Statistics

The Statistics Department Office:
1005 School of Social Work (1255 Amsterdam Avenue); 212-851-2132
http://www.stat.columbia.edu

Statistics Major and Concentration Advising:
Banu Baydil, 611 Watson; 212-853-1397; bb2717@columbia.edu
Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu

Data Science Major Advising:
Computer Science: Adam Cannon, 459 CSB; 212-939-7016; cannon@cs.columbia.edu
Statistics: Banu Baydil, 611 Watson; 212-853-1397; bb2717@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu

Economics - Statistics Major Advising:
Economics: Susan Elmes, 1006 IAB; 212-854-9124; se5@columbia.edu
Statistics: Banu Baydil, 611 Watson; 212-853-1397; bb2717@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu

Mathematics - Statistics Major Advising:
Mathematics: Julien Dubedat, 601 Mathematics; 212-854-8806; jd2653@columbia.edu
Statistics: Banu Baydil, 611 Watson; 212-853-1397; bb2717@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu

Political Science - Statistic Major Advising:
Political Science: Robert Shapiro, 730 IAB; 212-854-3944; rys3@columbia.edu
Statistics: Banu Baydil, 611 Watson; 212-853-1397; bb2717@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu

Department Administrator:
Dood Kalicharan, 1003 School of Social Work; 212-851-2130; dk@stat.columbia.edu

The Department offers several introductory courses.  Students interested in statistical concepts, who plan on consuming, but not creating statistics, should take STAT UN1001 Introduction to Statistical Reasoning.  The course is designed for students who have taken a pre-calculus course, and the focus is on general principles.  It is suitable for students seeking to satisfy the Barnard quantitative reasoning requirements.  Students seeking an introduction to applied statistics should take STAT UN1101 Introduction to Statistics.  The course is designed for students who have some mathematical maturity, but who may not have taken a course in calculus, and the focus is on the elements of data analysis.  It is recommended for pre-med students, and students contemplating the concentration in statistics.  Students seeking a foundation for further study of probability theory and statistical theory and methods should take STAT UN1201 Calculus-Based Introduction to Statistics.   The course is designed for students who have taken a semester of college calculus or the equivalent, and the focus is on preparation for a mathematical study of probability and statistics.   It is recommended for students seeking to complete the prerequisite for econometrics, and for students contemplating the major in statistics.  Students seeking a one-semester calculus-based survey of probability theory and statistical theory and methods should take STAT GU4001 Introduction to Probability and Statistics.  This course is designed for students who have taken calculus, and is meant as a terminal course.  It provides a somewhat abridged version of the more demanding sequence STAT GU4203 PROBABILITY THEORY and STAT GU4204 Statistical Inference.  While some mathematically mature students take the more demanding sequence as an introduction to the field, it is generally recommended that students prepare for the sequence by taking STAT UN1201 Calculus-Based Introduction to Statistics.

The Department offers the Major in Statistics, the Concentration in Statistics, and interdisciplinary majors with Computer Science, Economics, Mathematics, and Political Science.  The concentration is suitable for students preparing for work or study where substantial skills in data analysis are valued and may be taken without mathematical prerequisites.  The concentration consists of a sequence of six courses in applied statistics, but students may substitute statistics electives numbered 4203 or above with permission of the concentration advisors.  The major consists of mathematical and computational prerequisites, an introductory course, and five core courses in probability theory and theoretical and applied statistics together with three electives.  The training in the undergraduate major is comparable to a masters degree in statistics.  

