Courses

Whether you’re passionate about the sciences, social sciences or humanities, data science can deepen your understanding of the world and broaden your horizons. We encourage you to explore our Data Science offerings during your time at Williams.

Data Science Courses

STAT 101 (F, S) LEC Elementary Statistics and Data Analysis

It is impossible to be an informed citizen in today’s world without an understanding of data. Whether it is opinion polls, unemployment rates, salary disparities, the efficacy of vaccines, etc., we need to be able to interpret and gain information from statistics. This course will introduce the common methods used to analyze and present data with an emphasis on interpretation and informed decision making.

Taught by: Benjamin Bradbury Seiler | Catalog details

CSCI 104 (F, S) LEC Data Science and Computing for All

Many of the world’s greatest discoveries and most consequential decisions are enabled or informed by the analysis of data from a myriad of sources. Indeed, the ability to wrangle, visualize, and draw conclusions from data is now a critical tool in the sciences, business, medicine, politics, other academic disciplines, and society as a whole. This course lays the foundations for quantifying relationships in data by exploring complementary computational, statistical, and visualization concepts. These concepts will be reinforced by lab experiences designed to teach programming and statistics skills while analyzing real-world data sets. This course will also examine the broader context and social issues surrounding data analysis, including privacy and ethics.

Taught by: Stephen Freund | Catalog details

CSCI 134 (F, S) LEC Introduction to Computer Science

This course introduces students to the science of computation by exploring the representation and manipulation of data and algorithms. We organize and transform information in order to solve problems using algorithms written in a modern object-oriented language. Topics include organization of data using objects and classes, and the description of processes using conditional control, iteration, methods and classes. We also begin the study of abstraction, self-reference, reuse, and performance analysis. While the choice of programming language and application area will vary in different offerings, the skills students develop will transfer equally well to more advanced study in many areas. In particular, this course is designed to provide the programming skills needed for further study in computer science and is expected to satisfy introductory programming requirements in other departments.

Taught by: Rohit Bhattacharya, Jeannie R Albrecht, Lucas Anton Rosenblatt | Catalog details

CSCI 136 (F, S) LEC Data Structures and Advanced Programming

This course builds on the programming skills acquired in Computer Science 134. It couples work on program design, analysis, and verification with an introduction to data structures. The study of data structures captures efficient methods in which to store and manipulate data, and they are important in the construction of high-performance computer programs. The course introduces the most commonly used data structures: files, lists, stacks, queues, trees, hash tables, and graphs. Students will be expected to write many programs: small programs at first, culminating in more elaborate systems by the end of the semester. Emphasis will be placed on the development of clear, modular programs that are easy to read, debug, verify, analyze, and modify.

Taught by: James Bern, Daniel Barowy | Catalog details

INTR 150/AMST 150/SOC 150/STS 150/WGSS 150 (F, S) LEC Data for Justice

This course is a unique and inclusive introduction to data science where quantitative thinking, programming, and social justice intertwine. We will build our data science skills using R, a popular open-source data science tool. We will focus on essential stages of data analysis, including data acquisition, cleaning, wrangling, visualization, and exploration. But rather than divorcing these techniques from the social issues they can help illuminate, we ground them in a social justice context. Overall, we will apply data science skills to topics drawn from criminal justice, environmental justice, diversity and inclusion in arts and media, education equity, and much more, with the goal of growing our collective capacity to use data science as a tool for social good. During a time when humans are increasingly subjugated to data-driven algorithmic decisions, when there are social media accounts dedicated to highlighting misuses of data, and when artificial intelligence makes faking data a nearly trivial task, using data to ethically and carefully promote justice is more important than ever.

Taught by: Chad Topaz | Catalog details

STAT 161 (F, S) LEC Introductory Statistics for Social Science

This course will cover the basics of modern statistical analysis with a view toward applications in the social sciences. Topics include exploratory data analysis, linear regression, basic statistical inference, and elements of probability theory. The course focuses on the application of statistical tools to solve problems, to make decisions, and the use of statistical thinking to understand the world.

