Carnegie Mellon
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Follow your curiosity / Undergraduate study
Explore Carnegie Mellon, Bowdoin, Amherst, MIT, and Northeastern through their courses and the paths that connect them.
140 selected courses · 36 program pathways · 7 focus areas and more
Official catalogs: CMU ↗ · Bowdoin ↗ · Amherst ↗ · MIT ↗ · Northeastern ↗
Counts include all your likes, regardless of the filters below. Majors and minors are counted separately. This is your shortlist from a selected collection, not a ranking of the schools’ full catalogs.
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34 courses in this collection
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22 courses in this collection
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28 courses in this collection
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0 courses liked
28 courses in this collection
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0 courses liked
28 courses in this collection
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140 results
This is a selected collection, with full department catalogs below. A catalog listing does not guarantee a class will run or have open seats.
15-112
Learn Python through program design, debugging, and testing; a rigorous introduction for new programmers.
Begin with Python fundamentals, then practice breaking problems into smaller pieces and producing reliable code. The emphasis is on design, testing, and debugging, with applications ranging from scripts to web programs. No programming background is assumed, but the pace is rigorous.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
Read the official course entry ↗Catalog listing · check current schedule
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CSCI 1101
Start programming and computational problem-solving through applications and weekly labs.
Explore computing through practical problem-solving and regular programming labs. The course introduces applications beyond computer science and considers computing’s place in society. It assumes no programming experience; students with experience may be placed into a different introductory course.
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COSC 111
Learn programming, algorithmic thinking, and structured problem-solving, with a weekly laboratory.
Practice breaking problems into procedures and organizing programs so they are understandable and reusable. Programming exercises and a weekly laboratory make abstract ideas concrete. This is a foundation for later courses in algorithms, data structures, and software design.
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Read the official course entry ↗Fall and spring 2026–27
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6.1000
Learn Python and computational modeling through simulations, optimization, and statistical examples.
Build programs using mutable data, functions, objects, and common libraries, while learning to reason about computational cost. The catalog recommends some prior programming exposure; complete beginners are encouraged to consider the two-term 6.100A/6.100B sequence or 16.C20 instead.
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CS 2000
Learn to design programs that work with numbers, text, images, and complex data.
Start without prior coding experience and build toward analyzing and graphing realistic datasets. Learn professional programming tools and consider ethical questions around computing. The associated CS 2001 lab is a corequisite.
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15-122
Turn algorithms into correct programs, using C-based languages, verification, and fundamental data structures.
Move from knowing basic programming to understanding why a program is correct. You translate algorithms into implementations, work with foundational data structures, and learn verification techniques in a restricted version of C before moving to the full language. Prior introductory programming is required.
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CSCI 2200
Design efficient solutions for sorting, searching, and graph problems.
Learn how to design efficient algorithms and justify their performance. Sorting, searching, and graph problems introduce techniques such as divide-and-conquer, dynamic programming, and greedy choices. Analysis includes recurrences and amortization, building on prior data structures.
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COSC 211
Organize information using lists, dictionaries, sets, and graphs while analyzing program correctness and efficiency.
Compare ways to organize information and separate what a data structure promises from how it is implemented. Lists, stacks, queues, dictionaries, sets, and graphs provide examples. Advanced programming work is paired with reasoning about correctness and computational cost.
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Read the official course entry ↗Fall and spring 2026–27
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6.1020
Build reliable software using specifications, testing, abstraction, and careful design.
Study how teams make programs correct and maintainable: invariants, abstract data types, design patterns, immutable data, and concurrency. Weekly programming work leads into larger group projects. Prior programming is required; the official entry explains the prerequisite.
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CS 3000
Analyze algorithm correctness and efficiency, including graphs and optimization.
Study divide-and-conquer methods, asymptotic analysis, representations of information, compression, and computational complexity. Mathematical reasoning helps explain the tradeoffs between solutions. The course entry lists a CS 3001 lab corequisite and earlier computing preparation.
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07-280
Combine AI theory with implementations of influential systems, including language models and reinforcement learning.
Connect the foundations of AI—search, probability, machine learning, and reinforcement learning—with building modern intelligent systems. This is an integrated introduction to the reasoning and learning sides of AI, combining mathematical ideas with practical implementation.
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CSCI 2400
Explore search, knowledge representation, neural networks, and intelligent agents.
Investigate how an artificial agent can represent knowledge, search for solutions, and learn to act. Problems draw on heuristic search, logic, neural networks, and reinforcement learning. This is a computing-focused course that builds on the department’s intermediate core.
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COSC 241
Explore computational approaches to intelligent behavior and problem-solving.
Explore game-playing, heuristic search, and agents that learn from experience or data. The course connects theoretical reasoning with practical complications such as noisy sensors. It builds on Data Structures and offers a broad introduction to intelligent computational behavior.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
Read the official course entry ↗Fall 2026
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6.1210
Design algorithms, choose data structures, and analyze how efficiently they solve problems.
Translate computational problems into mathematical models, then compare possible solutions by their performance and behavior. This is a foundations course connecting programming with mathematical reasoning. Programming and relevant discrete mathematics or probability preparation are expected.
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CS 3200
Design databases and use SQL to organize, retrieve, and protect information.
Move from entity-relationship models and normalization to relational algebra, queries, indexing, and optimization. Explore transactions, concurrency, and recovery, and implement schemas and short applications. Prior programming preparation is required.
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15-388
Work through data collection, modeling, visualization, and a team project using practical computing tools.
Follow data through collection, cleaning, modeling, and presentation, including approaches for text, networks, and large datasets. Weekly programming work is complemented by an advanced-topic tutorial and a team application project. This is a practical opportunity to try an entire data-science workflow.
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CSCI 2410
Build predictive models and evaluate overfitting, regularization, and data leakage.
Train and evaluate models on real datasets through projects and experiments. Compare regression and classification with clustering and dimensionality reduction, while examining overfitting, regularization, and data leakage. The emphasis combines understanding a method with implementing it responsibly.
