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Course 15: Management |
 | | | 15.00-15.299 | | | 15.30-15.699 | | | 15.70-15.999 plus UROP and Thesis | | |  |
Managerial Economics15.000 Explorations in Management
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Prereq: None Units: 2-0-1 [P/D/F]  Ends Dec 11. Lecture: R3-4.30 (E51-335)
Broad introduction to the various aspects of management including analytics, accounting and finance, operations, marketing, entrepreneurship and leadership, organizations, economics, systems dynamics, and negotiation and communication. Introduces the field of management through a variety of experiences as well as discussions led by faculty or industry experts. Also reviews the three undergraduate majors offered by Sloan as well as careers in management. Subject can count toward the 6-unit discovery-focused credit limit for first year students. Limited to undergraduates; preference to first years. J. Orlin No required or recommended textbooks 15.002 Leadership Challenges for an Inclusive World
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Prereq: None Units arranged [P/D/F]  You must participate in Sloan's Course Bidding to take this subject. Lecture: TBA
Units assigned to MBA students upon completion. Restricted to Sloan MBA students. Fall: Consult: Sloan Educational Services Spring: Consult: Sloan Educational Services No textbook information available 15.003 Analytics Tools
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Prereq: None Units: 2-0-1 [P/D/F] You must participate in Sloan's Course Bidding to take this subject. Lecture: TBA
Units assigned to Master of Business Analytics students upon completion of the Analytics Tools requirement. Restricted to Master of Business Analytics students. M. Li No textbook information available 15.004 Programming for Finance Professionals
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Prereq: None Units: 1-0-0 [P/D/F] You must participate in Sloan's Course Bidding to take this subject. TBA.
Two-day accelerated course with supplemental recitations designed to develop skills in applying basic methods from the programming language Python (with additional references from R) to financial problems. Topics include programming basics in Python, data manipulation, visualization and reporting and an overview of programming ethics. MFin students will apply and build upon these skills in 15.433 Financial Markets and 15.450/15.457 Analytics and Advanced Analytics of Finance. Students must pass one of two exams offered during the summer term to demonstrate their ability to solve financial problems using R and Python. Restricted to Sloan Master of Finance Program students. B. Vartak No textbook information available 15.005 Sloan Intensive Period Elective Requirement
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Prereq: 15.002 Units arranged [P/D/F]
Units assigned to MBA students upon completion of the Sloan Intensive Period (SIP) elective requirement. Restricted to Sloan MBA students. Consult: Sloan Educational Services 15.010 Economic Analysis for Business Decisions
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Prereq: None Units: 4-0-5 You must participate in Sloan's Course Bidding to take this subject. Ends Dec 11. Lecture: TR10-11.30 (E62-262, E51-315, E51-325) or TR8.30-10 (E62-262, E51-315, E51-325) Recitation: F10 (E51-325) or F11 (E51-325) or F12 (E51-325) +final
Introduces principles of microeconomics as a framework for making more informed managerial decisions. Discusses the supply and demand paradigm with applications to digital marketplaces, innovation, sources of market power, and strategic pricing. Provides an introduction to game theory to study competition and cooperation both within and between firms. Restricted to first-year Sloan MBA students. N. Kala, M. Demirer No textbook information available 15.011 Economic Analysis for Business Decisions
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(Subject meets with 15.0111) Prereq: None Units: 4-0-5
Introduces principles of microeconomics as a framework for making more informed managerial decisions. Discusses the supply and demand paradigm with applications to digital marketplaces, innovation, sources of market power, and strategic pricing. Provides an introduction to game theory to study competition and cooperation both within and between firms. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Intended for non-Sloan graduate students; not open to Sloan MBA students. C. Angelucci 15.0111 Economic Analysis for Business Decisions
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(Subject meets with 15.011) Prereq: None Units: 4-0-5
Introduces principles of microeconomics as a framework for making more informed managerial decisions. Discusses the supply and demand paradigm with applications to digital marketplaces, innovation, sources of market power, and strategic pricing. Provides an introduction to game theory to study competition and cooperation both within and between firms. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Intended for undergraduate students; not open to Sloan MBA students. Staff 15.012 Applied Macro- and International Economics
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Prereq: None Units: 3-0-6
Explores the macroeconomic environment in which firms operate. Aims to provide a strong foundation in macroeconomic concepts and apply them to understand specific country experiences. Introduces the basic tools of short-run macroeconomic management, primarily monetary and fiscal policy, utilizing historical case studies and modern policy discussions as context. Explores drivers of long-term growth, examining the cases of economic miracles and productivity slowdowns in developed economies, and then delves into the fundamental theory of trade, applying it to the discussions of global trade wars and trade agreements. A. Makarin, R. Rigobon 15.013 Economics for Strategic Decisions
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Prereq: 15.010 or 15.011 Units: 3-0-6 Credit cannot also be received for 15.0131
Applies principles of economics most relevant for corporate strategy to analysis of particular industries. Topics include market structure and its determinants; rational strategic behavior in small numbers situations; strategies for price and nonprice competition; dynamic pricing, output, and advertising decisions; entry and entry deterrence; competition with network externalities; investments under uncertainty; competition among platforms; R&D and patent licensing; and the growth and evolution of industries. Meets with 15.0131 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. R. Pindyck, A. Bonatti 15.0131 Economics for Strategic Decisions