Taught by: Duncan Clark, Anna Plantinga | Catalog details

NSCI 201/BIOL 212/PSYC 212 (F) LEC Neuroscience

This course is designed to give an overview of the field of neuroscience progressing from a molecular level onwards to individual neurons, neural circuits, and ultimately regulated output behaviors of the nervous system. Topics include a survey of the structure and function of the nervous system, basic neurophysiology and neurochemistry, learning and memory, sensory and motor systems, and clinical disorders. Throughout the course, many examples from current research in neuroscience are used to illustrate the concepts being considered. The lab portion of the course will emphasize a) practical hands-on exercises that amplify the material presented in class; b) interpreting and analyzing data; c) presenting the results in written form and placing them in the context of published work; and d) reading and critiquing scientific papers.

Taught by: Matt Carter, Victor Cazares | Catalog details

PSYC 201 (F, S) LEC Experimentation and Statistics

An introduction to the basic principles of research in psychology. We focus on how to design and execute experiments, analyze and interpret results, and write research reports. Students conduct a series of research studies in different areas of psychology that illustrate basic designs and methods of analysis. You must register for lab and lecture with the same instructor.

Taught by: Kris Kirby, Eliza L Congdon, Kenneth Savitsky, Amie Hane, Noah Sandstrom | Catalog details

STAT 201 (F, S) LEC Statistics and Data Analysis

Statistics can be viewed as the art and science of turning data into information. Real world decision-making, whether in business or science, is often based on data and the perceived information it contains. Sherlock Holmes, when prematurely asked the merits of a case by Dr. Watson, snapped back, “Data, data, data! I can’t make bricks without clay.” In this course, we will study the basic methods by which statisticians attempt to extract information from data. These will include many of the standard tools of statistical inference such as hypothesis testing, confidence intervals, and linear regression as well as exploratory and graphical data analysis techniques. This is an accelerated introductory statistics course that involves computational programming and incorporates modern statistical techniques.

Taught by: Shaoyang Ning | Catalog details

STAT 202 (F, S) LEC Introduction to Statistical Modeling

Data come from a variety of sources: sometimes from planned experiments or designed surveys, sometimes by less organized means. In this course we’ll explore the kinds of models and predictions that we can make from both kinds of data, as well as design aspects of collecting data. We’ll focus on model building, especially multiple regression, and talk about its potential to answer questions about the world — and about its limitations. We’ll emphasize applications over theory and analyze real data sets throughout the course.

Taught by: Anna Neufeld | Catalog details

ASTR 211 LEC Astronomical Observing and Data Analysis

Last offered Fall 2025

How do astronomers make scientific measurements for objects that are light-years away from Earth? This course will introduce the basics of telescopes and observations and will give students hands-on training in the techniques astronomers use to obtain, process, and analyze scientific data. We will discuss observation planning, CCD detectors, signal statistics, image processing, and photometric and spectroscopic observations. We will begin by focusing on ground-based optical observations and will move on to non-optical observations, both electromagnetic (e.g., radio waves, X-rays) and non-electromagnetic (e.g., gravitational waves, neutrinos). Throughout the course, students will use computational techniques to work with real astronomical data, taken with our 24″ telescope and from data archives.

Taught by: Anne Jaskot | Catalog details

GEOS 214/ENVI 214 (F) LEC Mastering GIS

The development of Geographic Information Systems (GIS) has allowed us to investigate incredibly large and spatially complex data sets like never before. From assessing the effects of climate change on alpine glaciers, to identifying ideal habitat ranges for critically endangered species, to determining the vulnerability of coastal communities to storms, GIS has opened the door for important, large-scale environmental analyses. And as these technologies improve, our ability to understand the world grows ever greater. This course will teach you how to use GIS to investigate environmental problems. We will review fundamental principles in geography, the construction and visualization of geospatial datasets, and tools for analyzing geospatial data. Special attention will also be given to analysis of remotely sensed (satellite) imagery. By the end of the course, you will be able to conduct independent GIS-based research and produce maps and other geospatial imagery of professional quality.

