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Course Criteria
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4.00 Credits
No course description available.
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8.00 Credits
No course description available.
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4.00 Credits
Introduction to key ideas underlying statistical and quantitative reasoning. Topics covered: methods for organizing, summarizing and displaying data; elements of sample surveys, experimental design and observational studies; methods of parameter estimation and hypothesis testing in one- and two-sample problems; regression with one or more predictors; correlation; and analysis of variance. Explores applications in a wide range of fields, including the social and political sciences, medical research, and business and economics.
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4.00 Credits
Similar to Statistics 100, but emphasizes concepts and practice of statistics used in psychology and other social and behavioral sciences. Topics covered: describing center and variability; probability and sampling distributions; estimation and hypothesis testing for comparing means and comparing proportions; contingency tables; correlation and regression; multiple regression; analysis of variance. Emphasis on translation of research questions into statistically testable hypotheses and models, and interpretation of results in context.
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4.00 Credits
Similar to Statistics 100, but emphasizes applications in fields including, but not limited to, economics, health sciences and policy analysis. Topics covered: descriptive and summary statistics for both measured and counted variables; elements of experimental and survey design; probability; and statistical inference including estimation and tests of hypotheses as applied to one- and two-sample problems, multiple regression, correlation, and analysis of variance. Taught at a slightly higher level than Statistics 100 and 101.
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4.00 Credits
This course introduces the technical skills required for data-driven analysis of business and financial data. Emphasis is placed on applying statistical methods to summarize and make inferences from complex data and to develop quantitative models to assist business decision making. The software packages Excel and R will be used to obtain quantitative solutions to financial problems. Topics include: understanding the concept of risk, portfolio construction and analysis, valuing options, testing trading systems, and simulation techniques.
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4.00 Credits
A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes' Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.
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4.00 Credits
Basic concepts of statistical inference from frequentist and Bayesian perspectives. Topics include maximum likelihood methods, confidence and Bayesian interval estimation, hypothesis testing, least squares methods and categorical data analysis.
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4.00 Credits
The course will cover basic technology platforms, data analysis problems and algorithms in computational biology. Topics include sequence alignment and search, high throughput experiments for gene expression, transcription factor binding and epigenetic profiling, motif finding, RNA/protein structure prediction, proteomics and genome-wide association studies. Computational algorithms covered include hidden Markov model, Gibbs sampler, clustering and classification methods.
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4.00 Credits
An introduction to modern financial derivative markets and the probabilistic and statistical techniques used to navigate them. Methodology will largely be motivated by real problems from the financial industry. Topics include: interest-rates; forward and futures contracts; option markets and probabilistic valuation methods; interest-rate derivatives and structured notes; electronic trading and performance evaluation. Designed for those seeking an understanding of the quantitative challenges on Wall Street and the probabilistic tool-kit developed to address them.
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