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Prereq: 14.01 Units: 3-0-6 Credit cannot also be received for 15.013
Applies principles of economics most relevant for corporate strategy to analysis of particular industries. Topics include: market structure and its determinants; rational strategic behavior in small numbers situations; strategies for price and nonprice competition; dynamic pricing, output, and advertising decisions; entry and entry deterrence; competition with network externalities; investments under uncertainty; competition among platforms; R&D and patent licensing; and the growth and evolution of industries. Meets with 15.013 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Staff 15.014 Applied Macro- and International Economics II
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Prereq: None Units: 4-0-2
Establishes understanding of the development processes of societies and economies. Studies several dimensions of sustainability (environmental, social, political, institutional, economy, organizational, relational, and personal) and the balance among them. Explores the basics of governmental intervention, focusing on areas such as the judicial system, environment, social security, and health. Builds skills to determine what type of policy is most appropriate. Considers implications of new technologies on the financial sector: internationalization of currencies, mobile payment systems, and cryptocurrencies. Discusses the institutional framework to ensure choices are sustainable across all dimensions and applications. Staff 15.015 Macroeconomic Policy Reforms
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Prereq: Permission of instructor Units: 2-0-4
Focuses on the current policy and economic issues in the US economy. Students propose economic and policy reforms around issues such as labor markets, inflation and central banking, financial regulation, education, health, housing, transportation, social security, democracy, immigration, diversity, and environmental policy. Topics change year to year. In each class, proposals are presented and voted upon by the group. Staff 15.016[J] Climate and Energy in the Global Economy
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(Same subject as 14.450[J]) Prereq: 14.01, 15.010, 15.011, or 15.024 Units: 3-0-9 Credit cannot also be received for 14.45, 15.0161 You must participate in Sloan's Course Bidding to take this subject. Ends Dec 11. Lecture: MW1-2.30 (E51-315) +final
Provides students with a comprehensive understanding of the challenges, opportunities, and policy responses to the global climate and energy transitions. Discusses the role of energy in world economies, paying particular attention to low- and middle-income countries, as well as the impacts of climate change on those economies. Considers how energy access, cost, reliability, and environmental harm drive or hinder economic growth, the political influences on the energy sector, and the role of energy in mitigating future impacts of climate change. Also discusses global climate solutions, including the role of the United Nations Framework Convention on Climate Change process, trade policy, climate finance, business strategies to reduce emissions, and business strategies to help people adapt to a changing climate. Students taking graduate version complete additional assignments. C. Wolfram, N. Kala No textbook information available 15.0161[J] Climate and Energy in the Global Economy
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(Same subject as 14.45[J]) Prereq: 14.01 or 15.0111 Units: 3-0-9 Credit cannot also be received for 14.450, 15.016 Lecture: MW11.30-1 (E51-315) +final
Provides students with a comprehensive understanding of the challenges, opportunities, and policy responses to the global climate and energy transitions. Discusses the role of energy in world economies, paying particular attention to low- and middle-income countries, as well as the impacts of climate change on those economies. Considers how energy access, cost, reliability, and environmental harm drive or hinder economic growth, the political influences on the energy sector, and the role of energy in mitigating future impacts of climate change. Also discusses global climate solutions, including the role of the United Nations Framework Convention on Climate Change process, trade policy, climate finance, business strategies to reduce emissions, and business strategies to help people adapt to a changing climate. Students taking graduate version complete additional assignments. C. Wolfram, N. Kala No textbook information available 15.018 Current Debates of Macroeconomics and Public Policy
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Prereq: None Units: 3-0-6
Concentrates on debates about current policy challenges. Students debate and vote on policy actions on current issues in developed and developing nations. Subjects include industrial policy, macroeconomics, poverty, social safety net, labor practices, immigration and labor markets, international economics, human rights, civil rights, democracy, environmental policy, regulation, and crypto assets. Topics change from year to year. R. Rigobon 15.020 Economics of Energy, Innovation, and Sustainability
()Not offered regularly; consult department Prereq: 14.01 or 15.011 Units: 3-0-9 Credit cannot also be received for 14.43, 15.0201
Covers energy and environmental market organization and regulation. Explores economic challenges and solutions to transforming energy markets to be more efficient, accessible, affordable, and sustainable. Applies core economic concepts - consumer choice, firm profit maximization, and strategic behavior - to understand when energy and environmental markets work well and when they fail. They also conduct data-driven economic analysis on the trade-offs of real and proposed policy interventions. Topics include renewable generation sources for electricity, energy access in emerging markets, efficiency programs and fuel efficiency standards, transitioning transportation to alternative fuels, measuring damages and adaptation to climate change, and the effect of energy and environmental policy on innovation. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Staff 15.0201[J] Economics of Energy, Innovation, and Sustainability
() Not offered regularly; consult department (Same subject as 14.43[J]) Prereq: 14.01 or 15.0111 Units: 3-0-9 Credit cannot also be received for 15.020