Taught by: Alex Apotsos | Catalog details

PHIL 239/STS 239 LEC The Ethics of Artificial Intelligence

Last offered Spring 2025

Human beings will someday live alongside artificially intelligent beings who equal or exceed us. The rise of AI will be a tectonic shift for culture, technology, and our fundamental sense of ourselves. When AI is fully realized, it is likely to be amongst the most important things to happen to our species. Some challenges we face are broad and about the future. How can we ensure that AI’s will act morally? Is a world with AI’s overall better or worse for us? How do we create legal and policy frameworks that cover a new kind of thinking being? If they are conscious, will AI’s have dignity and rights? Other questions are pressing and immediate: Artificial intelligence techniques are used today to help decide whether someone gets a bank loan, is eligible to be released on bail, or in need of particular medical treatment. And right now there are autonomous vehicles deciding how to behave in traffic, and autonomous weapons capable of delivering lethal force. Is it moral for us to pass along these sorts of decisions to AI’s? What if they are biased, unbeknownst to us? What if they are more fair? How should we understand intellectual and creative work in an era of generative models that take on some aspects of thought? In this course we will engage ethical questions surrounding the seeming inevitability of AI.

Taught by: Joseph Cruz | Catalog details

STAT 250 (S) LEC Statistics Foundations: Computation and Communication

Computation and communication are essential for modern statistical practice. In this course students will learn to tidy, visualize, and model data with an emphasis on communication, reproducibility, and ethical responsibility in statistical analysis. Students will engage deeply with statistical computation to work with complex data sources; implement randomization-based statistical tests; design simulation studies to empirically explore theoretical concepts such as sampling variability, model misspecification, and robustness; and train and evaluate statistical/machine learning models. In the process, we will pay attention to best practices for coding (e.g., writing functions, documenting code, using version control) and reproducible research methods (via R Markdown and GitHub). Students will practice combining the collaborative, technical, and communication aspects of data science and statistics throughout the semester by completing data-oriented projects and producing formal written analysis reports. This course counts as the computing requirement for the Statistics major.

Taught by: Anna Plantinga | Catalog details

POEC 253/PSCI 293 (F) LEC Empirical Methods in Political Economy

This course introduces students to common empirical tools used in policy analysis and implementation. Students will develop skills in statistical literacy to become critical consumers of public policy-relevant research. Through hands-on work with data and critical assessment of existing empirical social scientific research, students will develop the ability to choose and employ the appropriate tool for a particular research problem, and to understand the limitations of the techniques. Topics to be covered include basic principles of probability; statistical inference and hypothesis testing; and multiple regression analysis. A particular focus will be placed on understanding causality, the challenges of estimating causal relationships, and the design of evidence-based policy.

Taught by: Anand Swamy | Catalog details

ECON 255 (F, S) LEC Econometrics

An introduction to the theory and practice of applied quantitative economic analysis. This course familiarizes students with the strengths and weaknesses of the basic empirical methods used by economists to evaluate economic theory against economic data. Emphasizes both the statistical foundations of regression techniques and the practical application of those techniques in empirical research, with a focus on understanding when a causal interpretation is warranted. Computer exercises will provide experience in using the empirical methods, but no previous computer experience is expected. Highly recommended for students considering graduate training in economics or public policy.

Taught by: Owen Ozier, Matthew Gibson, Steven Lee, Pamela Jakiela | Catalog details

GEOS 255/CAOS 255 LEC Environmental Observation

Last offered Fall 2024

To study the environment, we need to observe and measure it. We collect data–numbers that represent system states–and analyze them to create understanding of the world we live in. Advances in technology create more opportunities to discover how the planet works. Through a survey of observational approaches (including weather stations, direct sampling, remote sensing, community-based monitoring, and other techniques), this course will investigate the process of turning a physical property in the environment into a number on a computer and then into meaningful information. We will explore both direct field measurements and remote sensing techniques, diving into how to choose the appropriate sensor for a scientific question, how sensors work, analysis approaches and statistical methods, and how to interpret the resulting data. We will also learn how to mitigate measurement bias through a combination of lab experiments and field work and how to make interpretations of measurements that accurately reflect what is being measured. The course will focus on the near-surface environment, including the atmosphere, water, and biosphere. Students will carry out a research project using observation techniques covered in class to explore a scientific question of interest. This course is in the Oceans and Climate group for the Geosciences major.