Students may wish to consult the following guidelines when undertaking course planning.  It is advisable to take STAT UN1101 Introduction to Statistics and STAT UN2102 Applied Statistical Computing before taking any of the more advanced concentration courses, STAT UN2103 Applied Linear Regression Analysis, STAT UN2104 Applied Categorical Data Analysis, STAT UN3105 Applied Statistical Methods, and STAT UN3106 Applied Data Mining.  It is advisable to take STAT UN1201 Calculus-Based Introduction to StatisticsSTAT GU4203 PROBABILITY THEORY, STAT GU4204 Statistical Inference, and STAT GU4205 Linear Regression Models in sequence.  Courses in stochastic analysis should be preceded by STAT GU4203 PROBABILITY THEORY, and for many students, it is advisable to take STAT GU4207 Elementary Stochastic Processes before embarking on STAT GU4262 Stochastic Processes for Finance, STAT GU4264 Stochastic Processes and Applications, or STAT GU4265 Stochastic Methods in Finance.  Most of the statistics courses numbered from 4221 to 4234 are best preceded by STAT GU4205 Linear Regression Models.  The data science courses STAT GU4206 Statistical Computing and Introduction to Data Science, STAT GU4241 Statistical Machine Learning, and STAT Q4242 Advanced Machine Learning should be taken in sequence. 

Advanced Placement

The Department offers three points of advanced credit for a score of 5 on the AP statistics exam.  Students who are required to take an introductory statistics course for their major should check with their major advisor to determine whether this credit provides exemption from their requirement.

Departmental Honors

Students are considered for department honors on the basis of GPA and the comprehensiveness and difficulty of their course work in the Department.  The Department is generally permitted to nominate one tenth of graduating students for departmental honors.

Undergraduate Research in Statistics and the Summer Internship

Matriculated students who will be undergraduates at Columbia College, Barnard College, the School of General Studies, or the School of Engineering and Applied Sciences may apply to the Department's summer internship program.  The internship provides summer housing and a stipend.  Students work with Statistics Department faculty mentors.  Applicants should send a brief statement of interest and a copy of their transcript to Ms. Dood Kalicharan in the Statistics Department office by the end of March to be considered.  If summer project descriptions are posted on the Department's website, please indicate in the statement of interest which project is of interest.  Students seeking research opportunities with Statistics Department faculty during the academic year are advised to be entrepreneurial and proactive: identify congenial faculty whose research is appealing, request an opportunity to meet, and provide some indication of previous course work when asking for a project.

Professors

  • David Blei (with Computer Science)
  • Mark Brown
  • Richard R. Davis
  • Victor H. de la Peña
  • Andrew Gelman (with Political Science)
  • Shaw-Hwa Lo
  • David Madigan
  • Ioannis Karatzas (Mathematics)
  • Liam Paninski
  • Philip Protter
  • Michael Sobel
  • Daniel Rabinowitz
  • Zhiliang Ying

Associate Professors

  • Jose Blanchet (with Industrial Engineering and Operations Research)
  • Jingchen Liu
  • Bodhisattva Sen
  • Tian Zheng

Assistant Professors

  • John Cunningham
  • Yang Feng
  • Lauren Hannah
  • Arian Maleki
  • Sumit Mukherjee
  • Marcel Nutz
  • Peter Orbanz

Term Assistant Professors

  • Yuchong Zhang
  • Abolfazl Safikhani

Adjunct Professors

  • Demissie Alemayehu
  • Flavio Bartmann
  • Guy Cohen
  • Regina Dolgoarshinnykh
  • Anthony Donoghue
  • Hammou Elbarmi
  • Birol Emir
  • Irene Hueter
  • James Landwehr
  • Ha Nguyen

Lecturers in Discipline

  • Banu Baydil
  • Ronald Neath
  • David Rios
  • Gabriel Young
  •  

Major in Statistics

The requirements for this program were modified in March 2016. Students who declared this program before this date should contact the director of undergraduate studies for the department in order to confirm their options for major requirements.