Covers energy and environmental market organization and regulation. Explores economic challenges and solutions to transforming energy markets to be more efficient, accessible, affordable, and sustainable. Applies core economic concepts - consumer choice, firm profit maximization, and strategic behavior - to understand when energy and environmental markets work well and when they fail. They also conduct data-driven economic analysis on the trade-offs of real and proposed policy interventions. Topics include renewable generation sources for electricity, energy access in emerging markets, efficiency programs and fuel efficiency standards, transitioning transportation to alternative fuels, measuring damages and adaptation to climate change, and the effect of energy and environmental policy on innovation. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Staff 15.021[J] Real Estate Economics
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(Same subject as 11.433[J]) Prereq: 14.01, 15.010, or 15.011 Units: 4-0-8 Lecture: TR2.30-4 (9-354) Recitation: W EVE (4.30-6 PM) (9-354)
Develops an understanding of the fundamental economic factors that shape the market for real property, as well as the influence of capital markets in asset pricing. Analyzes of housing as well as commercial real estate. Covers demographic analysis, regional growth, construction cycles, urban land markets, and location theory as well as recent technology impacts. Exercises and modeling techniques for measuring and predicting property demand, supply, vacancy, rents, and prices. Bill Wheaton No textbook information available 15.022[J] Real Estate Markets: Macroeconomics
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(Same subject as 11.429[J]) Prereq: 11.431 or permission of instructor Units: 3-0-3
Applies the latest economic thinking and research to the task of analyzing aggregate real estate market time series, assessing risk, and developing forecasts. Presents the premise that because of capital durability and construction lags, real estate markets exhibit some degree of mean reversion and as such are at least partially predictable. Examines the extent and causes of market volatility across different markets and types of property. Long-term aggregate trends impacting the real estate sector, from demographics to technology, discussed. Limited to 30. W. Wheaton 15.024 Applied Economics for Managers
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Prereq: Permission of instructor Units: 3-0-6 Credit cannot also be received for 15.722
Develops facility with concepts, language and tools of micro economics. Primary focus on the analysis of markets, strategic interactions among firms and game theory as applied to firms. Emphasizes integration of theory, data, and judgment in the analysis of a wide range of corporate decisions, both between and within firms. Restricted to Sloan Fellow MBAs. T. Suri No textbook information available 15.025 Game Theory for Strategic Advantage
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(Subject meets with 15.0251) Prereq: 14.01, 15.010, 15.011, 15.024, or permission of instructor Units: 3-0-6 Credit cannot also be received for 15.741
Develops and applies principles of game theory relevant to managers' strategic decisions. Topics include how to reason about strategies and opponents; strategic commitment and negotiations; reputation and seemingly irrational actions; bidding in auctions; and the design of auctions, contests and markets. Applications to a variety of business decisions that arise in different industries, both within and outside the firm. Meets with 15.0251 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. A. Bonatti 15.0251 Game Theory for Strategic Advantage
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(Subject meets with 15.025) Prereq: 14.01, 15.0111, or permission of instructor Units: 3-0-6 Credit cannot also be received for 15.741
Develops and applies principles of game theory relevant to managers' strategic decisions. Topics include how to reason about strategies and opponents; strategic commitment and negotiations; reputation and seemingly irrational actions; bidding in auctions; and the design of auctions, contests and markets. Applications to a variety of business decisions that arise in different industries, both within and outside the firm. Meets with 15.025 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. A. Bonatti 15.026[J] Global Climate Change: Economics, Science, and Policy
()Not offered regularly; consult department (Same subject as 12.348[J]) Prereq: (Calculus II (GIR), 5.60, and 14.01) or permission of instructor Units: 3-0-6
Introduces scientific, economic, and ecological issues underlying the threat of global climate change, and the institutions engaged in negotiating an international response. Develops an integrated approach to analysis of climate change processes, and assessment of proposed policy measures, drawing on research and model development within the MIT Joint Program on the Science and Policy of Global Change. Graduate students are expected to explore the topic in greater depth through reading and individual research.. Staff 15.027 Opportunities in Developing Economies
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Prereq: None Units: 3-0-3
Investigates the role of the private sector in developing economies, highlighting how solving market failures can improve overall welfare. Covers constraints faced by firms in developing economies: contract enforcement, corruption, political risk, human rights, IP and infrastructure. Uses case studies to discuss successful firms and innovative solutions to these constraints, including public-private partnerships, the role of technology, the role of finance and impact investing. T. Suri 15.029[J] United States Energy Policy: Lessons Learned for the Future
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(Same subject as 5.81[J]) (Subject meets with 5.811[J], 15.0291[J]) Prereq: None Units: 2-0-4
Compares the US policy responses, from the Nixon administration to the current administration, on issues ranging from oil import dependence to nuclear nonproliferation. Examines what lessons were learned from these issues and how they have shaped the country's current climate change policy. Prepares students to be informed and effective participants in policy deliberations that require difficult decisions and trade-offs. Addresses both domestic and international policy aspects. Students taking graduate version complete additional assignments. J. Deutch 15.0291[J] United States Energy Policy: Lessons Learned for the Future
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(Same subject as 5.811[J]) (Subject meets with 5.81[J], 15.029[J]) Prereq: None Units: 2-0-4
Compares the US policy responses, from the Nixon administration to the current administration, on issues ranging from oil import dependence to nuclear nonproliferation. Examines what lessons were learned from these issues and how they have shaped the country's current climate change policy. Prepares students to be informed and effective participants in policy deliberations that require difficult decisions and trade-offs. Addresses both domestic and international policy aspects. Students taking graduate version complete additional assignments. J. Deutch 15.034 Business Experiments and Causal Analytics