Taught by: Alice Bradley | Catalog details

CSCI 270 (F, S) LEC Foundations of Artificial Intelligence

Computer science has increasingly set its sights on problems with no obvious prescriptive solution, such as image classification, natural language understanding, and game playing. As it is infeasible to prescriptively identify a cat from a set of pixels, or a winning move from the state of a chess board, tools from the traditional computer science canon of algorithms and system building are often insufficient. Artificial intelligence (AI) techniques have increasingly been leveraged to fill this gap. Rather than explicitly specifying how to solve a task, AI techniques typically take an indirect approach: first describing the task using a standardized representation (e.g., labeled data for supervised machine learning and state spaces for heuristic search), and then employing general-purpose algorithms that operate on the task description. The goal of this course is to introduce the theoretical and practical foundations that will enable students to add AI methodologies to their computational toolbox. It provides the fundamentals for more advanced study of artificial intelligence and machine learning.

Taught by: Mark Hopkins, Nate Chambers | Catalog details

GEOS 290/ENVI 290 LEC Data Analysis in Earth Science

Last offered Spring 2026

As Earth and environmental sciences are increasingly data-driven areas of study, quantitative and data analysis skills are core to the field. This class provides a survey of quantitative and data analysis skills necessary for advanced work in Earth and environmental sciences. The course introduces the mathematical concepts required for earth system modeling, including calculus, differential equations, and linear algebra, with a focus on how these tools are used to better understand earth processes. We provide an introduction to statistical methods used in time series and spatial data analysis, along with data visualization, dimensional analysis, and uncertainty/error analysis. We introduce basic skills in Geographic Information Systems (GIS) software for making maps and geospatial analysis. The class also provides an introduction to scientific programming in Matlab and R. This class will focus on climate-related datasets throughout the term.

Taught by: Alice Bradley | Catalog details

PSYC 312/NSCI 322 (S) SEM From Order to Disorder(s): The Role of Genes & the Environment in Psychopathology

This course examines how experimental methods in neuroscience can be used to understand the role of nature (genes) and nurture (the environment) in shaping the brain and behavior. In particular, we will explore how neuroscience informs our understanding of psychiatric disorders such as anxiety, depression, and schizophrenia. We will investigate the biological underpinning of these disorders as well as their treatments. Readings will include human studies as well as work based on animal models. Topics will include: the ways in which environmental and genetic factors shape risk and resiliency in the context of psychiatric disease, the neural circuits and peripheral systems that contribute to psychopathology, and the mechanisms through which interventions may act. In the laboratory component of the course, students will gain hands-on experience in using animal models to study complex behavior and their associated neural mechanisms.

Taught by: Victor Cazares | Catalog details

BIOL 314/NSCI 324 LEC Neuroethology

Last offered Spring 2026

How does an animal experience its environment? What mechanisms allow an animal to select and generate behaviors? In this course we will use a comparative approach to examine how nervous systems have evolved to solve problems inherent to an animal’s natural environment. We will discuss how animals sense physical and chemical properties of their surroundings and convert this information to a signal encoded in their brain. We will explore how nervous systems of diverse species are adapted to extract sensory information that is relevant to their survival–such as sound, light, and smell. We will also examine how neural circuits control muscles to generate motor behaviors such as locomotion and vocalization and how sensory information is integrated to influence behavior. To highlight the discovery process, we will read and discuss primary research articles that complement course content. During labs we will use a variety of approaches such as electrophysiology, optogenetics, behavior, and data analysis to understand sensory and motor systems in several different organisms.

Taught by: Charlotte Barkan | Catalog details

PHYS 315/CSCI 315/BIOL 351 (S) LEC Computational Biology

This course will provide an overview of Computational Biology, the application of computational, mathematical, statistical, and physical problem-solving techniques to interpret the rapidly expanding amount of biological data. Topics covered will include database searching, DNA sequence alignment, clustering, RNA structure prediction, protein structural alignment, methods of analyzing gene expression, networks, and genome assembly using techniques such as string matching, dynamic programming, hidden Markov models, and statistics.

Taught by: Daniel Aalberts | Catalog details

CSCI 317/COGS 317/NSCI 317 (S) LEC Foundations of Computational Neuroscience

How does the brain process information? Despite the continuous scientific pursuits to understand the brain, many questions about brain function remain unanswered. In this course, we take an interdisciplinary, hands-on approach to understanding the brain, focusing on how neural systems encode, transmit, and decode information. Students will learn foundational techniques in computational neuroscience as it pertains to simulating neuronal dynamics with canonical models such as the integrate-and-fire, Hodgkin-Huxley, and Wilson-Cowan equations, performing statistical analysis of neurological data, and examining biological neural networks and their parallels to artificial intelligence.