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COSC 247
Explore supervised, unsupervised, ensemble, and reinforcement learning methods.
Compare ways machines learn from labeled examples, unlabeled data, combined models, and feedback. Possible methods include decision trees, neural networks, support-vector machines, and Bayesian approaches. Data Structures is required; the catalog allows students to take both this and Artificial Intelligence.
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Read the official course entry ↗Spring 2027
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6.3900
Learn how machines make predictions and decisions through supervised, unsupervised, and reinforcement learning.
Approach learning as an optimization problem, including gradient-based methods and neural-network architectures. Mathematical ideas connect to practical algorithms. This is an undergraduate introduction with programming and mathematics prerequisites, rather than a first coding course.
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CS 4100
Explore how AI systems search, reason, handle uncertainty, and learn from data.
Connect classical AI approaches with data-centered techniques, using logic, probability, and mathematical models. Written and programming work develop understanding of the algorithms. Relevant mathematics and computer-science prerequisites apply.
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15-445
Explore how databases store information, execute queries, and keep transactions reliable.
Look underneath the database interface: how information is stored and indexed, how queries are optimized, and how transactions survive concurrency and failures. Case studies compare real systems and their tradeoffs. The catalog expects strong systems-programming skills and completion of 15-213.
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CSCI 3210
Study algorithms for strategic interactions, including markets, auctions, and social influence.
Ask how algorithms handle situations where participants act strategically. Markets, auctions, kidney exchanges, and social influence motivate questions about computation, incentives, and collective outcomes. Advanced algorithmic techniques include optimization and approximation; prior Algorithms is required.
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COSC 254
Implement algorithms for extracting information from large datasets, streams, and networks.
Implement algorithms in interactive notebooks and large-scale processing systems. Possible applications include recommendation systems, social networks, data streams, and time series. The focus is extracting useful information accurately and efficiently; the current curriculum marks this course not offered in 2026–27.
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Read the official course entry ↗Not offered in 2026–27
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6.1830
Build the software behind data analysis, from cleaning and databases to large-scale computation.
Work with data integration, relational and NoSQL systems, Spark, visualization, and scalable analysis. Extended programming assignments build toward a term project and paper. The catalog explicitly says this subject is not offered regularly, so check with the department before planning around it.
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Read the official course entry ↗Irregular offering · consult department
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DS 3000
Use linear algebra, probability, and numerical programming to understand AI algorithms.
Work with vectors, matrices, tensors, and probabilistic ideas while implementing algorithms from scratch. Written and coding assignments connect mathematical foundations with computation. This is the current 2026–27 title for DS 3000; older catalogs used different titles.
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10-301
Study learning algorithms mathematically and through programming experiments; the catalog recommends this version for non-SCS undergraduates.
Combine mathematical explanations of learning with experiments using tools such as Python and PyTorch. Topics span decision trees, neural networks, probabilistic methods, and reinforcement learning. The course expects prior programming, mathematics, and probability; it is the recommended introductory ML option for non-SCS undergraduates.
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CSCI 3465
Investigate financial prediction and portfolio models through programming-intensive machine-learning projects.
Use substantial programming projects to investigate financial predictions and investment allocation. Computational methods meet portfolio theory and market-efficiency questions, with attention to interpreting results and responsible use. Prior finance knowledge is not assumed, but the catalog requires the AI course.
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COSC 347
Investigate why learning algorithms work and the mathematical limits on what machines can learn.
Ask why learning algorithms work, what can be learned, and how much computation or data learning needs. Familiar methods become objects of rigorous mathematical study. The course expects prior AI or machine learning and comfort with proof-based mathematics.
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Read the official course entry ↗Fall 2026
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6.1200
Develop the discrete mathematics and proof techniques used throughout computer science.
Explore logic, sets, relations, graphs, induction, invariants, recurrences, and asymptotic growth. Counting, discrete probability, and number theory connect the mathematics to computing and cryptography. Cross-listed with 18.062; calculus preparation is required.
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DS 3500
Build robust data applications and pipelines that can handle real-world complexity.
Go beyond a one-off analysis to consider architecture, testing, scalability, data provenance, and maintainability. Work with APIs and large datasets while designing and implementing data-processing systems. Prior programming and data coursework is expected.
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21-127
Develop proof-writing tools through logic, sets, functions, induction, and number theory.
Learn to construct mathematical arguments rather than only calculate answers. Number theory, sets, relations, functions, and induction provide the material for practicing proofs and informal logic. These reasoning tools also support later work in theoretical computer science.
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MATH 1756
Learn programming and statistical analysis through data exploration and scientific applications.
Learn programming while exploring, visualizing, and making arguments from data. Statistical tests, regression, probability, and the interpretation of p-values connect computation with scientific readings. No previous programming experience is assumed; placement and course-overlap restrictions apply.
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MATH 140
Translate real-world problems into models and simulations through projects in biology and social science.
Turn a real-world question into a model, investigate it mathematically or through simulation, and interpret the result. Projects may involve flocking, disease spread, or artificial societies and adapt to students’ preparation. The catalog lists no requisites but no 2026–27 offering.
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Read the official course entry ↗Not offered in 2026–27
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18.06
Study matrices and vector spaces, with applications from least squares to networks.
Solve systems of equations and explore eigenvalues, singular values, orthogonality, and positive-definite matrices. Applications include fitting data, Markov processes, and Fourier ideas, with software supporting the mathematics. The catalog lists calculus preparation.
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DS 4200
Turn datasets into visual explanations using design principles and interactive tools.
Study perception, visual encoding, and interaction alongside tools such as Tableau and web-based libraries. Programming can involve Python, JavaScript, HTML, and CSS. The course connects visualization with written explanation in scientific and business applications.
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21-241
Study vector spaces, matrices, eigenvalues, and linear transformations, with introductory proofs.
Study how matrices describe transformations and systems of equations. Topics include basis and dimension, orthogonality, eigenvalues, and diagonalization, with some proof writing. This provides a foundation for recognizing the linear structure behind scientific and computational problems.