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(Subject meets with 15.0341) Prereq: None Units: 4-0-5
<p class="xmsonormal">Looks at predicting and understanding the causal effects of decisions across finance, marketing, human resources, and general business strategy. Develops a toolkit to go beyond correlations and get to causality. Students gain a deeper understanding of designing experiments, modeling natural experiments using multivariate regression, instrumental-variable regression, and machine learning tools including regression trees, random forest, LASSO, and neural networks. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. J. Doyle, R. Rigobon 15.0341 Business Experiments and Causal Analytics
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(Subject meets with 15.034) Prereq: None Units: 4-0-5
<p class="xmsonormal">Looks at predicting and understanding the causal effects of decisions across finance, marketing, human resources, and general business strategy. Develops a toolkit to go beyond correlations and get to causality. Students gain a deeper understanding of designing experiments, modeling natural experiments using multivariate regression, instrumental-variable regression, and machine learning tools including regression trees, random forest, LASSO, and neural networks. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. J. Doyle, R. Rigobon 15.035 Energy Market Dynamics in a Decarbonizing Economy
(); second half of term
Prereq: 14.01 or permission of instructor Units: 2-0-4 Credit cannot also be received for 14.44, 14.444, 15.0351, 15.037, 15.038
Explores how energy markets function, what changes as the world decarbonizes, and the role of new technologies in this change. Examines how market outcomes are influenced by policies, with a focus on environmental policies. Uses economic tools to analyze efficiency and public policy challenges in interconnected energy and environmental markets. Topics include how electricity markets are shaped by large-scale renewable penetration, how decarbonization policies affect different regions and socio-economic groups, measuring the social costs of climate change, and the role of critical minerals in a decarbonizing world. Students gain experience in linking theory to real-world policy problems, particularly through a team-based electricity market simulation that mirrors decision-making by market participants. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Limited to 65. Staff 15.0351 Energy Market Dynamics in a Decarbonizing Economy
(); second half of term
Prereq: 14.01 or permission of instructor Units: 2-0-4 Credit cannot also be received for 14.44, 14.444, 15.035, 15.037, 15.038
Explores how energy markets function, what changes as the world decarbonizes, and the role of new technologies in this change. Examines how market outcomes are influenced by policies, with a focus on environmental policies. Uses economic tools to analyze efficiency and public policy challenges in interconnected energy and environmental markets. Topics include how electricity markets are shaped by large-scale renewable penetration, how decarbonization policies affect different regions and socio-economic groups, measuring the social costs of climate change, and the role of critical minerals in a decarbonizing world. Students gain experience in linking theory to real-world policy problems, particularly through a team-based electricity market simulation that mirrors decision-making by market participants. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Limited to 65. Staff 15.036[J] Dimensions of Geoengineering
(); first half of termNot offered regularly; consult department (Same subject as 1.850[J], 5.000[J], 10.600[J], 11.388[J], 12.884[J], 16.645[J]) Prereq: None Units: 2-0-4
Familiarizes students with the potential contributions and risks of using geoengineering technologies to control climate damage from global warming caused by greenhouse gas emissions. Discusses geoengineering in relation to other climate change responses: reducing emissions, removing CO2 from the atmosphere, and adapting to the impacts of climate change. Limited to 100. J. Deutch, M. Zuber 15.037[J] Energy Economics and Policy
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(Same subject as 14.44[J]) Prereq: 14.01 or 15.0111 Units: 4-0-8 Credit cannot also be received for 14.444, 15.035, 15.0351, 15.038
Analyzes business and public policy issues in energy markets and in the environmental markets to which they are closely tied. Examines the economic determinants of industry structure and evolution of competition among firms in these industries. Investigates successful and unsuccessful strategies for entering new markets and competing in existing markets. Industries studied include oil, natural gas, coal, electricity, and transportation. Topics include climate change and environmental policy, the role of speculation in energy markets, the political economy of energy policies, and market power and antitrust. Two team-based simulation games, representing the world oil market and a deregulated electricity market, act to cement the concepts covered in lecture. Students taking graduate version complete additional assignments. Limited to 60. C. Knittel 15.038[J] Energy Economics and Policy
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(Same subject as 14.444[J]) Prereq: 14.01 or 15.0111 Units: 4-0-8 Credit cannot also be received for 14.44, 15.035, 15.0351, 15.037
Analyzes business and public policy issues in energy markets and in the environmental markets to which they are closely tied. Examines the economic determinants of industry structure and evolution of competition among firms in these industries. Investigates successful and unsuccessful strategies for entering new markets and competing in existing markets. Industries studied include oil, natural gas, coal, electricity, and transportation. Topics include climate change and environmental policy, the role of speculation in energy markets, the political economy of energy policies, and market power and antitrust. Two team-based simulation games, representing the world oil market and a deregulated electricity market, act to cement the concepts covered in lecture. Students taking graduate version complete additional assignments. Limited to 60. C. Knittel 15.039[J] Organizational Economics
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(Same subject as 14.26[J]) (Subject meets with 14.260) Prereq: 14.01 Units: 4-0-8 Lecture: TR10.30-12 (E51-057) Recitation: F11 (E51-372)