Taught by: TBA | Catalog details

BIOL 319/CHEM 319/CSCI 319/MATH 319/PHYS 319 LEC Integrative Bioinformatics, Genomics, and Proteomics Lab

Last offered Fall 2025

What can computational biology teach us about cancer? In this lab-intensive experience for the Genomics, Proteomics, and Bioinformatics program, computational analysis and wet-lab investigations will inform each other, as students majoring in biology, chemistry, computer science, mathematics/statistics, and physics contribute their own expertise to explore how ever-growing gene and protein data-sets can provide key insights into human disease. In this course, we will take advantage of one well-studied system, the highly conserved Ras-related family of proteins, which play a central role in numerous fundamental processes within the cell. The course will integrate bioinformatics and molecular biology, using database searching, alignments and pattern matching, and phylogenetics to reconstruct the evolution of gene families by focusing on the gene duplication events and gene rearrangements that have occurred over the course of eukaryotic speciation. By utilizing high through-put approaches to investigate genes involved in the inflammatory and MAPK signal transduction pathways in human colon cancer cell lines, students will uncover regulatory mechanisms that are aberrantly altered by siRNA knockdown of putative regulatory components. This functional genomic strategy will be coupled with independent projects using phosphorylation-state specific antisera to test our hypotheses. Proteomic analysis will introduce the students to de novo structural prediction and threading algorithms, as well as data-mining approaches and Bayesian modeling of protein network dynamics in single cells. Flow cytometry and mass spectrometry may also be used to study networks of interacting proteins in colon tumor cells.

Taught by: Lois Banta | Catalog details

STAT 335 (F) LEC Introduction to Biostatistics and Epidemiology

Epidemiology is the study of disease and disability in human populations, while biostatistics focuses on the development and application of statistical methods to address questions that arise in medicine, public health, or biology. This course will begin with epidemiological study designs and core concepts in epidemiology, followed by key statistical methods in public health research. Topics will include multiple regression, analysis of categorical data (two sample methods, sets of 2×2 tables, RxC tables, and logistic regression), survival analysis (Cox proportional hazards model), and if time permits, a brief introduction to regression with correlated data.

Taught by: Anna Neufeld | Catalog details

STAT 344 LEC Statistical Design of Experiments

Last offered Spring 2026

When you hear the word experiment you might be picturing white lab coats and pipettes, but businesses, especially e-commerce, are constantly experimenting as well. How do you get the most out of both scientific and business investigations? By doing the right experiment in the first place. We’ll explore the techniques used to plan experiments that are both efficient and statistically sound. We’ll learn how to analyze the data that come from these experiments and the conclusions we can draw from that analysis. We’ll look at both classical tools like fractional factorial designs as well as optimal design, and see how these two frameworks differ in their philosophy and in what they can do. Throughout the course, we’ll make extensive use of both R and JMP software to work with real-world data.

Taught by: Richard De Veaux | Catalog details

STAT 345 (S) LEC Applied Causal Inference

Association is NOT causality, a slogan from introductory statistics that we all know too well. Natural, the next question is: when can we actually draw causal conclusions from data, and how? This course introduces the fundamental concepts and modern methods of causal inference, with an emphasis on real-world applications. Moving beyond association and prediction, students will learn how to rigorously define, identify, estimate, and interpret causal effects from randomized experiments and observational studies. Through hands-on analysis of datasets in R, students develop practical skills to apply causal thinking and analysis to problems in public health, economics, and the social sciences.

Taught by: Shaoyang Ning | Catalog details

INTR 350/SOC 351/AMST 363/STS 363/WGSS 363 (F, S) SEM Data for Justice Research Practicum

Civil rights activist, educator, and investigative journalist Ida B. Wells said that “the way to right wrongs is to shine the light of truth upon them.” In this inclusive, collaborative, research-based course, students will bring statistical, computational, and/or mathematical approaches to bear on issues of social justice. Guided closely by the instructor, students will work in groups to carry out original research in an area such as criminal justice, education equity, environmental justice, health care equity, economic justice, or inclusion in arts/media. Prior research experience is not required; one goal of this course is to build skills for advanced research. Students must contact the instructor prior to preregistration to fill out an interest form.