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MATH 2000
Study matrices and vector spaces, with applications including graphics and Markov chains.
Connect vectors, matrices, transformations, and eigenvalues to applications such as graphics, Markov chains, and least-squares approximation. The course develops the structure of Euclidean spaces while showing how the same ideas appear in different practical settings.
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MATH 150
Compare voting systems, manipulation, and mathematical limits on fair collective decisions.
Compare majority rule, ranked choice, approval voting, and other ways of combining preferences. Mathematical proofs and case studies expose tradeoffs among fairness, influence, and resistance to manipulation. Arrow’s impossibility theorem is part of the investigation.
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Read the official course entry ↗Fall 2026
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18.100A
Move from calculating with calculus to proving why its central ideas work.
Study sequences, series, continuity, derivatives, integration, and convergence through rigorous arguments. This version emphasizes the real line and is less abstract than 18.100B. It suits an interest in proof and foundations, with calculus as preparation.
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DS 4400
Build and evaluate predictive models, from regression and trees to clustering.
Explore supervised and unsupervised learning, including support-vector machines, logistic regression, principal components, gradient methods, regularization, and boosting. Probability, statistics, and linear algebra underpin the techniques; mathematical prerequisites apply.
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21-270
Use replication and probability models to understand derivative pricing and portfolio decisions.
The central question is how to price a financial contract by reproducing its payoff with other assets. Work from simple cash flows and hedging to random one-period and binomial models, exploring risk-neutral pricing and portfolio choice. This is an entry point into computational finance.
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MATH 2109
Model real-world decisions using linear and nonlinear optimization.
Model problems from the natural and social sciences and search for the best feasible solution. Both analytic and numerical methods are used, with emphasis on nonlinear models. Sensitivity analysis asks how much a solution changes when the input data changes.
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MATH 220
Practice rigorous reasoning and proof-writing using logic, sets, functions, and induction.
Develop the language and habits needed to write convincing mathematical proofs. Logic, sets, functions, relations, and induction supply the examples, with topics varying by semester. This is a bridge from calculation-oriented courses to more abstract mathematics.
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Read the official course entry ↗Fall 2026 listed
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18.05
Use probability, inference, regression, and simulation to reason about uncertain evidence.
Compare Bayesian and frequentist approaches, work with resampling and regression, and use R for simulation. The course also considers causality, privacy, and fairness. Active learning connects mathematical tools to interpreting data and the limits of conclusions.
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MATH 1365
Learn to read and write proofs using logic, sets, relations, and functions.
Develop the style of reasoning used in advanced mathematics, with attention to precise statements and convincing arguments. The subject helps bridge computational mathematics and proof-based study and serves as preparation for the mathematics major.
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21-355
Build rigorous proofs about limits, continuity, differentiation, integration, and convergence.
Revisit calculus through rigorous arguments: what guarantees a limit, when a function reaches an extremum, and why differentiation and integration work. Sequences, compactness, and convergence connect the topics. The course assumes comfort reading and writing proofs.
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MATH 2206
Model chance using random variables, distributions, conditioning, and expectation.
Build mathematical models of chance using conditional probability, independence, expectation, and discrete and continuous random variables. Study common distributions in depth and learn how their assumptions shape the random phenomena they describe.
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MATH 222
Use the Lean proof assistant to formalize and verify mathematical arguments.
Use Lean to turn mathematical arguments into steps a computer can verify. Work with formalized theorems while learning how to understand and write ordinary proofs more clearly. Examples may connect to students’ other mathematical interests and coursework.
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Read the official course entry ↗Fall 2026
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18.650
Build a theoretical foundation for estimation, hypothesis tests, and statistical models.
A fast-paced treatment of confidence intervals, parametric estimation, Bayesian methods, and linear and logistic regression. The emphasis is mathematical intuition and derivation rather than a sequence of formal proofs. Prior probability is required; cross-listed with IDS.014.
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MATH 2331
Study linear systems, vector spaces, eigenvalues, and matrix factorizations.
Connect row reduction with images and kernels, then explore orthogonality, Gram-Schmidt, least squares, diagonalization, and singular values. Software supports mathematical work. These ideas also provide foundations for data analysis and machine learning.
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21-370
Connect binomial option-pricing models to Black–Scholes, martingales, and risk-neutral valuation.
Use discrete financial models to approach the Black–Scholes formula and fit models to data. Martingales, risk-neutral measures, American options, and interest-rate models connect probability with pricing. This is proof-based preparation for the continuous-time finance course.
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MATH 2606
Develop the mathematical foundations of estimation and significance testing.
Study the mathematical basis for learning about a population from data. Likelihood, estimation, confidence intervals, and significance tests connect probability models with inference. The course builds on Probability and includes applications to counts and continuous measurements.
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MATH 252
Apply number theory and algebra to encryption, implementing and testing systems in Python.
Investigate encryption and digital signatures through number theory, algebra, and algorithms. Short Python programs implement systems and expose weaknesses in flawed versions. Prior programming is unnecessary, but proof experience is expected; the course is not offered in 2026–27.
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Read the official course entry ↗Not offered in 2026–27
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18.065
Use linear algebra to understand data compression, principal components, and neural networks.
Explore matrix factorizations, covariance, singular values, graphs, and learning methods, connecting their mathematics to applications in engineering, finance, and large datasets. Linear algebra is the prerequisite. The catalog lists spring in 2026–27, but no offering in 2027–28.
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Read the official course entry ↗Catalog lists spring 2027 · verify schedule
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MATH 3081
Model randomness and use probability to make statistical inferences.
Study conditional probability, independence, distributions, expectation, and variance. The law of large numbers and central limit theorem connect to estimation, confidence intervals, and hypothesis testing. Consult the official entry for mathematical preparation.
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21-420
Apply Brownian motion and stochastic calculus to financial models and option pricing.