The major should be planned with the director of undergraduate studies. Courses in which the grade of D has been received do not count toward the major. The requirements for the major are as follows:

Mathematics and Computer Science Prerequisites
MATH UN1101Calculus I
MATH UN1102Calculus II
MATH UN1201Calculus III
MATH UN2010Linear Algebra
One of the following five courses
Honors Introduction to Computer Science
Introduction to Computing for Engineers and Applied Scientists
Introduction to Computer Science and Programming in MATLAB
Applied Statistical Computing
Introduction to Computer Science and Programming in Java
Core courses in probability and statistics
STAT UN1201Calculus-Based Introduction to Statistics
STAT GU4203PROBABILITY THEORY
STAT GU4204Statistical Inference
STAT GU4205Linear Regression Models
STAT GU4206Statistical Computing and Introduction to Data Science
STAT GU4207Elementary Stochastic Processes
Three approved electives in statistics or, with permission, a cognate field.
  • Students preparing for a career in actuarial science are encouraged to replace STAT GU4205 Linear Regression Models with STAT GU4282 Linear Regression and Time Series Methods, and should take as one of their electives STAT GU4281 Theory of Interest.
  • Students preparing for graduate study in statistics are encouraged to replace two electives with MATH GU4061 Introduction To Modern Analysis I and  MATH GU4062 Introduction To Modern Analysis II .

Concentration in Statistics

Courses in which the grade of D has been received do not count towards the concentration. The requirements for the concentration are as follows.

STAT UN1101Introduction to Statistics
STAT UN2102Applied Statistical Computing
STAT UN2103Applied Linear Regression Analysis
STAT UN2104Applied Categorical Data Analysis
STAT UN3105Applied Statistical Methods
STAT UN3106Applied Data Mining
  • Students may replace courses required for the concentration by approved Statistics Department courses.

Minor in Statistics

Courses in which the grade of D has been received do not count towards the minor. The requirements for the minor are as follows.

STAT UN1101Introduction to Statistics
STAT UN2102Applied Statistical Computing
STAT UN2103Applied Linear Regression Analysis
STAT UN2104Applied Categorical Data Analysis
STAT UN3105Applied Statistical Methods
STAT UN3106Applied Data Mining
  • Students may replace courses required for the minor by approved Statistics Department courses.

Major in Data Science

In response to the ever growing importance of "big data" in scientific and policy endeavors, the last few years have seen an explosive growth in theory, methods, and applications at the interface between computer science and statistics. The Statistics Department and the Department of Computer Science have responded with a joint-major that emphasizes the interface between the disciplines.

Mathematical Prerequisites
Calculus I
Calculus II
Calculus III
Linear Algebra
Statistics Required Courses
Calculus-Based Introduction to Statistics
PROBABILITY THEORY
Statistical Inference
Linear Regression Models
Statistical Machine Learning
Statistics Electives
Select two of the following courses:
Applied Data Mining
Statistical Computing and Introduction to Data Science
Applied Data Science
Bayesian Statistics
Advanced Machine Learning
Computer Science Introductory Courses
Select one of the following courses:
Introduction to Computer Science and Programming in Java
Introduction to Computer Science and Programming in MATLAB
Introduction to Computing for Engineers and Applied Scientists
Honors Introduction to Computer Science
And select one of the following courses:
Data Structures in Java
Data Structures with C/C++
Honors Data Structures and Algorithms
Computer Science Required Courses
Discrete Mathematics: Introduction to Combinatorics and Graph Theory
Analysis of Algorithms I
Computer Science Electives
Select three of the following courses:
Computer Science Theory
Introduction to Computational Complexity
Introduction to Computational Learning Theory
Introduction to Databases
Principles and Practice of Parallel Programming
Any COMS W47xx course EXCEPT W4771

Major in Economics-Statistics

Please read Requirements for all Economics Majors, Concentrators, and Interdepartmental Majors in the Economics section of this Bulletin.

Please read Requirements for all Economics Majors, Concentrators, and Interdepartmental Majors in the Economics section of this Bulletin.

The major in Economics-Statistics provides students with a grounding in economic theory comparable to that of the general economics major, but also exposes students to a more rigorous and extensive statistics training. This program is recommended for students with strong quantitative skills and for those contemplating graduate studies in economics.

Two advisers are assigned for the interdepartmental major, one in the Department of Economics and one in the Department of Statistics. The economics adviser can only advise on economics requirements and the statistics adviser can only advise on statistics requirements.