Provides a rigorous, but not overly technical introduction to the economic theory of organization together with a varying set of applications. Addresses incentives, control, relationships, decision processes, and organizational culture and performance. Introduces selected fundamentals of game theory. Students taking graduate version complete additional assignments. A. Morjaria No textbook information available Operations Research/Statistics15.053 Optimization Methods in Business Analytics
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Prereq: 1.00, 1.000, 6.100A, or permission of instructor Units: 4-0-8
Introduces optimization methods with a focus on modeling, solution techniques, and analysis. Covers linear programming, network optimization, integer programming, nonlinear programming, and heuristics. Applications to logistics, manufacturing, statistics, machine learning, transportation, game theory, marketing, project management, and finance. Includes projects in which student teams solve optimization problems of practical interest. J. Orlin, T. Magnanti 15.054[J] The Airline Industry
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(Same subject as 1.232[J], 16.71[J]) Prereq: None Units: 3-0-9 Lecture: MW1-2.30 (4-237)
Overview of the global airline industry and its economic and operational foundations. Examines interactions among key stakeholders in the aviation system, including airlines, airports, and air traffic management. Introduces fundamentals of airline economics, including demand, cost structures, pricing, competition, and regulatory frameworks. Covers operational and strategic decision-making such as fleet and network planning and airline operations. Considers system-level challenges including safety, environmental sustainability, and evolving global market dynamics. Students analyze industry performance, market developments, and emerging operational challenges in aviation. F. Allroggen No textbook information available 15.060 Data, Models, and Decisions
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Prereq: Permission of instructor Units: 3-0-6 Credit cannot also be received for 15.730 You must participate in Sloan's Course Bidding to take this subject. Lecture: MW8.30-10 (ENDS DEC 11) (E62-262, E62-223, E51-325) or MW10-11.30 (ENDS DEC 11) (E62-262, E62-223, E51-325) Recitation: TBA +final
Introduces students to the basic tools in using data to make informed management decisions. Covers basic topics in data analytics, including introductory probability, decision analysis, basic statistics, regression, simulation, linear and discrete optimization, and introductory machine learning. Spreadsheet exercises, cases, and examples drawn from marketing, finance, operations management, and other management functions. Restricted to first-year Sloan master's students. C. Podimata, R. Ramakrishnan, E. Yao No textbook information available 15.062[J] Data Mining: Finding the Models and Predictions that Create Value
(); first half of termNot offered regularly; consult department (Same subject as IDS.145[J]) (Subject meets with 15.0621) Prereq: 15.060, 15.075, or permission of instructor Units: 2-0-4
Introduction to data mining, data science, and machine learning for recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, medical databases, etc. Topics include logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software (R and Python). Grading based on homework, cases, and a term project. Expectations and evaluation criteria differ for students taking the undergraduate version; consult syllabus or instructor for specific details. R. Welsch 15.0621 Data Mining: Finding the Models and Predictions that Create Value
(); first half of termNot offered regularly; consult department (Subject meets with 15.062[J], IDS.145[J]) Prereq: 15.075 or permission of instructor Units: 2-0-4
Introduction to data mining, data science, and machine learning for recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, medical databases, etc. Topics include logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software (R and Python). Grading based on homework, cases, and a term project. Expectations and evaluation criteria differ for students taking the graduate version; consult syllabus or instructor for specific details. R. Welsch 15.066[J] System Optimization and Analysis for Operations
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(Same subject as 2.851[J]) Prereq: Calculus II (GIR) Units: 4-0-8
Introduction to mathematical modeling, optimization, and simulation, as applied to manufacturing and operations. Specific methods include linear programming, network flow problems, integer and nonlinear programming, discrete-event simulation, heuristics and computer applications for manufacturing processes, operations and systems. Restricted to Leaders for Global Operations students. D. Freund No textbook information available 15.068 Statistical Consulting
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Prereq: 15.060 Units: 3-0-6
Addresses statistical issues as a consultant would face them: deciphering the client's question; finding appropriate data; performing a viable analysis; and presenting the results in compelling ways. Real-life cases and examples. Staff 15.069 Applied Probability and Statistics
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Prereq: Calculus I (GIR) Units: 4-0-8 Lecture: MW2.30-4 (E51-376) +final
Presents probability from the perspective of applied mathematics, with strong emphasis on an intuitive overview of key theorems and continuing demonstrations of their usefulness. Covers the laws of probability and numerous important discrete and continuous random variables, both individually and in combination. Introduces simulation. Offers an introduction to statistics that emphasizes its probabilistic foundations and the fact that statistical reasoning is applied common sense. Covers hypothesis testing, statistical sampling, and various forms of regression analysis. Draws applications from economics, finance, engineering, marketing, public policy, operations management, and operations research. A. Barnett No textbook information available 15.070[J] Discrete Probability and Stochastic Processes
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(Same subject as 6.7720[J], 18.619[J]) Prereq: 6.3702, 6.7700, 18.100A, 18.100B, or 18.100Q Units: 3-0-9
Provides an introduction to tools used for probabilistic reasoning in the context of discrete systems and processes. Tools such as the probabilistic method, first and second moment method, martingales, concentration and correlation inequalities, theory of random graphs, weak convergence, random walks and Brownian motion, branching processes, Markov chains, Markov random fields, correlation decay method, isoperimetry, coupling, influences and other basic tools of modern research in probability will be presented. Algorithmic aspects and connections to statistics and machine learning will be emphasized. Staff 15.071 The AI Edge