Develop the stochastic-calculus machinery behind continuous financial models, beginning with Brownian motion and Ito’s formula. Apply it to Black–Scholes pricing and its differential equation. Further topics may include credit risk and simulation; substantial prior mathematics and finance are required.
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MATH 2805
Connect neural networks and learning methods to mathematics, with labs and projects.
Study regression, classification, clustering, and component analysis through the mathematics supporting neural networks. Labs and projects complement the theory; possible extensions include optimization and algorithmic fairness. Formal programming experience is unnecessary, but prior analysis is required.
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MATH 271
Study vector spaces, matrices, eigenvalues, and transformations with emphasis on theoretical foundations.
Explore matrices and linear transformations through the structure of vector spaces. The emphasis on theoretical foundations makes this useful preparation for further mathematics and quantitative work involving multidimensional objects. Consult the official entry for placement and prerequisite rules.
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Read the official course entry ↗Fall 2026 listed
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18.642
Connect probability, linear algebra, and numerical methods to financial problems.
Study mathematical tools used in finance, including stochastic and computational methods, with perspectives from industry speakers. Prior finance knowledge is helpful but not required. Mathematical prerequisites include linear algebra, differential equations, and probability.
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MATH 4570
Connect linear algebra and optimization to modern data analysis and neural networks.
Study matrix factorization, matrix calculus, gradient and Newton methods, principal components, regression, and neural-network models. Python labs and a final project link the mathematics with implementation. Linear algebra is required.
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36-202
Analyze real data in R using regression, experimental comparisons, and introductory machine learning.
Analyze real datasets with regression, logistic models, experimental comparisons, and introductory machine learning. R labs build computing skills, while written projects develop interpretation and communication. Model selection, overfitting, reproducibility, and data ethics are part of the work.
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ECON 1088
Explore securities, valuation, and financial statements without prior economics training.
Explore stocks, bonds, options, banking, venture capital, and newer financial technologies without assuming prior economics. Apply valuation ideas in portfolio simulations and interpret corporate statements. Questions include market-beating strategies and the effects of AI and cryptocurrencies.
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STAT 135
Use computational tools to develop statistical models, interpret data, and study regression.
Develop statistical reasoning by building and interpreting models with computational tools. Regression provides a way to connect variables and investigate questions using data. This is an introductory route into the later statistics and data-science sequence.
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Read the official course entry ↗Fall and spring 2026–27
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14.01
Explain how consumers, firms, and markets make choices—and when markets fail.
Use supply and demand, consumer behavior, and firm decisions to examine competition, monopoly, welfare, and public policy. This introductory undergraduate subject has no listed prerequisite and provides a foundation for later economics and business study.
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ECON 1116
Examine markets, firms, government policy, and the tradeoffs in allocating resources.
Use supply and demand to understand how decisions interact, then consider market failures and policy responses. Equity, efficiency, sustainability, and distribution are part of the analysis. This is a starting point for further economics study.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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36-315
Create and critique statistical displays, then use graphical methods in a real-data project.
Learn both to make statistical graphics and to recognize when a display misleads. Software labs support a project built around data from a scientific or engineering experiment. The focus connects quantitative analysis with communicating what the evidence actually shows.
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ECON 2225
Examine AI’s effects on productivity, employment, industries, and regulation.
Develop a conceptual understanding of AI before examining its effects on work and productivity. Applications across health, education, and finance lead into questions about regulation and policy. This connects technological change with economic consequences rather than focusing only on algorithms.
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STAT 230
Frame statistical questions, wrangle data, fit models, and communicate results.
Move from a research question to an analysis and a defensible explanation of the results. Topics include experiments, resampling, multiple regression, model selection, and ethics. Software work develops data-wrangling and model-assessment skills without assuming prior software experience.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
Read the official course entry ↗Fall and spring 2026–27
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14.02
Study economic growth, unemployment, inflation, and the policies used to influence them.
Connect interest rates, exchange rates, government spending, and monetary policy to economic outcomes in the United States and other countries. Topics include financial crises, debt, and long-run growth. The catalog lists no prerequisite.
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ECON 1115
Explore growth, business cycles, banking, and government economic policy.
Study money, fiscal and monetary policy, and the performance of economies as a whole. Consider income inequality and the limitations of aggregate measures, alongside the tools used to explain growth and fluctuations.
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36-318
Estimate intervention effects from experiments and observational studies, using R to examine causal questions.
Investigate how to distinguish an intervention’s effect from a correlation. Compare randomized experiments with observational approaches such as matching, instrumental variables, and regression discontinuity, using R for analysis. Applications include questions about medicine, education, and public policy.
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ECON 2409
Study financial institutions, monetary systems, and the Federal Reserve’s policy choices.
Examine how monetary and financial systems function and how well they support economic activity. Contemporary debates about institutions and markets sit alongside the choices made by central banks and regulators. Introductory microeconomics and macroeconomics provide the foundation.
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STAT 231
Work through the data-analysis cycle, including databases, cleaning, modeling, and interpretation.
Work through a complete analysis of large, messy datasets: ask a question, acquire and clean data, build models, and interpret findings. Databases and data-management tools are part of the workflow. Prior introductory statistics is required; introductory computing is recommended.
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Read the official course entry ↗Fall 2026 listed
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14.12
Analyze strategic choices in auctions, bargaining, competition, and repeated interactions.
Work with dynamic games, incomplete information, signaling, and repeated play. Applications show how one decision depends on what others know and might do. This course builds on microeconomics and quantitative preparation; consult the entry for prerequisite choices.
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ECON 2560
Use economic data and regression to investigate questions in an applied research paper.
Work with tools such as R, Stata, or Python while confronting omitted-variable bias, heteroskedasticity, and multicollinearity. Additional methods may include instrumental variables, panel data, or time series. The course emphasizes interpreting results and conducting empirical analysis.
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36-350
Build programming skills for statistical analysis using R and Python.
Build the programming habits needed for statistical work: functions, data structures, debugging, input/output, and abstraction. The course uses R and Python, linking computing fundamentals to statistical practice. It assumes prior statistics and introductory computing rather than advanced software experience.
