Summer Sessions | Courses | Statistics

Statistics

The Statistics Department offers a range of courses that build on a foundation in probability and statistical theory to provide practical training in statistical methods, study design, and data analysis.

The courses on this page reflect Summer 2018 offerings. 

 

Courses
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Statistical Machine Learning
STAT S5241D 3 points.

Open to MA students in Statistics only


The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 5241 001/70583 M Tu W Th 2:45p - 4:20p
417 MATHEMATICS BUILDING
Gabriel Young 3 Open
Introduction to Probability and Inference
STAT S4001D 3 points.

Life is a gamble and with some knowledge of probability / statistics is easier evaluate the risks and rewards involved.  Probability theory allows us take a known underlying model and estimate how likely will we be able to see future events. Statistical Inference allows us to take data we have seen and estimate the missing parts of an unknown model. The first part of the course focus on the former and the second part the latter.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4001 001/11426 M Tu W Th 4:30p - 6:05p
503 HAMILTON HALL
David Rios 3 Open
Introduction to Statistics (with calculus)
STAT S1201D 3 points.

Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value,confidence intervals. maximum likelihood estimation. Satisfies the pre-requisites for ECON W3412.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 1201 001/63679 M Tu W Th 6:15p - 7:50p
327 SEELEY W. MUDD BUILDING
Yayun Hsu 3 Open
Introduction to Statistics (without calculus)
STAT S1101D 3 points.

Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 1101 001/72590 M Tu W Th 4:30p - 6:05p
903 SCHOOL OF SOCIAL WORK
Guanhua Fang 3 Open
Probability
STAT S4203D 3 points.

This course can be taken as a single course for students requiring knowledge of probability or as a foundation for more advanced courses. It is open to both undergraduate and master students. This course satisfies the prerequisite for STAT W3107 and W4107. Topics covered include combinatorics, conditional probability, random variables and common distributions, expectation, independence, Bayes' rule, joint distributions, conditional expectations, moment generating functions, central limit theorem, laws of large numbers, characteristic functions.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4203 001/28033 M Tu W Th 6:15p - 7:50p
903 SCHOOL OF SOCIAL WORK
Young Kim 3 Open
Statistical Computing and Introduction to Data Science
STAT S5206D 3 points.

Open to MA students in Statistics only


Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction.  Writing code for numerical and graphical statistical analyses.  Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets.  Examples from data science will be used for demonstration.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 5206 001/21546 M Tu W Th 4:30p - 6:05p
203 MATHEMATICS BUILDING
Linxi Liu 3 Open
Statistical Computing and Introduction to Data Science
STAT S4206D 3 points.

Open to CC, CN, GS, GN, BC, EN, GSAS, GSAS Liberal, and SEAS Graduate Students


Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction.  Writing code for numerical and graphical statistical analyses.  Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets.  Examples from data science will be used for demonstration.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4206 001/19122 M Tu W Th 4:30p - 6:05p
203 MATHEMATICS BUILDING
Linxi Liu 3 Open
Statistical Computing in SAS
STAT S4199D 3 points.

Data handling in SAS (including SAS INPUT, reading and writing raw and system files, data set subsetting, concatenating, merging, updating and working with arrays), SAS MACROS, common SAS functions, and graphics in SAS. Review of SAS tools for exploratory data analysis, and common statistical procedures (including, categorical data, dates and longitudinal data, correlation and regression, nonparametric comparisons, ANOVA, multiple regression, multivariate data analysis).

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4199 001/71374 M Tu W Th 2:45p - 4:20p
252 ENGINEERING TERRACE
Anthony Donoghue 3 Open
Statistical Machine Learning
STAT S4241D 3 points.

The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4241 001/70158 M Tu W Th 2:45p - 4:20p
417 MATHEMATICS BUILDING
Gabriel Young 3 Open
Statistical Methods for Finance
STAT S4261D 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4261 001/26817 M Tu W Th 6:15p - 7:50p
312 MATHEMATICS BUILDING
Pawel Polak 3 Open
Statistical Methods for Finance
STAT S5261D 3 points.

Open to MA students in Statistics only


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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 5261 001/68967 M Tu W Th 6:15p - 7:50p
312 MATHEMATICS BUILDING
Pawel Polak 3 Open
Time Series Analysis
STAT S4221D 3 points.

Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4221 001/10210 M Tu W Th 6:15p - 7:50p
222 PUPIN LABORATORIES
Abolfazal Safikhani 3 Open
Introduction to Statistics (with calculus)
STAT S1201Q 3 points.

Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value,confidence intervals. maximum likelihood estimation. Satisfies the pre-requisites for ECON W3412.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 1201 002/20338 M Tu W Th 4:30p - 6:05p
903 SCHOOL OF SOCIAL WORK
Miguel Garrido Garcia 3 Open
Introduction to Statistics (without calculus)
STAT S1101Q 3 points.

Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 1101 002/29249 M Tu W Th 6:15p - 7:50p
903 SCHOOL OF SOCIAL WORK
Alessandro Anto Grande 3 Open
Statistical Inference
STAT S4204Q 3 points.

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.

Course
Number
Section/Call
Number
Times/Location Instructor Points Enrollment
STAT 4204 001/62463 M Tu W Th 6:15p - 7:50p
312 MATHEMATICS BUILDING
Ji Meng Loh 3 Open