Taught by: Chad Topaz | Catalog details

STAT 355 LEC Multivariate Statistical Analysis

Last offered Fall 2024

To better understand complex processes, we study how variables are related to one another and how they work in combination. In addition, we want to make inferences about more than one variable at a time. Elementary statistical methods might not apply. In this course, we study the tools and the intuition that are necessary to analyze and describe such datasets with more than multiple variables. Topics covered will include data visualization techniques for data sets with more variables, clustering algorithms, parametric and non-parametric techniques to estimate joint distributions, techniques for combining variables, performing dimension reduction, and making inferences.

Taught by: Xizhen Cai | Catalog details

STAT 356 LEC Time Series Analysis

Last offered Spring 2026

We encounter time series data in settings from astronomy to engineering to finance and beyond, but our traditional statistical toolbox is often inadequate in the face of time-dependent observations. This course will introduce a range of specialized statistical and machine learning methods for exploring, modeling, and forecasting time series data. There will be a strong emphasis on application, as we use these techniques on a variety of real-world data using R.

Taught by: Benjamin Bradbury Seiler | Catalog details

CHEM 368 TUT Computational Physical Chemistry

Last offered Spring 2026

This tutorial provides an introduction to the principles of computational methods and their applications to problems of chemical interest, such as chemical dynamics, statistical mechanics, chemical bonding, chemical reactivity, and molecular spectroscopy. Emphasis is placed upon modern calculations, their fundamentals, practical considerations, interpretation, and applications to current research questions. Under the guidance in sessions and through independent work, students will use computational methods to explore assigned weekly research problems. The research results will be presented to and discussed with the tutorial partner at the end of each week.

Taught by: Enrique Peacock-López | Catalog details

ECON 370 (F) LEC Data Science for Economic Analysis

This course provides a hands-on introduction to data science tools most relevant for economic analysis including data visualization, machine learning, and text analysis. Economists and other social scientists tend to use these data science tools differently than many researchers in statistics and computer science – conducting empirical analysis that is explicitly grounded in economic theory, and focusing on causal inference rather than prediction. Through a combination of lectures, hands-on labs, and group projects, students will develop the theoretical and practical skills needed to analyze economic data using modern data science techniques in both R and Python.

Taught by: Pamela Jakiela | Catalog details

ECON 371 (F) SEM Time Series Econometrics and Empirical Methods for Macro

Econometric methods in many fields including macro and monetary economics, finance and international growth and development, as well as numerous fields beyond economics, have evolved a distinct set of techniques which are designed to meet the practical challenges posed by the typical empirical questions and available time series data of these fields. The course will begin with an introductory review of concepts of estimation and inference for large data samples in the context of the challenges of multivariate endogenous systems, and will then focus on associated methods for analysis of short run dynamics such as vector autoregressive techniques and methods for analysis of long run dynamics such as cointegration techniques. Students will be introduced to concepts and techniques analytically, but also by intuition, learning by doing, and by computer simulation and illustration. The course is particularly well suited for economics majors wishing to explore advanced empirical methods, or for statistics, mathematics or computer science majors wishing to learn more about the ways in which the subject of their majors interacts with the field of economics. The method of evaluation will include a term paper. ECON 252 and either STATS 346 or ECON 255 are formal prerequisites, although for students with exceptionally strong math/stats backgrounds these can be waived subject to instructor permission. Students who complete this course will also be permitted to enroll in Econ 471 (a follow up senior seminar course) during the spring semester if they are interested.

Taught by: Peter Pedroni | Catalog details

CSCI 374 LEC Machine Learning

Last offered Spring 2025

Machine learning is a field that derives from artificial intelligence and statistics, and is concerned with the design and analysis of computer algorithms that “learn” automatically through the use of data. Computer algorithms are capable of discerning subtle patterns and structure in the data that would be practically impossible for a human to find. As a result, real-world decisions, such as treatment options and loan approvals, are being increasingly automated based on predictions or factual knowledge derived from such algorithms. This course explores topics in supervised learning (e.g., random forests and neural networks), unsupervised learning (e.g., k-means clustering and expectation maximization), and possibly reinforcement learning (e.g., Q-learning and temporal difference learning.) It will also introduce methods for the evaluation of learning algorithms (with an emphasis on analysis of generalizability and robustness of the algorithms to distribution/environmental shift), as well as topics in computational learning theory and ethics.