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ECON 3277
Conduct empirical economic research in R, including causal inference.
Develop an empirical research project from data cleaning and hypothesis formation through estimation and interpretation. Use R and methods for drawing causal conclusions from non-experimental data. The course emphasizes conducting applied research rather than replacing the theoretical econometrics sequence.
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STAT 240
Explore multidimensional datasets through techniques such as clustering, random forests, and principal components.
Choose methods for datasets with many variables, including principal components, trees, random forests, and clustering. Hands-on software analysis emphasizes applications and the modeling process over theory. The curriculum currently lists no offering in 2026–27.
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Read the official course entry ↗Not offered in 2026–27
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14.19
Investigate how to design auctions and matching systems that allocate resources effectively.
Apply economic theory to the rules of markets, including mechanism design and matching. The focus is on shaping how people interact and how scarce resources are assigned, rather than simply observing prices. Introductory microeconomics is required.
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ECON 3416
Study how psychology changes economic decisions and their policy implications.
Explore present bias, reference dependence, social preferences, beliefs, attention, and framing. Empirical evidence tests where standard models need adjustment, with applications including finance. Microeconomics and statistics preparation are required.
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36-401
Fit models to real datasets, evaluate assumptions, and explain findings in a scientific report.
Treat data analysis as a sequence of judgments about the question, model, assumptions, and defensible conclusions. Work with real datasets and compare plausible approaches instead of applying a formula mechanically. Scientific reporting is part of the analytical task.
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ECON 3401
Apply microeconomics to risk pricing, portfolios, and market efficiency.
Use microeconomic theory to study how markets price risk, allocate capital, and move value over time. Likely topics include portfolio choice, derivatives, and market efficiency. Questions about the social usefulness of finance accompany the analytical models.
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STAT 360
Combine theoretical and empirical approaches to probability, distributions, and random phenomena.
Study random phenomena through both mathematical reasoning and empirical investigation. Probability models and distributions provide the language for analyzing uncertainty. This supports further work in statistical theory as well as quantitative applications.
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Read the official course entry ↗Fall 2026 listed
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14.32
Use regression and causal inference to evaluate real economic claims.
Study experiments, instrumental variables, regression discontinuity, differences-in-differences, and time-series methods. Learn to read and critique empirical work and identify the assumptions behind a causal claim. Statistics preparation is required; the subject can fulfill MIT’s laboratory requirement.
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ENTR 1201
Explore what entrepreneurs do across startups, growing firms, and small businesses.
Survey the entrepreneurial process and the characteristics associated with starting and developing ventures. The scope extends beyond high-growth technology companies to small and medium businesses and microbusinesses. It provides an introduction to later entrepreneurship study.
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36-497
Apply statistical methods to practical problems through an undergraduate capstone experience.
Work in a small team on a supervised project for an industry client. Alongside analysis, practice defining a real problem, managing deliverables, collaborating, and explaining findings. Projects rotate and enrollment is application-based, with attention to the skills each project needs.
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ECON 3634
Study how psychological biases influence investors and financial markets.
Read empirical research on financial decisions that depart from rational-choice models. Possible topics include overconfidence, inattention, bubbles, and reluctance to realize losses. The seminar connects psychological evidence with how investors and financial markets behave.
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STAT 495
Investigate complex datasets and communicate findings in the Statistics major’s capstone.
Bring together earlier statistical methods to investigate complicated datasets and communicate what the evidence supports. This advanced course serves as the Statistics major’s capstone, connecting analysis with the interpretation and presentation of findings.
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Read the official course entry ↗Fall 2026 listed
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14.17
Examine blockchain and tokenization through economics, contracts, and financial-system design.
Connect technological mechanisms with incentives, monetary theory, smart contracts, market structure, and regulation. The course examines both possible uses and risks of these systems. It is listed as a new undergraduate subject and requires introductory microeconomics.
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ENTR 3305
Design a business model and a strategy for creating value in a changing industry.
Study platforms, ecosystems, open innovation, and startup strategy through cases and research into companies. Apply the ideas to developing a strategy of your own. The emphasis is on how an enterprise works and competes, not just its initial product idea.
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73-102
Analyze individual decisions, market outcomes, policy, and the conditions behind market failures.
Use economic reasoning to connect individual choices with prices, shortages, and government policy. Everyday market questions lead into market failure and strategic interaction. The course develops a toolkit for analyzing incentives rather than treating economic outcomes as isolated facts.
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ECON 111
Explore how markets and economies organize production, consumption, investment, and public policy.
Build an introductory framework for thinking about consumers, firms, markets, and the overall economy. Questions about production, investment, and policy connect individual decisions with aggregate outcomes. This is the starting point for much of the economics curriculum.
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Read the official course entry ↗Fall and spring 2026–27
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15.053
Turn business decisions into optimization models and solve them computationally.
Study linear, integer, network, and nonlinear optimization alongside heuristic approaches. Applications connect logistics, finance, and machine learning. Team projects apply optimization to concrete problems; prior coding is required.
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MISM 2301
Understand how organizations use digital systems and data to compete and operate.
Examine the benefits, risks, and implementation of information technology, including its potential to disrupt businesses. Data analysis and business frameworks help connect technical capabilities with strategic decisions and organizational needs.
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73-265
Use programming, statistics, and visualization to investigate economic and business datasets.
Practice organizing, visualizing, and interpreting economic and business data with programming and statistical tools. Potential applications range from pricing and earnings to investment risk and worker productivity. The course connects economic questions to the practical decisions involved in empirical analysis.
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ECON 237
Weigh the growth benefits and crisis risks of international financial integration.
Compare the promise of cross-border finance for growth and poverty reduction with the danger of crises. Theory and evidence help identify conditions and policies that make integration work better. The course also considers inflation and monetary policy.
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Read the official course entry ↗Fall 2026
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15.3901
Develop a startup idea through customer research, market selection, and business planning.
