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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

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34 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.

Carnegie Mellon

15-112

Fundamentals of Programming and Computer Science

Computer science

Learn Python through program design, debugging, and testing; a rigorous introduction for new programmers.

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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.

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Carnegie Mellon

15-122

Principles of Imperative Computation

Computer science

Turn algorithms into correct programs, using C-based languages, verification, and fundamental data structures.

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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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Carnegie Mellon

07-280

Artificial Intelligence and Machine Learning I

Computer scienceData science

Combine AI theory with implementations of influential systems, including language models and reinforcement learning.

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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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Carnegie Mellon

15-388

Practical Data Science

Computer scienceData scienceStatistics

Work through data collection, modeling, visualization, and a team project using practical computing tools.

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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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Carnegie Mellon

15-445

Database Systems

Computer scienceData science

Explore how databases store information, execute queries, and keep transactions reliable.

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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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Carnegie Mellon

10-301

Introduction to Machine Learning

Computer scienceData scienceStatistics

Study learning algorithms mathematically and through programming experiments; the catalog recommends this version for non-SCS undergraduates.

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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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Carnegie Mellon

21-127

Concepts of Mathematics

MathComputer science

Develop proof-writing tools through logic, sets, functions, induction, and number theory.

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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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Carnegie Mellon

21-241

Matrices and Linear Transformations

MathData science

Study vector spaces, matrices, eigenvalues, and linear transformations, with introductory proofs.

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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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Carnegie Mellon

21-270

Introduction to Mathematical Finance

MathFinance

Use replication and probability models to understand derivative pricing and portfolio decisions.

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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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Carnegie Mellon

21-355

Principles of Real Analysis I

Math

Build rigorous proofs about limits, continuity, differentiation, integration, and convergence.

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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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Carnegie Mellon

21-370

Discrete Time Finance

MathFinance

Connect binomial option-pricing models to Black–Scholes, martingales, and risk-neutral valuation.

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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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Carnegie Mellon

21-420

Continuous-Time Finance

MathFinance

Apply Brownian motion and stochastic calculus to financial models and option pricing.

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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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Carnegie Mellon

36-202

Methods for Statistics & Data Science

StatisticsData science

Analyze real data in R using regression, experimental comparisons, and introductory machine learning.

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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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Carnegie Mellon

36-315

Statistical Graphics and Visualization

StatisticsData science

Create and critique statistical displays, then use graphical methods in a real-data project.

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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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Carnegie Mellon

36-318

Introduction to Causal Inference

StatisticsData scienceEconomics

Estimate intervention effects from experiments and observational studies, using R to examine causal questions.

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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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Carnegie Mellon

36-350

Statistical Computing

StatisticsData scienceComputer science

Build programming skills for statistical analysis using R and Python.

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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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Carnegie Mellon

36-401

Modern Regression

StatisticsData science

Fit models to real datasets, evaluate assumptions, and explain findings in a scientific report.

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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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Carnegie Mellon

36-497

Corporate Capstone Project

StatisticsData scienceBusiness

Apply statistical methods to practical problems through an undergraduate capstone experience.

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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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Carnegie Mellon

73-102

Principles of Microeconomics

EconomicsBusiness

Analyze individual decisions, market outcomes, policy, and the conditions behind market failures.

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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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Carnegie Mellon

73-265

Economics and Data Science

EconomicsData scienceBusiness

Use programming, statistics, and visualization to investigate economic and business datasets.

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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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Carnegie Mellon

73-274

Econometrics I

EconomicsStatisticsData science

Estimate economic relationships and evaluate theories or policies using imperfect real-world data.

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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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Carnegie Mellon

73-315

Market Design

EconomicsMathBusiness

Study matching and auctions, with applications such as school assignment, transplantation, and advertising markets.

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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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Carnegie Mellon

73-347

Game Theory Applications for Economics and Business

EconomicsMathBusiness

Use strategic games and equilibrium concepts to analyze competition and collective-action problems.

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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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Carnegie Mellon

73-367

AI, Technology, and Work

EconomicsComputer science

Investigate how automation changes jobs, using economic theory, worker data, and policy analysis.

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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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Carnegie Mellon

70-122

Introduction to Accounting

BusinessFinance

Interpret financial statements and accounting records for management and investment decisions.

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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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Carnegie Mellon

70-257

Optimization for Business

BusinessMathData science

Translate business problems into mathematical models and solve them with optimization software.

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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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Carnegie Mellon

70-374

Data Mining & Business Analytics

BusinessData scienceStatistics

Use R and predictive methods to extract useful patterns from business and consumer data.

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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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Carnegie Mellon

70-391

Finance

BusinessFinance

Analyze cash flows, risk, capital structure, and valuation to support investment decisions.

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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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Carnegie Mellon

70-415

Introduction to Entrepreneurship

Business

Test a business idea through market research, presentations, and interaction with entrepreneurs.

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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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Carnegie Mellon

70-492

Investment Analysis

FinanceBusiness

Compare securities, risk and returns, and approaches to constructing investment portfolios.

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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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Carnegie Mellon

16-467

Introduction to Human Robot Interaction

Computer scienceRoboticsPsychology

Explore how people and robots can interact naturally, safely, and effectively.

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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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Carnegie Mellon

05-362

Transformational Game Design Studio

Computer scienceGame design

Make and play-test games intended to change how people think, feel, or behave.

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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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Carnegie Mellon

05-391

Designing Human Centered Software

Computer scienceDesign

Learn to design software interfaces around what people need and can use.

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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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Carnegie Mellon

05-317

Design of Artificial Intelligence Products

Computer scienceBusinessProduct design

Design AI products and services, then refine ideas with real users.

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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.

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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.