Students who declare prior to Spring 2014:

The economics-statistics major requires a total of 53 points: 23 in economics, 15 points in statistics, 12 points in mathematics, and 3 points in computer science, as follows:

Economics Core Courses
Complete the Economics core courses.
Economics Electives
Select two electives at the 3000-level or above, of which no more than one may be a Barnard course.
Mathematics
Select one of the following sequences:
Calculus I
Calculus II
Calculus III
Linear Algebra
or
Honors Mathematics A
Honors Mathematics B
Statistics
STAT UN1201Calculus-Based Introduction to Statistics
STAT GU4203PROBABILITY THEORY
STAT GU4204Statistical Inference
STAT GU4205Linear Regression Models
One elective from among courses numbered STAT GU4206 through GU4266.
Computer Science
Select one of the following courses:
Introduction to Computer Science and Programming in Java
Introduction to Computer Science and Programming in MATLAB
Honors Introduction to Computer Science
Applied Statistical Computing
Seminar
ECON GU4918Seminar In Econometrics

Students who declare in Spring 2014 or beyond:

In addition to the above requirements, students are required to take three ECON electives at the 3000-level or above.


Major in Mathematics-Statistics

The program is designed to prepare the student for: (1) a career in industries such as finance and insurance that require a high level of mathematical sophistication and a substantial knowledge of probability and statistics; and (2) graduate study in quantitative disciplines. Students choose electives in finance, actuarial science, operations research, or other quantitative fields to complement requirements in mathematics, statistics, and computer science.

Mathematics
Select one of the following sequences:
MATH UN1101
 - MATH UN1102
 - MATH UN1201
 - MATH UN2010
 - MATH UN2500
Calculus I
and Calculus II
and Calculus III
and Linear Algebra
and Analysis and Optimization
Honors Mathematics A
and Honors Mathematics B
and Analysis and Optimization
Statistics required courses
STAT UN1201Calculus-Based Introduction to Statistics
STAT GU4203PROBABILITY THEORY
STAT GU4204Statistical Inference
STAT GU4205Linear Regression Models
And select one of the following courses:
STAT GU4207Elementary Stochastic Processes
STAT GU4262Stochastic Processes for Finance
STAT GU4264Stochastic Processes and Applications
STAT GU4265Stochastic Methods in Finance
Computer Science
Select one of the following courses:
Introduction to Computer Science and Programming in Java
Introduction to Computer Science and Programming in MATLAB
Introduction to Computing for Engineers and Applied Scientists
Honors Introduction to Computer Science
or an advanced Computer Science offering in programming
Electives
An approved selection of three advanced courses in mathematics, statistics, applied mathematics, industrial engineering and operations research, computer science, or approved mathematical methods courses in a quantitative discipline. At least one elective must be a Mathematics Department course numbered 3000 or above.
  • Students interested in modeling applications are recommended to take MATH UN3027 Ordinary Differential Equations and MATH UN3028 Partial Differential Equations
  • Students interested in finance are recommended to include among their electives,MATH GR5010 Introduction to the Mathematics of FinanceSTAT GU4261 Statistical Methods in Finance, and STAT GU4221 Time Series Analysis.
  • Students interested in graduate study in mathematics or in statistics are recommended to take MATH GU4061 Introduction To Modern Analysis I and MATH GU4062 Introduction To Modern Analysis II.
  • Students preparing for a career in actuarial science are encouraged to replace STAT GU4205 Linear Regression Models with STAT GU4282 Linear Regression and Time Series Methods, and to take among their electives STAT GU4281 Theory of Interest.

Major in Political Science–Statistics

The interdepartmental major of political science–statistics is designed for students who desire an understanding of political science to pursue advanced study in this field and who also wish to have at their command a broad range of sophisticated statistical tools to analyze data related to social science and public policy research.

Students should be aware of the rules regarding the use of the Pass/D/Fail option. Courses in which a grade of D has been received do not count toward the major requirements.

Political science–statistics students are eligible for all prizes reserved for political science majors.

Students take courses in mathematics and 31 or 34 points in political science, statistics, and computer science.

