|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
4.00 Credits
Theoretical and empirical perspectives on individual and industrial demand for energy, energy supply, energy markets, and public policies affecting energy markets. Discusses aspects of the oil, natural gas, electricity, and nuclear power sectors. Examines energy tax, price regulation, deregulation, energy efficiency and policies for controlling pollution and CO2 emissions. Students taking the graduate version complete additional assignments. Limited to 60.
Prerequisite:
Prereq: 14.01
-
0.00 - 6.00 Credits
Group study of current topics related to managerial economics.
Prerequisite:
Prereq: 15.010, 15.012
-
4.00 Credits
Introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance.
Prerequisite:
Prereq: None
-
3.00 Credits
Overview of the global airline industry, focusing on recent industry performance, current issues and challenges for the future. Fundamentals of airline industry structure, airline economics, operations planning, safety, labor relations, airports and air traffic control, marketing, and competitive strategies, with an emphasis on the interrelationships among major industry stakeholders. Recent research findings of the MIT Global Airline Industry Program are showcased, including the impacts of congestion and delays, evolution of information technologies, changing human resource management practices, and competitive effects of new entrant airlines. Taught by faculty participants of the Global Airline Industry Program.
Prerequisite:
Prereq: None
-
4.00 Credits
Provides an introduction to optimization, building upon the fundamentals of linear algebra. Covers optimization methodologies, including linear programming, network optimization, integer programming, decision trees, and dynamic programming. Applications to logistics, manufacturing, transportation, marketing, project management, and finance.
Prerequisite:
Prereq: 18.06 or permission of instructor
-
3.00 Credits
Introduces students to the basic tools in using data to make informed management decisions. Covers introductory probability, decision analysis, basic statistics, regression, simulation, linear and nonlinear optimization, and discrete optimization. Computer spreadsheet exercises, cases, and examples drawn from marketing, finance, operations management, and other management functions. Restricted to first-year Sloan master's students.
Prerequisite:
Prereq: Permission of instructor
-
2.00 Credits
Provides an introduction to data mining (i.e., machine learning), a class of methods that that assist in recognizing patterns and making intelligent use of massive amounts of electronic data collected via the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, medical databases, search engines, and social networks. Topics selected from logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in areas such as credit ratings, fraud detection, marketing, customer relationship management, and investments. Introduces data-mining software.
Prerequisite:
Prereq: 15.060 or 15.075
-
3.00 Credits
Introduces statistical tools and communication skills for using data to influence management decisions. In real-life decisions, decision-makers use both analytical and intuitive approaches to understand problems and to persuade others to act. Statistical tools are important, but statistical arguments are often met with skepticism. Covers decision analysis, communication principles, probability, testing theories, statistical sampling and regression, and misuses of statistics, with exercises and examples drawn from marketing, finance, operations management, strategy, and law. Restricted to MIT Sloan Fellows in Innovation and Global Leadership.
Prerequisite:
Prereq: Permission of instructor
-
4.00 Credits
Modeling and analysis of uncertainty and variation. Covers probability models and distributions, regression, and basic statistical procedures pertinent to manufacturing and operations. Introduces experimental and robust design, statistical process control, forecasting, and data-mining. Students use a data analysis package, such as JMP, Minitab, or MATLAB. Primarily for Leaders for Global Operations students.
Prerequisite:
Prereq: Calculus II (GIR)
-
4.00 Credits
Introduction to mathematical modeling, optimization, and simulation, as applied to manufacturing. Specific methods include linear programming, network flow problems, integer and nonlinear programming, discrete-event simulation, heuristics and computer applications for manufacturing processes and systems. Restricted to Leaders for Global Operations students.
Prerequisite:
Prereq: Calculus II (GIR)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|