Consider a venture’s value proposition, founding team, customers, fundraising, and legal and ethical questions. This undergraduate version introduces a systematic approach to creating a business, with no listed prerequisite. It is distinct from the graduate-numbered 15.390.
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MISM 3515
Choose analytical techniques and interpret patterns in business datasets.
Learn how to extract useful insights from unfamiliar data, select methods, and explain their results. Examples include market-basket analysis and principal components, using business problems and accessible tools. The course requires prior statistics or an approved alternative.
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73-274
Estimate economic relationships and evaluate theories or policies using imperfect real-world data.
Develop methods for estimating economic relationships and evaluating theories, business strategies, and government policies. Theoretical foundations are paired with examples involving imperfect real-world data. This builds on economics, statistics, and the Economics and Data Science course.
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ECON 250
Connect financial markets and monetary policy to employment, output, and inflation.
Trace how banks and financial markets affect jobs, output, and inflation. Study institutions, regulation, central banking, international finance, and crises, then assess how policy changes travel through the system. Introductory economics is required.
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Read the official course entry ↗Spring 2027
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15.501
Learn to read financial statements and assess a company’s performance and financial position.
Study how accounting information is prepared and interpreted for decisions about businesses and securities. Connect statements with valuation, cash flows, economics, and finance. This undergraduate subject has no listed prerequisite and supports later finance work.
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FINA 3301
Evaluate company value, financing choices, risk, and investment decisions.
Use cases and spreadsheets to study cost of capital, financial policies, working capital, and multinational decisions. Topics can include mergers, leases, and restructuring, with attention to legal and ethical context. Teamwork and communication matter alongside analysis; introductory finance is required.
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73-315
Study matching and auctions, with applications such as school assignment, transplantation, and advertising markets.
Start with allocation problems, then ask what rules would produce better outcomes. Study matching systems for schools, medical residencies, and kidneys alongside auctions for advertising, securities, and public resources. Algorithms and economic theory help explain why a market’s design matters.
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ECON 360
Use statistical and mathematical tools to investigate economic relationships with data.
Use mathematical and statistical methods to investigate economic relationships with data. The course develops the quantitative foundation for evaluating empirical claims and for later research-oriented economics electives. Check the official listing for the required mathematical preparation.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
Read the official course entry ↗Fall and spring 2026–27
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15.417
Combine investment theory with team investigations using real financial-market data.
Explore asset pricing, forecasting, and investment strategies through analytical work and projects. The laboratory develops communication as well as financial reasoning. It is an undergraduate subject with no listed prerequisite, and is also part of MIT’s Finance degree.
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FINA 3303
Study securities, risk and return, and the construction and evaluation of portfolios.
Compare domestic and international markets, assess valuation and market efficiency, and explore portfolio protection and performance. The course offers an opportunity to manage a portfolio while applying investment concepts. Earlier finance preparation is required.
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73-347
Use strategic games and equilibrium concepts to analyze competition and collective-action problems.
Use formal games to analyze decisions whose outcomes depend on what other people do. Study equilibrium concepts and their limits through economic examples such as competing firms and free-rider problems. This is a theory-led route into strategy and incentives.
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ECON 420
Analyze strategic cooperation and conflict among firms, people, and governments.
Analyze situations where outcomes depend on the choices of multiple participants. Mathematical models of strategic interaction help explain cooperation and conflict among individuals, firms, and governments. This is an advanced economics elective; consult the official entry for prerequisites.
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Read the official course entry ↗Fall 2026
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15.418
Analyze corporate investment and financing decisions through cases and valuation projects.
Work in teams with financial data to evaluate cash flows, business investments, and transactions. Cases connect theory to decisions such as valuing an oilfield or a merger. Financial accounting is a corequisite, and the laboratory emphasizes communicating the analysis.
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FINA 4390
Apply machine learning and deep learning to financial analysis.
Connect computational models with financial problems while considering responsible use of analytical tools. Programming and finance preparation, or listed alternatives, are required; familiarity with spreadsheets is recommended. This is an advanced application course rather than an introduction to coding.
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73-367
Investigate how automation changes jobs, using economic theory, worker data, and policy analysis.
Combine theories of technological change with data on workers and technology adoption. Examine how automation reshapes tasks and skill demands, then consider policies such as retraining and income support. Historical technologies provide context for current AI and robotics.
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ECON 453
Study entrepreneurial success, innovation, and policy through empirical research on firms and workers.
Investigate what makes entrepreneurs successful and how they contribute to innovation and growth. Read empirical studies, use public data on firms and workers, and consider policy implications. The focus is economic research about entrepreneurship, with prior econometrics required.
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Read the official course entry ↗Fall 2026
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15.4311
Study how startups raise money and how investors value young companies.
Examine when to raise capital, how much to seek, which sources to use, and how financing contracts shape incentives. Topics include valuation, exits, and private equity from both founder and investor perspectives. The undergraduate subject requires 15.417.
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FINA 4460
Analyze systematic trading strategies and their performance using computational tools.
Explore fundamental, factor, and covariance models alongside high-frequency trading and real-time performance analysis. Consider the limits of systematic strategies as well as their construction. Python familiarity is expected, with earlier finance coursework required.
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70-122
Interpret financial statements and accounting records for management and investment decisions.
Learn to read the balance sheet, income statement, and cash-flow statement as accounts of how a business operates. Topics include inventory, assets, liabilities, equity, and financial analysis. The aim is to use accounting information in management and investment decisions.
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70-257
Translate business problems into mathematical models and solve them with optimization software.
Translate a practical business problem into an objective, variables, and constraints. Explore linear, integer, and nonlinear optimization, including network and routing problems, then solve models with spreadsheet tools. The emphasis joins mathematical formulation with implementation.
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70-374
Use R and predictive methods to extract useful patterns from business and consumer data.
Use R to explore and predict patterns in business and consumer data. Methods include classification, nearest-neighbor approaches, decision trees, and clustering. Applications across marketing, sales, finance, and operations help connect model choices with the decisions they support.