Taught by: Rohit Bhattacharya | Catalog details

CSCI 375 (F, S) LEC Natural Language Processing

Natural language processing (NLP) is a set of methods for making human language accessible to computers. NLP underlies many technologies we use on a daily basis including automatic machine translation, search engines, email spam detection, and automated personalized assistants. These methods draw from a combination of algorithms, linguistics and statistics. This course will provide a foundation in building NLP models to classify, generate, and learn from text data.

Taught by: Nate Chambers, Katie Keith | Catalog details

CSCI 379 (S) LEC Causal Inference

Does X cause Y? If so, how? And what is the strength of this causal relation? Seeking answers to such causal (as opposed to associational) questions is a fundamental human endeavor; the answers we find can be used to support decision-making in various settings such as healthcare and public policy. But how does one tease apart causation from association–early in our statistical education we are taught that “correlation does not imply causation.” In this course, we will re-examine this phrase and learn how to reason with confidence about the validity of causal conclusions drawn from messy real-world data. We will cover core topics in causal inference including causal graphical models, unsupervised learning of the structure of these models, expression of causal quantities as functions of observed data, and robust/efficient estimation of these quantities using statistical and machine learning methods. Concepts in the course will be contextualized via regular case studies.

Taught by: Rohit Bhattacharya | Catalog details

CSCI 381 (S) LEC Deep Learning

This course is an introduction to deep neural networks and how to train them. Beginning with the fundamentals of regression and optimization, the course then surveys a variety of neural network architectures, which may include multilayer feedforward neural networks, convolutional neural networks, recurrent neural networks, and transformer networks. Students will also learn how to use deep learning software such as PyTorch or Tensorflow.

Taught by: Mark Hopkins | Catalog details

ECON 381/ECON 571 (S) LEC Global Health Policy Challenges

Poor health is both a cause and a consequence of poverty. It can trap individuals in poverty and reduce aggregate economic growth. This course will be structured around major global health challenges, including maternal health, infectious diseases (e.g. HIV/AIDS, tuberculosis, COVID), neglected tropical diseases (e.g malaria, dengue, Ebola), nutritional deficiencies, and mental health. We will focus primarily, but not exclusively, on health in low-income countries in this course. Students will read papers and conducted empirical assignments related to the various topics, as well as develop their own research idea during the semester related to one of the topics covered.

Taught by: Susan Godlonton | Catalog details

CSCI 382 (F, S) LEC Responsible Artificial Intelligence

Responsible Machine Learning/Artificial Intelligence is the science and practice of designing algorithms and deploying AI systems in ways that are socially sustainable. This course introduces students to core technical and socio-technical objectives in responsible AI, with an emphasis on algorithmic fairness, transparency and interpretability, (differential) data privacy, and bias/risk factors for LLMs. This course is socio-technically grounded. We study how and why machine learning systems can produce harmful or inequitable outcomes, and how different stakeholders (e.g., impacted communities, developers, institutions, regulators) define “responsible” in different, sometimes conflicting, ways. Students learn to evaluate these trade-offs, implement mitigation techniques, and communicate technical results clearly. The course will include both conceptual frameworks (ethics, governance, legal and policy constraints) and technical/algorithmic solutions (models with fairness/privacy constraints, explainability toolkits, etc.). Students work in Python to analyze datasets and models, apply bias mitigation methods, generate explanations for model decisions, and explore privacy-preserving data releases. Students will also be asked to work through a limited number of problems that require light proofs and pre-requisite knowledge of probability/statistics to deepen their understanding. Students will also be assessed on their ability to present their work coherently and demonstrate understanding of the material.

Taught by: Lucas Anton Rosenblatt | Catalog details

BIOL 437/NSCI 337 SEM Neural Flexibility: plasticity, modulation and evolution

Last offered Fall 2025

Animals must adapt their behaviors to match their environment in order to survive and reproduce. How does the nervous system mediate behavioral change that occurs in seconds, hours, months, or millions of years? In this course we will use a comparative approach to explore how neural circuits control behavioral flexibility over a range of timescales. We will first discuss circuits that control behavioral switches that occur very rapidly based on environmental and social stimuli. Next, we will consider the role that internal state and identity play in modulating neuronal circuits over an organism’s lifetime to influence behavioral decisions. Finally, we will examine how evolution tinkers with neural circuits to lead to behavioral change over very long timescales. Throughout the course we will explore how modifications to neural circuits–including connectivity, synaptic plasticity, neuromodulation and neuron physiology–can lead to differences in behavior and ask if there are connections between common mechanisms underlying behavioral change across timescales. Discussions and assignments in this course will focus on reading and critically evaluating primary scientific literature.