Political Science
Students must choose a primary subfield to study. Within the subfield, students must take a minimum of three courses, including the subfield's introductory course. The subfields and their corresponding introductory courses are as follows:
American Politics:
Introduction To American Government and Politics
Comparative Politics:
Introduction to Comparative Politics
International Relations:
Introduction to International Politics
Political Theory:
Political Theory I
Additionally, students must take a 4-point seminar in their primary subfield.
Research Methods
Students must take the following two research methods courses:
POLS GU4710Principles of Quantitative Political Research
or POLS UN3704 Data Analysis and Statistics for Political Science Research
POLS GU4712Analysis of Political Data
Statistics
Select one of the following two sequences.
Sequence recommended for students preparing for graduate study in statistics.
Calculus I
Calculus II
Linear Algebra
Calculus-Based Introduction to Statistics
PROBABILITY THEORY
Statistical Inference
Linear Regression Models
Statistical Computing and Introduction to Data Science
Students taking the first track may replace the Mathematics prerequisites with both of MATH UN1207 and MATH UN1208
or
Sequence recommend for students preparing to apply statistical methods in the social sciences.
Introduction to Statistics
Applied Statistical Computing
Applied Linear Regression Analysis
Applied Categorical Data Analysis
Applied Statistical Methods
Applied Data Mining
Statistics elective:
Students must take an approved elective in a statistics or a quantitatively oriented course in a social science.

Introductory Courses

Students interested in statistical concepts, but who do not anticipate undertaking statistical analyses, should take STAT UN1001 Introduction to Statistical Reasoning. Students seeking an introduction to applied statistics or preparing for the concentration should take STAT UN1101 Introduction to Statistics (without calculus). Students seeking a foundation for further study of probability theory and statistical theory and methods should take STAT UN1201 Calculus-based Introduction to Statistics. Students seeking a one-semester calculus-based survey should take STAT GU4001 Introduction to Probability and Statistics. The undergraduate seminar STAT UN1202 features faculty lectures prepared with undergraduates in mind; students may attend without registering.

STAT UN1001 Introduction to Statistical Reasoning. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.

Spring 2017: STAT UN1001
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1001 001/76540 T Th 6:10pm - 7:25pm
312 Mathematics Building
Anthony Donoghue 3 45/110
STAT 1001 002/73953 T Th 2:40pm - 3:55pm
214 Pupin Laboratories
Yayun Hsu 3 17/50
STAT 1001 003/67625 M W 10:10am - 11:25am
516 Hamilton Hall
Louis Mittel 3 46/50
Fall 2017: STAT UN1001
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1001 001/25100 M W 10:10am - 11:25am
517 Hamilton Hall
Guy Cohen 3 76/86
STAT 1001 002/20569 T Th 10:10am - 11:25am
503 Hamilton Hall
Jonathan Auerbach 3 48/50
STAT 1001 003/29529 M W 6:10pm - 7:25pm
717 Hamilton Hall
Anthony Donoghue 3 43/86

STAT UN1101 Introduction to Statistics. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: intermediate high school algebra.

Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, analysis of variance, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Spring 2017: STAT UN1101
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1101 001/28974 M W 11:40am - 12:55pm
203 Mathematics Building
Banu Baydil 3 63/86
STAT 1101 002/76665 T Th 11:40am - 12:55pm
517 Hamilton Hall
Banu Baydil 3 69/86
STAT 1101 003/27319 T Th 6:10pm - 7:25pm
717 Hamilton Hall
Banu Baydil 3 54/86
Fall 2017: STAT UN1101
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1101 001/73141 M W 11:40am - 12:55pm
603 Hamilton Hall
David Rios 3 53/54
STAT 1101 002/14935 T Th 11:40am - 12:55pm
602 Hamilton Hall
David Rios 3 29/86
STAT 1101 003/15455 T Th 6:10pm - 7:25pm
717 Hamilton Hall
Ha Nguyen 3 86/86

STAT UN1201 Calculus-Based Introduction to Statistics. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: one semester of calculus.