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70-391
Analyze cash flows, risk, capital structure, and valuation to support investment decisions.
Build a financial decision from assumptions and spreadsheet cash flows through to valuation. Study discounting, portfolio risk, the cost of capital, and corporate financing choices. The course links financial theory to evaluating alternative solutions to a business problem.
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70-415
Test a business idea through market research, presentations, and interaction with entrepreneurs.
Develop a business idea and test whether it represents a viable opportunity. Market research, contact with entrepreneurs, and written and oral pitches make this an experiential introduction. It also asks students to examine their own strengths and approach to leadership.
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70-492
Compare securities, risk and returns, and approaches to constructing investment portfolios.
Compare types of publicly traded securities and the relationships among their prices, risks, and returns. Apply valuation tools to real situations and consider how different assets fit into a portfolio or strategy. Prior finance coursework is required.
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16-467
Explore how people and robots can interact naturally, safely, and effectively.
Bring together robotics, psychology, design, and AI to study human–robot relationships. Mini-projects and a group project connect theory with applications such as assistive robots. The catalog lists no prerequisites; basic robotics familiarity is recommended and programming can help with the project.
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DCS 2475
Use stories, films, and ethical analysis to examine AI’s effects on people and society.
Consider AI from the perspectives of its creators, users, and policymakers. Literary and cinematic narratives help explore responsibility and human agency, alongside conceptual and functional analysis of AI artifacts. Prior DCS 1100 is required.
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Read the official course entry ↗Fall 2026 listed
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PSYC 208
Design an experiment about creativity and investigate how original ideas emerge.
Read psychological research, propose a study, and join a team to design experiments and analyze data. Written and oral presentations accompany the process. Creativity is examined across settings such as art, science, sport, and business; prior statistics is required.
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Read the official course entry ↗Spring 2027
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6.4210
Explore how robots perceive, plan, and handle objects in unstructured environments.
Combine perception and deep learning with 3D geometry, kinematics, planning, and control under uncertainty. Applications include robots working around objects in homes or restaurants. Programming and machine-learning preparation are expected; written and oral communication are part of the course.
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CS 2386
Build a 3D game using a game engine and supporting development tools.
Work with rendering, animation, collisions, physics, audio, game logic, and simple AI. Study how assets, scripting, and level design fit into a development pipeline. Prior programming or appropriate design preparation is required.
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05-362
Make and play-test games intended to change how people think, feel, or behave.
Develop games for real clients and problems, revising ideas through player research and repeated testing. The course treats a player’s actual experience as essential evidence. It combines creative design with questions about whether a game produces its intended change.
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DCS 3301
Use maps and statistical analysis to investigate racial and economic inequality.
Combine GIS with R to study urban inequality, including housing, health, environmental justice, and access to credit. Examine how maps can reinforce disparities as well as reveal them. Students develop independent projects; approved prior methods coursework is required.
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PSYC 233
Investigate perception, memory, intelligence, and the mind’s ways of solving problems.
Follow information from sights and sounds through storage and retrieval, comparing explanations of how memory works. Apply these ideas to thinking and intelligence, including what brain damage reveals about learning. Introductory psychology or instructor consent is required.
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Read the official course entry ↗Spring 2027
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CMS.301
Design games through rapid prototypes, playtesting, and repeated revision.
A play-centered approach to game design built around exercises, group work, and critiques. Test how rules and design choices change the experience, then improve a prototype. No programming background or formal prerequisite is required.
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CS 4050
Discuss how AI affects people, institutions, and social values.
Use multidisciplinary readings to examine the social consequences and ethical questions surrounding AI. The catalog describes a broad discussion-based perspective on technology in society; consult the official entry for enrollment requirements and the current course context.
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05-391
Learn to design software interfaces around what people need and can use.
Investigate why interfaces frustrate people and how design can make systems more useful. The course introduces interface design, prototyping, and evaluation. It is an entry point into human–computer interaction for students curious about the human experience of technology.
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EOS 1505
Explore oceans and climate through labs, Maine coastal fieldwork, and a research project.
Connect ocean basins and sediments with circulation, tides, chemical cycles, and ecosystems. Weekly labs and fieldwork apply the science in Casco Bay and the Gulf of Maine. A coastal research project offers a hands-on way into environmental science.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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PSYC 235
Study the psychology of motivation, performance, coaching, and athletic participation.
Examine questions such as choking under pressure, the hot-hand effect, imagery, and the role of personality in sport. Discussion and writing connect research with athletic experience. The course requires introductory psychology or consent and is open to juniors and seniors.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
Read the official course entry ↗Spring 2027
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CMS.611
Join a multidisciplinary team to create a video game, from concept to testing.
Bring programming, visual art, music, fiction, or production interests into a collaborative project. The course emphasizes development, testing, and iteration across creative and technical roles. Prior programming or CMS.301 is required; cross-listed with 6.4570.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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CS 4610
Explore how robots represent space, plan movement, and interact with the world.
Study robotic system architecture, three-dimensional kinematics, motion planning, and mobile or manipulator systems. Additional topics may include perception, learning, control, or human-robot interaction. The course builds on advanced programming and mathematics preparation.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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05-317
Design AI products and services, then refine ideas with real users.
Explore possible products through user-centered and service-design methods. Projects consider how to handle AI errors and combine human and machine capabilities effectively. The work connects technical possibilities with whether a proposed system improves people’s lives.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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EOS 2405
Use Google Earth Engine to investigate environmental change from local to global scales.
Pose questions about climate, wildfires, deforestation, urbanization, drought, or changing glaciers using spatial data. The course connects Earth-system concepts with computational investigation of human-driven change. Prior Earth and oceanographic science coursework is required.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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ENST 225
Evaluate climate evidence and possible solutions across science, policy, and society.