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Prereq: 15.060 or permission of instructor Units: 4-0-8 Credit cannot also be received for 15.0711 You must participate in Sloan's Course Bidding to take this subject. Ends Dec 11. Lecture: MW8.30-10 (E51-345) Recitation: F10 (E51-315)
Presents methods and applications of data science, artificial intelligence, and generative AI. Covers the core toolkit of machine learning ? regularized regression and classification, time series, tree-based methods ? and deep learning ? neural networks, transformers, large language models, and multi-modal AI. Follows a problem-driven approach using case studies in supply chains, finance, transportation, energy, healthcare, and digital platforms. Intended for students who aim to understand the foundations underpinning the AI revolution, design and deploy designing that create operational and strategic value, and lead AI projects in organizations. Includes a team project focused on designing an end-to-end AI solution and communicating results to technical and executive audiences. Prior familiarity with Python and foundational machine learning concepts recommended. Students taking graduate version complete additional assignments. Fall: A. Jacquillat Spring: H. Lu No textbook information available 15.0711 The Analytics Edge
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Prereq: 15.053 and 15.069 Units: 4-0-8 Credit cannot also be received for 15.071
Develops models and tools of data analytics that are used to transform businesses and industries, using examples and case studies in e-commerce, healthcare, social media, high technology, criminal justice, the internet, and beyond. Covers analytics methods such as linear regression, logistic regression, classification trees, random forests, neural networks, text analytics, social network analysis, time series modeling, clustering, and optimization. Uses mostly R programming language and some work in Jupyter notebooks. Includes team project. Meets with 15.071 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. H. Lu 15.072 Advanced Analytics Edge
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Prereq: Permission of instructor Units: 3-0-3 You must participate in Sloan's Course Bidding to take this subject. Ends Oct 23. Lecture: MW1-2.30 (E51-345) Recitation: F9 (E51-315)
Covers key topics in data science (in the context of analytics) ranging from descriptive, inferential statistics, and regressions analysis to modern predictive analytics (interpretable machine learning models, trees, rules, and flexible black-box methods). All methods and tools illustrated through real-world applications. Focuses on topics that are most relevant to the practical analysis of managerial decisions. Uses the R and Julia programming languages. Preference to Sloan Master of Business Analytics students. R. Mazumder No textbook information available 15.073[J] Applied Probability and Stochastic Models
()Not offered regularly; consult department (Same subject as 1.203[J], IDS.700[J]) Prereq: 6.3700 or 18.600 Units: 3-0-9
A vigorous use of probabilistic models to approximate real-life situations in Finance, Operations Management, Economics, and Operations Research. Emphasis on how to develop a suitable probabilistic model in a given setting and, merging probability with statistics, and on how to validate a proposed model against empirical evidence. Extensive treatment of Monte Carlo simulation for modeling random processes when analytic solutions are unattainable. Staff 15.075[J] Statistical Thinking and Data Analysis
()  Not offered regularly; consult department (Same subject as IDS.013[J]) Prereq: 6.3700 or 15.069 Units: 3-1-8
Introduces a rigorous treatment of statistical data analysis while helping students develop a strong intuition for the strengths and limitations of various methods. Topics include statistical sampling and uncertainty, estimation, hypothesis testing, linear regression, classification, analysis of variation, and elements of data mining. Involves empirical use of hypothesis testing and other statistical methodologies in several domains, including the assessment of A-B experiments on the web and the identification of genes correlated with diseases. Staff 15.076 Analytics for a Better World
()
Prereq: Calculus I (GIR) Units: 4-0-8
Introduces predictive and prescriptive analytics methods to solve problems that contribute to the welfare of society. Emphasis on using machine learning and optimization methods in innovative ways using real world data. Methods used include: linear and discrete optimization, linear and logistic regression, optimal classification and regression trees, deep learning, random forests, and boosted trees. Projects utilize Julia, Jump, and Tensor Flow. Assessment based on projects, including a capstone project. Restricted to undergraduates. Staff 15.077[J] Statistical Machine Learning and Data Science
()Not offered regularly; consult department (Same subject as IDS.147[J]) Prereq: Permission of instructor Units: 4-0-8
Advanced introduction to theory and application of statistics, data-mining and machine learning using techniques from management science, marketing, finance, consulting, and bioinformatics. Covers bootstrap theory of estimation, testing, nonparametric statistics, analysis of variance, experimental design, categorical data analysis, regression analysis, MCMC, and Bayesian methods. Focuses on data mining, supervised learning, and multivariate analysis. Topics chosen from logistic regression, principal components and dimension reduction; discrimination and classification analysis, trees (CART), partial least squares, nearest neighbors, regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software R, Python, and MATLAB. Grading based on homework, cases, and a term project. R. Welsch 15.081[J] Introduction to Mathematical Programming
()
(Same subject as 6.7210[J]) Prereq: 18.06 Units: 4-0-8 Lecture: TR1-2.30 (32-141) Recitation: F12 (2-105) +final
Introduction to linear optimization and its extensions emphasizing both methodology and the underlying mathematical structures and geometrical ideas. Covers classical theory of linear programming as well as some recent advances in the field. Topics: simplex method; duality theory; sensitivity analysis; network flow problems; decomposition; robust optimization; integer programming; interior point algorithms for linear programming; and introduction to combinatorial optimization and NP-completeness. P. Jaillet No textbook information available 15.083 Integer Optimization