Taught by: TBA | Catalog details

STAT 442 (S) LEC Statistical Learning and Data Mining

In both science and industry today, the ability to collect and store data can outpace our ability to analyze it. Traditional techniques in statistics are often unable to cope with the size and complexity of today’s data bases and data warehouses. New methodologies in Statistics have recently been developed, designed to address these inadequacies, emphasizing visualization, exploration and empirical model building at the expense of traditional hypothesis testing. In this course we will examine these new techniques and apply them to a variety of real data sets.

Taught by: Benjamin Bradbury Seiler | Catalog details

ECON 460 SEM Women, Work, and the World Economy from 5,000 BC to the Present

Last offered Fall 2024

Now and throughout history, views of the appropriate role for women in society have varied tremendously across cultures and communities: are women autonomous productive agents, are they men’s property, or do they fall somewhere in between? In this course, we explore the causes and consequences of women’s position in society for growth and economic development, analyzing women’s economic roles in historical and cultural perspective. Students will become more critical readers of current economic literature, and will apply their skills in conducting empirical research.

Taught by: Pamela Jakiela | Catalog details

ECON 462 SEM Topics in African Development

Last offered Spring 2023

This course will examine a selection of current issues in development economics with a specific emphasis on how they relate to Sub-Saharan Africa. Core topics to be addressed include agriculture, labor markets with a specific emphasis on south-south migration, credit, and land markets. Some specific questions that may be addressed include: How has agricultural productivity changed over time? What are constraints to improving agricultural productivity? What drives south-south migration? What are the impacts of migration on destination and origin communities? Students will critically read published journal articles (or working papers) and actively participate in class discussions. Students will also complete original empirical analysis on a related topic.

Taught by: Susan Godlonton | Catalog details

ECON 474/ECON 524 (S) SEM Advanced Microeconometrics

Estimation and inference problems in applied research sometimes demand more advanced statistical methods than the workhorse options that are covered in intermediate econometrics and which are widely available in statistical packages. Building on a basic understanding of econometrics and statistics, this methodology course will take students through several applied microeconometric techniques. Students will be expected to use statistical software throughout, as we simulate data and use real-world datasets to explore the inner workings of several modern methods. We will consider inference situations where groups of observations are statistically related to one another; compare small-sample and bootstrap methods to asymptotic approaches; explore well-known special cases of instrumental variables estimation; set up our own maximum likelihood estimators; and more. This course may be most useful to those considering graduate study in economics or careers in data science.

Taught by: Owen Ozier | Catalog details

ECON 477/ENVI 376/CAOS 477 (S) SEM Economics of Environmental Behavior

A community maintains a fishery; a firm decides whether to get a green certification; you choose to fly home or stay here for spring break: behaviors of people and firms determine our impact on the environment. We’ll use economics to model environmental behavior and to assess how policies can help or hurt the environment. Topics we may study include: common pool resources, voluntary conservation, social norms and nudges, discrimination and justice, rationality, firm responses to mandatory and voluntary regulation, voting and public opinion, and international environmental agreements. We’ll also build familiarity with the main methodologies of modern economic research: theoretical modeling, empirical analysis of observational data, and experiments.

Taught by: Sarah Jacobson | Catalog details

ECON 523/ECON 379 (S) SEM Program Evaluation for International Development

Causal inference techniques that can be used to evaluate the effects of social policies are at the core of modern empirical microeconomics. These tools are increasingly relevant to development organizations, which face strict competition for scarce resources. Both public and private organizations are moving toward using rigorous program evaluation techniques to justify funding for their programs and to design more effective interventions. This course is an introduction to evaluation methodology and the tools available to development practitioners and policymakers, drawing on examples from developing countries. It will cover a wide range of experimental and quasi-experimental evaluation techniques including randomized trials, difference-in-differences, instrumental variables, and regression discontinuity. The course is a mix of applied econometrics and practical applications covering implementation, analysis, and interpretation.

Taught by: Pamela Jakiela | Catalog details