Combine biological, physical, and social perspectives to assess mitigation and adaptation. Read scientific work, discuss stakeholder knowledge, and explore local and global responses through projects and experiential activities. Written, oral, and visual science communication are part of the course.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
Read the official course entry ↗Spring 2027
Short description · Full description & prerequisites ↗
9.00
Explore how the mind works, from perception and memory to emotion and social behavior.
Survey learning, development, personality, psychological disorders, and the relationship between mind and brain. Consider debates about consciousness and the roles of biology and experience. This broad introductory subject has no listed prerequisite.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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ENTR 3330
Develop and test a startup idea through customer research and prototypes.
Small teams combine design thinking with lean and agile approaches to investigate a problem, validate assumptions, and refine a concept. Work emphasizes learning with limited resources and testing ideas with potential users. Assessment includes collaborative work.
SchoolSkimmer summary. Check the official entry for the full description, prerequisites, and enrollment rules.
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B.S. major
A broad computing degree spanning theory, systems, software, and AI.
Requirements shown are for students entering in fall 2026.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
A computing core with breadth across theory, systems, and artificial intelligence.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
Develop foundations in programming, algorithms, and systems before choosing electives.
Earlier class years have different totals.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Build depth in software, algorithms, systems, and computing theory, then choose advanced areas.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S.C.S. major
Build a computing foundation and choose a concentration such as AI, foundations, or software.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
A flexible mathematics degree with options in applied mathematics, discrete mathematics, statistics, and operations research.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
Build a mathematics program that can include probability, statistics, data science, and machine learning.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
Study calculus and linear algebra, then pursue analysis, algebra, and electives.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Combine computing and mathematics with learning, decision-making, and human-centered applications.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Combine programming, data systems, machine learning, and mathematical foundations.
The former Data Science B.S. URL redirects to this Artificial Intelligence program in the current catalog; the title here follows that catalog.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Develop statistical judgment and apply it to a self-defined area outside statistics.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
Study economic theory and statistics, then investigate advanced topics.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
Combine statistical methods and theory with computing and a data-analysis capstone.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Connect algorithms and machine learning with economic theory and empirical analysis.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Study proof, analysis, algebra, and applied methods, with electives and a capstone.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Pair statistical inference with a deeper sequence in computation and machine learning.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Minor
A formal finance-related option alongside another major.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Major
Develop microeconomics, macroeconomics, and econometrics alongside electives.
Advanced core versions have additional mathematics prerequisites.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Build a foundation in proof and mathematical methods with room for advanced electives.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Combine economic theory with statistics, econometrics, and a broad elective selection.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
A quantitative business degree with concentrations including Finance, AI and Business, and Business Analytics and Technologies.
Finance is a concentration within this degree. Computational Finance is a separate B.S. program.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Interdisciplinary major
Combine the computing core with proof-based mathematics.
An interdisciplinary major cannot normally be combined with another major.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Study economic models and evidence, with options ranging from market design to public policy.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S.B.A. major
Explore core business fields, select a concentration, and connect coursework with professional experience.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Combine economic theory, quantitative analysis, and an independent senior project.
CMU also lists a B.A. in Economics and joint economics degrees in its official catalog.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Interdisciplinary major
Connect economic analysis with algorithms and software development.
An interdisciplinary major cannot normally be combined with another major.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Combine a business foundation with a concentration tailored to your interests.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Business concentration
Focus a business degree on corporate finance, investments, risk, and financial decisions.
A concentration within an eligible business degree, rather than a standalone Finance major.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
A mathematical route into finance combining programming, probability, economics, and financial engineering.
Admission is by application. Overlap rules prohibit combining this major with Business Administration, Mathematical Sciences, or Economics and Mathematical Sciences.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Interdisciplinary major
Emphasize the mathematical models behind economic questions.
An interdisciplinary major cannot normally be combined with another major.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Use programming, optimization, and statistical learning to improve organizational decisions.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Business concentration
Use data management, visualization, and analytics to inform business decisions.
A concentration within an eligible business degree; the parent degree’s requirements also apply.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Coordinate major
Connect computational methods with questions from another liberal-arts discipline.
A coordinate major requires another field; it is not a standalone Data Science major.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Study investment and corporate finance through analytical coursework and laboratories.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
B.S. major
Bring finance, mathematical modeling, and programming together in one undergraduate degree.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Minor
Add a structured sequence in uncertainty, computation, and data analysis to another major.
This is a minor; the separate 6-14 major also offers a route into data science.
Overview of key requirements. The official degree plan includes full rules, electives, and exceptions.
Try a broader search, another subject, or reset the filters. The full official catalogs below may have more options.
A program map across all five schools. Follow the links for curriculum details.
CMU
Specialized degrees, including Statistics and Data Science, Business Administration, and Computational Finance.
Bowdoin
Departmental and interdisciplinary majors, plus an Economics and Finance minor. Statistics and data science also appear in mathematics and computing.
Amherst
Separate Computer Science, Mathematics, Statistics, and Economics majors. Explore business and finance interests through related courses.
MIT
Separate degrees in computing, mathematics, economics, management, business analytics, and finance. MIT calls classes “subjects”; numbered “Courses” also identify degree programs.
Northeastern
Explore computing and AI, business concentrations, and a Quantitative Finance degree combining finance, mathematics, and programming. Program summaries here refer to Boston; other campuses can differ.
Program names describe different structures. A minor, concentration, coordinate major, and standalone major are different credentials. Related courses can be available even when a school has no degree with that exact name.
Course descriptions are SchoolSkimmer summaries. Subject tags are our browsing categories, including related applications. Requirements are abbreviated; consult the official program for degree totals, prerequisites, placement, grades, and enrollment restrictions.
Reviewed September 22, 2026. This collection is a dated snapshot, not a live schedule. “Not offered” refers specifically to 2026–27. Other offerings may rotate. Units, course credits, and semester hours differ across schools and are not directly comparable.
2026–27 undergraduate catalog
2026–27 catalogue
2026–27 curriculum and current major pages
2026–27 undergraduate catalog
2026–27 undergraduate catalog · Boston programs