requisite: MATH 121 or consent of the instructor. This course and MATH 271 may not both be taken for credit. Limited to 25 students. Fall and Spring semester: The Department. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Pamela B. Matheson

MWF 09:00 AM-09:50 AM

Amherst College
STAT-230-01-2223F
pmatheson@amherst.edu

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall semester: Professors Matheson and Horton. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Pamela B. Matheson

MWF 10:00 AM-10:50 AM

Amherst College
STAT-230-02-2223F
pmatheson@amherst.edu

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall semester: Professors Matheson and Horton. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Brittney E. Bailey

TTH 08:30 AM-09:50 AM

Amherst College
STAT-231-01-2223F
bebailey@amherst.edu

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools.

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Brittney E. Bailey

TTH 11:30 AM-12:50 PM

Amherst College
STAT-231-02-2223F
bebailey@amherst.edu

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools.

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Amy S. Wagaman

MWF 11:00 AM-11:50 AM

Amherst College
STAT-240-01-2223F
awagaman@amherst.edu

Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering). Additional methods covered may include factor analysis, dimension reduction methods, or network analysis at instructor discretion. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.

The course is expected to include small group work, interactive labs, peer interactions such as peer review and short presentations, and a personal project, to foster student engagement in the course and with each other.

Requisite: STAT 111 or 135. Limited to 24 students. Fall semester. Omitted 2021-22. Professor Wagaman.

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Martha Hoopes

MWF 11:30AM-12:45PM

Mount Holyoke College
118111
mhoopes@mtholyoke.edu
This ecology course will cover the fundamental factors controlling the distribution and abundance of organisms, including interactions with the abiotic environment, fitness and natural selection, population growth and dynamics, species interactions, community dynamics, and diversity. We will address variation across space and time. The course will combine observational, experimental, and mathematical approaches to some of the applications of ecological theory, including conservation, disease dynamics, and biological control.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
0.00

Martha Hoopes,Molly McCutcheon

M 01:30PM-04:20PM

Mount Holyoke College
118112

Clapp Laboratory 008

mhoopes@mtholyoke.edummccutcheon@mtholyoke.edu

02
0.00

Molly McCutcheon,Martha Hoopes

T 01:30PM-04:20PM

Mount Holyoke College
118113

Clapp Laboratory 008

mmccutcheon@mtholyoke.edumhoopes@mtholyoke.edu

03
0.00

Martha Hoopes,Molly McCutcheon

W 01:30PM-04:20PM

Mount Holyoke College
118114

Clapp Laboratory 008

mhoopes@mtholyoke.edummccutcheon@mtholyoke.edu

04
0.00

Molly McCutcheon,Martha Hoopes

TH 01:30PM-04:20PM

Mount Holyoke College
118116

Clapp Laboratory 008

mmccutcheon@mtholyoke.edumhoopes@mtholyoke.edu

01
4.00

Timothy Chumley

MWF 10:00AM-11:15AM

Mount Holyoke College
118412
tchumley@mtholyoke.edu
Topics include differential and integral calculus of functions of several variables.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Timothy Chumley

MWF 11:30AM-12:45PM

Mount Holyoke College
118413
tchumley@mtholyoke.edu
Topics include differential and integral calculus of functions of several variables.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Samantha Kirk

MWF 08:30AM-09:45AM

Mount Holyoke College
118415
skirk@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Samantha Kirk

MWF 01:45PM-03:00PM

Mount Holyoke College
118416
skirk@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Dylan Shepardson

MWF 03:15PM-04:30PM

Mount Holyoke College
118423
dshepard@mtholyoke.edu
This is an introduction to differential equations for students in the mathematical or other sciences. Topics include first-order equations, second-order linear equations, and qualitative study of dynamical systems
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Samantha Kirk