()
Prereq: 6.7210 or 15.093 Units: 4-0-8
In-depth treatment of mixed-integer optimization. Topics include modeling techniques, combinatorial optimization, ideal formulations, cutting plane methods, branching algorithms, row generation algorithms, column generation algorithms, heuristic algorithms, and mixed-integer non-linear optimization. Instruction provided in modeling complex problems arising in practice; understanding the theory of integer optimization; knowing the core technologies employed within modern solvers; and developing algorithms to solve large-scale problems for which off-the-shelf solvers may not be sufficient. Examples drawn from a broad range of industries, such as transportation, energy, telecommunications, finance, product design, sports, and social networks. Includes a term project. A. Jacquillat 15.084[J] Nonlinear Optimization
()
(Same subject as 6.7220[J]) Prereq: 18.06 and (18.100A, 18.100B, or 18.100Q) Units: 4-0-8
Unified analytical and computational approach to nonlinear optimization problems. Unconstrained optimization methods include gradient, conjugate direction, Newton, sub-gradient and first-order methods. Constrained optimization methods include feasible directions, projection, interior point methods, and Lagrange multiplier methods. Convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. Comprehensive treatment of optimality conditions and Lagrange multipliers. Geometric approach to duality theory. Applications drawn from control, communications, machine learning, and resource allocation problems. Staff 15.085[J] Fundamentals of Probability
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(Same subject as 6.7700[J]) Prereq: Calculus II (GIR) Units: 4-0-8 Lecture: MW2.30-4 (45-230) Recitation: F1 (3-333) or F2 (3-333) +final
Introduction to probability theory. Probability spaces and measures. Discrete and continuous random variables. Conditioning and independence. Multivariate normal distribution. Abstract integration, expectation, and related convergence results. Moment generating and characteristic functions. Bernoulli and Poisson process. Finite-state Markov chains. Convergence notions and their relations. Limit theorems. Familiarity with elementary probability and real analysis is desirable. D. Gamarnik No textbook information available 15.086 Engineering Probability
(); first half of term
Prereq: Calculus I (GIR) and permission of instructor Units: 1-0-2
Introduction to applied probability. Makes real-life problems central to the pedagogy and aims for an intuitive understanding of probability as well as mastery of key probabilistic concepts and methods. Preference to first-year Leaders for Global Operations students. Staff No textbook information available 15.087 Engineering Statistics and Data Science
()
Prereq: Calculus II (GIR), 15.086, 18.06, and permission of instructor Units: 4-0-8
Develops ideas for making principled decisions and recommendations based on data, providing an introduction to statistical inference and statistical learning. Covers data displays and summary statistics for quantitative and qualitative data, the law of large numbers for means and empirical distributions, the normal distribution and the central limit theorem, confidence intervals, statistical hypothesis tests for the population mean and differences between population means, simple and multiple regression with quantitative data, model selection, the bias-variance tradeoff, logistic regression for binary outcomes, CART, random forests, gradient boosting, and deep learning. The statistical programming language R is used for in-class demonstrations and for out-of-class assignments. Preference to first-year Leaders for Global Operations students. No required textbook. Staff No textbook information available 15.089 Analytics Capstone
(, , ) 
Prereq: None Units arranged
Practical application of business analytics problems within a real company. Teams of 1-2 students, matched with company projects, visit companies to define project and scope. In class, students refine and improve on projects and devise methods for solving problems for their select companies. Mentors are assigned to each team. The culmination of the program is summer, on-site, practical training. Restricted to Master of Business Analytics students. IAP: M. Li, W. McEntee Spring: M. Li, W. McEntee Summer: M. Li, W. McEntee No textbook information available 15.090 Common Experience in Operations Research
()
Prereq: None Units arranged [P/D/F]
Provides students with experience working in teams on a data-driven ML project. After a week of classes that cover a range of tools (Keras, Google Collab, etc.) and deep learning technologies, students compete in teams in a jointly chosen Kaggle competition. Short homework assignments help students get acquainted with the required technologies, and regular presentations foster interactions within the ORC cohort. Restricted to Operations Research Center doctoral students. S. Gupta, A. Aouad No textbook information available 15.094[J] Robust Modeling, Optimization, and Computation
()
(Same subject as 1.142[J]) Prereq: 18.06 or permission of instructor Units: 4-0-8
Introduces modern robust optimization, including theory, applications, and computation. Presents formulations and their connection to probability, information and risk theory for conic optimization (linear, second-order, and semidefinite cones) and integer optimization. Application domains include analysis and optimization of stochastic networks, optimal mechanism design, network information theory, transportation, pattern classification, structural and engineering design, and financial engineering. Students formulate and solve a problem aligned with their interests in a final project. Staff 15.095 Machine Learning Under a Modern Optimization Lens
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Prereq: 6.7210, 15.093, or permission of instructor Units: 3-1-8 You must participate in Sloan's Course Bidding to take this subject. Lecture: MW4-5.30 (E51-345) Recitation: F11 (E51-345)
Develops algorithms for central problems in machine learning from a modern optimization perspective. Topics include sparse, convex, robust and median regression; an algorithmic framework for regression; optimal classification and regression trees, and their relationship with neural networks; how to transform predictive algorithms to prescriptive algorithms; optimal prescriptive trees; and robust classification. Also covers design of experiments, missing data imputations, mixture of Gaussian models, exact bootstrap, and sparse matrix estimation, including principal component analysis, factor analysis, inverse co-variance matrix estimation, and matrix completion. D. Bertsimas No textbook information available 15.097 Seminar in Statistics and Data Analysis
