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Course Criteria
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3.00 Credits
Data Mining is a dynamic and fast growing field at the interface of Statistics and Computer Science. The emergence of massive datasets containing millions or even billions of observations provides the primary impetus for the field. Such datasets arise, for instance, in large-scale retailing, telecommunications, astronomy, computational and statistical challenges. This course will provide an overview of current practice in data mining. Specific topics covered with include databases and data warehousing, exploratory data analysis and visualization, descriptive modeling, predictive modeling, pattern and rule discovery, text mining, Bayesian data mining, and causal inference. The use of statistical software will be emphasized.
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6.00 Credits
Prerequisites: MATH V1101 or permission of the instructor. A fast-paced coverage of those aspects of the differential and integral calculus of one and several variables and of the linear algebra required for the core courses in the Statistics major. The mathematical topics are integrated with an introduction to computing. Students seeking more comprehensive background should consider replacing this course with MATH V1102 and V2010, and one of COMS W1003, W1004, or W1007.
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3.00 Credits
Prerequisites: MATH V1101 and V1102 or the equivalent A calculus-based introduction to probability theory. A quick review of multivariate calculus is provided. Topics covered include random variables, conditional probability, expectation, independence, Bayes' rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markov's inequality.
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3.00 Credits
Prerequisites: STAT W3105 or W4105, or the equivalent. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.
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3.00 Credits
Prerequisites: STAT W3107 (or STAT W4150) and STAT W3103 (or MATH V1101, V1102, and V2110). Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data. Equivalent to STAT W4315 except that enrollment is limited to undergraduate students.
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3.00 Credits
Prerequisites: STAT W4315. At least one of W4290, W4325, W4330, W4437, W4413, W4543 is recommended. This is a course on getting the most out of data. The emphasis will be on hands-on experience, involving case studies with real data and using common statistical packages. The course covers, at a very high level, exploratory data analysis, model formulation, goodness of fit testing, and other standard and non-standard statistical procedures, including linear regression, analysis of variance, nonlinear regression, generalized linear models, survival analysis, time series analysis, and modern regression methods. Students will be expected to propose a data set of their choice for use as case study material.
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3.00 Credits
Prerequisites: COMS W1003, W1004, W1005, W1007, or the equivalent. Corequisites: Either STAT W3105 or W4105, and either STAT W3107 or W4107. Data Mining is a dynamic and fast growing field at the interface of Statistics and Computer Science. The emergence of massive datasets containing millions or even billions of observations provides the primary impetus for the field. Such datasets arise, for instance, in large-scale retailing, telecommunications, astronomy, computational and statistical challenges.
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3.00 Credits
Prerequisites: STAT W3107 or W4107. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.
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3.00 Credits
Prerequisites: STAT W4315 Statistical methods for rates and proportions, ordered and nominal categorical responses, contingency tables, odds-ratios, exact inference, logistic regression, Poisson regression, generalized linear models.
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3.00 Credits
Prerequisites: STAT W3107 or W4107. Introductory course on the design and analysis of sample surveys. How sample surveys are conducted, why the designs are used, how to analyze survey results, and how to derive from first principles the standard results and their generalizations. Examples from public health, social work, opinion polling, and other topics of interest.
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