MWF 03:15PM-04:30PM

Mount Holyoke College
118424
skirk@mtholyoke.edu
This course develops the ideas of probability simultaneously from experimental and theoretical perspectives. The laboratory provides a range of experiences that enhance and sharpen the theoretical approach and, moreover, allows us to observe regularities in complex phenomena and to conjecture theorems. Topics include: introductory experiments; axiomatic probability; random variables, expectation, and variance; discrete distributions; continuous distributions; stochastic processes; functions of random variables; estimation and hypothesis testing.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Isabelle Beaudry

MWF 08:30AM-09:45AM

Mount Holyoke College
118425
ibeaudry@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Instructor To Be Announced

MWF 11:30AM-12:45PM

Mount Holyoke College
118426
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

03
4.00

Pramesh Subedi

MWF 10:00AM-11:15AM

Mount Holyoke College
118427
psubedi@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

04
4.00

Instructor To Be Announced

MWF 01:45PM-03:00PM

Mount Holyoke College
118428
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Isabelle Beaudry

MWF 11:30AM-12:45PM

Mount Holyoke College
118429
ibeaudry@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Instructor To Be Announced

MWF 10:00AM-11:15AM

Mount Holyoke College
118430
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Laurie Tupper

MWF 10:00AM-11:15AM

Mount Holyoke College
118833
ltupper@mtholyoke.edu
How do you get informative research results? By doing the right experiment in the first place. We'll look at the techniques used to plan experiments that are both efficient and statistically sound, the analysis of the resulting data, and the conclusions we can draw from that analysis. Using a framework of optimal design, we'll examine the theory both of classical designs and of alternatives when those designs aren't appropriate. On the applied side, we'll use R to explore real-world experimental data from science, industry, and everyday life; and we'll discuss key principles for working with expert (and not-so-expert) collaborators to help them set up the experiments they need.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Laurie Tupper

MWF 01:45PM-03:00PM

Mount Holyoke College
118431
ltupper@mtholyoke.edu
This course includes methods for choosing, fitting, evaluating, and comparing statistical models; introduces statistical inference; and analyzes data sets taken from research projects in the natural, physical, and social sciences.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
3.00

Rob Dorit

TU TH 10:50 AM - 12:05 PM

Smith College
BIO-334-01-202301

Bass 210

rdorit@smith.edu
This course focuses on methods and approaches in the emerging fields of bioinformatics and molecular evolution. Topics include the quantitative examination of genetic variation; selective and stochastic forces shaping proteins and catalytic RNA; data mining; comparative analysis of whole genome data sets; comparative genomics and bioinformatics; and hypothesis testing in computational biology. We explore the role of bioinformatics and comparative methods in the fields of molecular medicine, drug design, and in systematic, conservation and population biology. Prerequisite: BIO 132, or BIO 230, or BIO 232, or permission of the instructor. Laboratory (BIO 335) is strongly recommended but not required.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Katherine M. Kinnaird

M W 10:50 AM - 12:05 PM

Smith College
CSC-294-01-202301

Bass 002

kkinnaird@smith.edu
An introduction to machine learning from a programming perspective. Students will develop an understanding of the basic machine learning concepts (including underfitting/overfitting, measures of model complexity, training/test set splitting, and cross validation), but with an explicit focus on machine learning systems design (including evaluating algorithmic complexity and development of programming architecture) and on machine learning at scale. Principles of supervised and unsupervised learning will be demonstrated via an array of machine learning methods including decision trees, k-nearest neighbors, ensemble methods, and neural-networks/deep-learning as well as dimension reduction, clustering and recommender systems. Students will implement classic machine learning techniques, including gradient descent. Prerequisites: CSC 212, CSC 250 & (MTH 112 or MTH 211), and knowledge of Python.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Pau Atela

TU TH 2:45 PM - 4:00 PM

Smith College
MTH-264de-01-202301

Burton 301

patela@smith.edu
This course gives an introduction to the theory and applications of ordinary differential equations. We explore different applications in physics, chemistry, biology, engineering and social sciences. We learn to predict the behavior of a particular system described by differential equations by finding exact solutions, making numerical approximations, and performing qualitative and geometric analysis. Specific topics include solutions to first order equations and linear systems, existence and uniqueness of solutions, nonlinear systems and linear stability analysis, forcing and resonance, Laplace transforms. Prerequisites: MTH 112, MTH 212 and MTH 211 (recommended) or PHY 210, or permission of the instructor.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Shiya Cao

