3 Classes (27 Units)15.060 (9), 15.0621 (6), ESD.86J (12)
15.060 Data, Models, and Decisions
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.
This class has no prerequisites.
Lecture occurs 10:00 AM to 11:30 AM on Mondays and Wednesdays in E62-223.
This class counts for a total of 9 credits.
15.0621 Data Mining: Finding the Models and Predictions that Create Value
Introduction to data mining, data science, and machine learning, methods that assist in recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, pointof-sale devices, bar-code readers, medical databases, and other sources. Topics include logistic regression, association rules, treestructured 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 focusing on R. Term project required. Meets with 15.062 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
This class has 15.075 as a prerequisite.
15.0621 will not be offered this semester. It will be available in the Fall semester, and will be instructed by R. E. Welsch.
Lecture occurs 4:00 PM to 5:30 PM on Mondays and Wednesdays in E51-345.
This class counts for a total of 6 credits.
You can find more information at the MIT + 15.0621 - Google Search site.
ESD.86J Models, Data and Inference for Socio-Technical Systems
Use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Enhance model-building skills, including: review and extension of functions of random variables, Poisson processes, and Markov processes. Move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables. Review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. Class project.
Lecture occurs 10:30 AM to 12:00 PM on Mondays and Wednesdays in E51-372.
This class counts for a total of 12 credits. This is a graduate-level class.
In the Spring 2015 Subject Evaluations, ESD.86J was rated 5.4 out of 7.0. You can find more information at the http://www.google.com/search?&q=MIT+%2B+ESD.86&btnG=Google+Search&inurl=https site.
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