()Not offered regularly; consult department Prereq: Permission of instructor Units arranged
Group study of current topics related to statistics and data analysis. Staff 15.098 Seminar in Applied Probability and Stochastic Processes
() Not offered regularly; consult department Prereq: 6.3702 Units: 2-0-4
Doctoral student seminar covering current topics in applied probability and stochastic processes. Staff 15.099 Seminar in Operations Research
()
Prereq: None Units arranged You must participate in Sloan's Course Bidding to take this subject. Ends Dec 11. Lecture: TR EVE (5.30-7 PM) (E51-315) Recitation: F10 (E51-335) +final
Doctoral student seminar covering current topics related to operations research. D. Bertsimas, G. Stamou No textbook information available 15.110 Operations Research Experience Internship
()
Prereq: None Units arranged [P/D/F]
Required subject in which students engage in an off-campus internship where they build operations research models and work with data that addresses a real-world problem. Internship experience must be at least ten weeks in length and students must have a formal offer letter from their employer or organization. Requirements include a report summarizing how OR models and methods were used by the student participating in the internship and a letter from the internship advisor. Report must be submitted to the ORC academic administrator upon completion of the internship. Restricted to ORC students. Additional restrictions may apply. Staff No textbook information available For additional related subjects in Statistics, see: Civil and Environmental Engineering: 1.151, 1.155, 1.202, 1.203, and 1.205 Electrical Engineering and Computer Science: 6.041, 6.231, 6.245, 6.262, 6.431, and 6.435 Management: 15.034, 15.070, 15.075, and 15.098 Mathematics: 18.05, 18.175, 18.177, 18.440, 18.443, 18.445, and 18.465 See also: 2.830, 5.70, 5.72, 7.02, 8.044, 8.08, 10.816, 11.220, 16.322, 22.38, HST.191, and MAS.622 Health Care Management15.128[J] Revolutionary Ventures: How to Invent and Deploy Transformative Technologies
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(Same subject as 9.455[J], 20.454[J], MAS.883[J]) Prereq: Permission of instructor Units: 2-0-7 Lecture: R2-4 (E14-633)
Seminar on envisioning and building ideas and organizations to accelerate engineering revolutions. Focuses on emerging technology domains, such as neurotechnology, imaging, cryotechnology, gerontechnology, and bio-and-nano fabrication. Draws on historical examples as well as live case studies of existing or emerging organizations, including labs, institutes, startups, and companies. Goals range from accelerating basic science to developing transformative products or therapeutics. Each class is devoted to a specific area, often with invited speakers, exploring issues from the deeply technical through the strategic. Individually or in small groups, students prototype new ventures aimed at inventing and deploying revolutionary technologies. E. Boyden, J. Bonsen, J. Jacobson No textbook information available 15.136[J] Principles and Practice of Drug Development
()
(Same subject as 10.547[J], HST.920[J], IDS.620[J]) Prereq: Permission of instructor Units: 3-0-6 You must participate in Sloan's Course Bidding to take this subject. Lecture: W EVE (3-6 PM) (4-237)
Description and critical assessment of the major issues and stages of developing a pharmaceutical or biopharmaceutical. Drug discovery, preclinical development, clinical investigation, manufacturing and regulatory issues considered for small and large molecules. Economic and financial considerations of the drug development process. Multidisciplinary perspective from faculty in clinical; life; and management sciences; as well as industry guests. S. Finkelstein No textbook information available 15.137[J] Case Studies and Strategies in Drug Discovery and Development
()Not offered regularly; consult department (Same subject as 7.549[J], 20.486[J], HST.916[J]) Prereq: None Units: 2-0-4
Aims to develop appreciation for the stages of drug discovery and development, from target identification, to the submission of preclinical and clinical data to regulatory authorities for marketing approval. Following introductory lectures on the process of drug development, students working in small teams analyze how one of four new drugs or drug candidates traversed the discovery/development landscape. For each case, an outside expert from the sponsoring drug company or pivotal clinical trial principal investigator provides guidance and critiques the teams' presentations to the class. A. W. Wood 15.141[J] Economics and Analytics of Health Care Industries
()
(Same subject as HST.918[J]) Prereq: None Units: 3-0-6 Credit cannot also be received for 15.1411
Uses economics as a framework to provide a better understanding of the US healthcare landscape, as well as incentives for global health investments. Topics include differences between health care and other industries, the role of health insurance, regulatory issues and incentives for innovation, strategic issues in pricing and marketing, use of e-commerce and information technology, and formation and management of various alliances. Describes modern data analytics to measure value, including personalized/stratified therapies. Visiting speakers from industry and academia provide multiple expert viewpoints on these topics. Expectations and evaluation criteria differ for students taking the graduate version; consult syllabus or instructor for specific details. Staff 15.1411 Economics and Analytics of Health Care Industries
()
Prereq: None Units: 3-0-6 Credit cannot also be received for 15.141, HST.918
Uses economics as a framework to provide a better understanding of the US healthcare landscape, as well as incentives for global health investments. Topics include differences between health care and other industries, the role of health insurance, regulatory issues and incentives for innovation, strategic issues in pricing and marketing, use of e-commerce and information technology, and formation and management of various alliances. Describes modern data analytics to measure value, including personalized/stratified therapies. Visiting speakers from industry and academia provide multiple expert viewpoints on these topics. Expectations and evaluation criteria differ for students taking the undergraduate version; consult syllabus or instructor for specific details. Staff Global Economics & Management15.215 AI Foundations
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| | | 15.00-15.299 | | | 15.30-15.699 | | | 15.70-15.999 plus UROP and Thesis | | | |
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