M W F 10:50 AM - 12:05 PM

Smith College
SDS-192-01-202301

Stoddard G2

scao53@smith.edu
An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. SDS 100 is required for students who have not previously completed SDS 201, 220, 290 or 291.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Lindsay Poirier

M W 9:25 AM - 10:40 AM

Smith College
SDS-192-02-202301

McConnell 404

lpoirier@smith.edu
An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. SDS 100 is required for students who have not previously completed SDS 201, 220, 290 or 291.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

03
4.00

Jared Joseph

W F 1:20 PM - 2:35 PM; M 1:40 PM - 2:55 PM

Smith College
SDS-192-03-202301

Stoddard G2

jjoseph34@smith.edu
An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. SDS 100 is required for students who have not previously completed SDS 201, 220, 290 or 291.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Ben Baumer

M W F 10:50 AM - 12:05 PM

Smith College
SDS-220-01-202301

Sabin-Reed 301

bbaumer@smith.edu
(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Corequisite: SDS 100 required for students who have not completed SDS 192, 201, 290 or 291. Prerequisite: MTH 111 or the equivalent, or permission of the instructor.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Kaitlyn Cook

M 1:40 PM - 2:55 PM; W F 1:20 PM - 2:35 PM

Smith College
SDS-220-02-202301

Sabin-Reed 301

kcook93@smith.edu
(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Corequisite: SDS 100 required for students who have not completed SDS 192, 201, 290 or 291. Prerequisite: MTH 111 or the equivalent, or permission of the instructor.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

TU TH 10:50 AM - 12:05 PM

Smith College
SDS-290-01-202301

Seelye 301

(Formerly MTH/SDS 290). A survey of statistical methods needed for scientific research, including planning data collection and data analyses that provide evidence about a research hypothesis. The course can include coverage of analyses of variance, interactions, contrasts, multiple comparisons, multiple regression, factor analysis, causal inference for observational and randomized studies and graphical methods for displaying data. Special attention is given to analysis of data from student projects such as theses and special studies. Statistical software is used for data analysis. Prerequisites: One of the following: PSY 201, SDS 201, GOV 190, ECO 220, SDS 220 or a score of 4 or 5 on the AP Statistics examination or the equivalent. Corequisite: SDS 100 required for students who have not completed SDS 192, 201, 220 or 291. Enrollment limited to 38.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Katherine M. Kinnaird

M W F 9:25 AM - 10:40 AM

Smith College
SDS-293-01-202301

Sabin-Reed 301

kkinnaird@smith.edu
In the era of “big data,” statistical models are becoming increasingly sophisticated. This course begins with linear regression models and introduces students to a variety of techniques for learning from data, as well as principled methods for assessing and comparing models. Topics include bias-variance trade-off, resampling and cross-validation, linear model selection and regularization, classification and regression trees, bagging, boosting, random forests, support vector machines, generalized additive models, principal component analysis, unsupervised learning and k-means clustering. Emphasis is placed on statistical computing in a high-level language (e.g. R or Python). Prerequisites: SDS 291 & MTH 211 (may be concurrent).
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

Contact Us

Amherst College Certificate Advisor:

Tanya Leise, Brian Boyle Professor in Mathematics and Computer Science, Amherst College

Hampshire College Certificate Advisor:

Sarah HewsAssociate Professor of Mathematics, Hampshire College & Visiting Assistant Professor of Mathematics & Statistics, Amherst College (on leave 2021-22)

Mount Holyoke College Certificate Advisor:

Martha HoopesProfessor of Biological Sciences, Mount Holyoke College

Smith College Certificate Advisor:

Christophe GoléProfessor of Mathematics & Statistics, Smith College

Five College Staff Liaison:

Ray Rennard, Director of Academic Programs