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Follow your curiosity / Undergraduate study

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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 ↗

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28 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.

MIT

6.1000

Introduction to Programming and Computer Science

Computer scienceData science

Learn Python and computational modeling through simulations, optimization, and statistical examples.

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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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MIT

6.1020

Software Construction

Computer science

Build reliable software using specifications, testing, abstraction, and careful design.

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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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MIT

6.1210

Introduction to Algorithms

Computer scienceMath

Design algorithms, choose data structures, and analyze how efficiently they solve problems.

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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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MIT

6.3900

Introduction to Machine Learning

Computer scienceData scienceStatistics

Learn how machines make predictions and decisions through supervised, unsupervised, and reinforcement learning.

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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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MIT

6.1830

Software Systems for Data Science

Computer scienceData science

Build the software behind data analysis, from cleaning and databases to large-scale computation.

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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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MIT

6.1200

Mathematics for Computer Science

Computer scienceMath

Develop the discrete mathematics and proof techniques used throughout computer science.

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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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MIT

18.06

Linear Algebra

MathData science

Study matrices and vector spaces, with applications from least squares to networks.

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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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MIT

18.100A

Real Analysis

Math

Move from calculating with calculus to proving why its central ideas work.

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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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MIT

18.05

Introduction to Probability and Statistics

StatisticsMathData science

Use probability, inference, regression, and simulation to reason about uncertain evidence.

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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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MIT

18.650

Fundamentals of Statistics

StatisticsMathData science

Build a theoretical foundation for estimation, hypothesis tests, and statistical models.

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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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MIT

18.065

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

MathData scienceStatistics

Use linear algebra to understand data compression, principal components, and neural networks.

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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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MIT

18.642

Topics in Mathematics with Applications in Finance

MathFinanceStatistics

Connect probability, linear algebra, and numerical methods to financial problems.

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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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MIT

14.01

Principles of Microeconomics

EconomicsBusiness

Explain how consumers, firms, and markets make choices—and when markets fail.

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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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MIT

14.02

Principles of Macroeconomics

EconomicsFinance

Study economic growth, unemployment, inflation, and the policies used to influence them.

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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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MIT

14.12

Economic Applications of Game Theory

EconomicsMathBusiness

Analyze strategic choices in auctions, bargaining, competition, and repeated interactions.

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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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MIT

14.19

Market Design

EconomicsMathBusiness

Investigate how to design auctions and matching systems that allocate resources effectively.

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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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MIT

14.32

Econometric Data Science

EconomicsData scienceStatistics

Use regression and causal inference to evaluate real economic claims.

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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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MIT

14.17

Blockchain and Financial System Design

EconomicsFinanceComputer science

Examine blockchain and tokenization through economics, contracts, and financial-system design.

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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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MIT

15.053

Optimization Methods in Business Analytics

BusinessMathData science

Turn business decisions into optimization models and solve them computationally.

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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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MIT

15.3901

Entrepreneurship 101: Systematic Approach to New Venture Creation

Business

Develop a startup idea through customer research, market selection, and business planning.

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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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MIT

15.501

Corporate Financial Accounting

BusinessFinance

Learn to read financial statements and assess a company’s performance and financial position.

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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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MIT

15.417

Laboratory in Investments

FinanceStatisticsBusiness

Combine investment theory with team investigations using real financial-market data.

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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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MIT

15.418

Laboratory in Corporate Finance

FinanceBusiness

Analyze corporate investment and financing decisions through cases and valuation projects.

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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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MIT

15.4311

Entrepreneurial Finance and Venture Capital

FinanceBusiness

Study how startups raise money and how investors value young companies.

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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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MIT

6.4210

Robotic Manipulation

Computer scienceMathRobotics

Explore how robots perceive, plan, and handle objects in unstructured environments.

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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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MIT

CMS.301

Game Design Methods

Game designCreative practice

Design games through rapid prototypes, playtesting, and repeated revision.

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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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MIT

CMS.611

Creating Video Games

Computer scienceGame developmentCreative practice

Join a multidisciplinary team to create a video game, from concept to testing.

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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.

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MIT

9.00

Introduction to Psychological Science

PsychologyMind & brain

Explore how the mind works, from perception and memory to emotion and social behavior.

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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.

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Full catalogs & how to read this page

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.