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. Serves as the pre-requisite for ECON W3412.

Spring 2017: STAT UN1201
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1201 001/19141 T Th 10:10am - 11:25am
C03 School Of Social Work
David Rios 3 46/86
STAT 1201 002/60742 T Th 8:40am - 9:55am
207 Mathematics Building
Joyce Robbins 3 81/86
STAT 1201 003/67320 M W 6:10pm - 7:25pm
602 Hamilton Hall
Ha Nguyen 3 78/86
STAT 1201 004/10614 F 11:40am - 2:25pm
207 Mathematics Building
Sheela Kolluri 3 48/86
Fall 2017: STAT UN1201
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1201 001/73218 M W 8:40am - 9:55am
517 Hamilton Hall
Joyce Robbins 3 70/86
STAT 1201 002/76589 T Th 8:40am - 9:55am
702 Hamilton Hall
Joyce Robbins 3 83/86
STAT 1201 003/68663 M W 11:40am - 12:55pm
717 Hamilton Hall
Arian Maleki 3 86/86
STAT 1201 004/70129 T Th 6:10pm - 7:25pm
702 Hamilton Hall
Sheela Kolluri 3 86/86

STAT UN1202 Undergraduate Seminar. 1 point.

Prerequisites: Previous or concurrent enrollment in a course in statistics would make the talks more accessible.

Prepared with undergraduates majoring in quantitative disciplines in mind, the presentations in this colloquium focus on the interface between data analysis, computation, and theory in interdisciplinary research. Meetings are open to all undergraduates, whether registered or not. Presenters are drawn from the faculty of department in Arts and Sciences, Engineering, Public Health and Medicine.

Fall 2017: STAT UN1202
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 1202 001/60803 F 10:10am - 12:00pm
312 Mathematics Building
Ronald Neath, Banu Baydil 1 7/50

STAT GU4001 Introduction to Probability and Statistics. 3 points.

BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: Calculus through multiple integration and infinite sums.

A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, conditioning, expectations, law of large numbers, central limit theorem, point and confidence interval estimation, hypothesis tests, linear regression.  This course replaces SIEO 4150.

Spring 2017: STAT GU4001
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4001 001/21880 T Th 1:10pm - 2:25pm
207 Mathematics Building
David Rios 3 86/110
STAT 4001 002/17296 M W 2:40pm - 3:55pm
312 Mathematics Building
Mark Brown 3 91/110
Fall 2017: STAT GU4001
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4001 001/65735 Sa 10:10am - 12:55pm
312 Mathematics Building
Daniel Rabinowitz 3 55/120
STAT 4001 002/62655 M W 2:40pm - 3:55pm
614 Schermerhorn Hall
Mark Brown 3 105/120

Applied Statistics Concentration Courses

The applied statistics sequence, together with an introductory course, forms the concentration in applied statistics. STAT UN2102 Applied statistical computing may be used to satisfy the computing requirement for the major, and the other concentration courses may be used to satisfy the elective requirements for the major. (Students who stat STAT W4205 Linear Regression for the major would find that they have covered essentially all of the material in STAT UN2103 Applied Linear Regression Analysis.

STAT UN2102 Applied Statistical Computing. 3 points.

Corequisites: An introductory course in statistic (STAT UN1101 is recommended).

This course is an introduction to R programming.  After learning basic programming component, such as defining variables and vectors, and learning different data structures in R, students will, via project-based assignments, study more advanced topics, such as recursion, conditionals, modular programming, and data visualization.  Students will also learn the fundamental concepts in computational complexity, and will practice writing reports based on their statistical analyses.

STAT UN2103 Applied Linear Regression Analysis. 3 points.

CC/GS: Partial Fulfillment of Science Requirement

Prerequisites: An introductory course in statistics (STAT UN1101 is recommended). Students without programming experience in R might find STAT UN2102 very helpful.

Develops critical thinking and data analysis skills for regression analysis in science and policy settings. Simple and multiple linear regression, non-linear and logistic models, random-effects models. Implementation in a statistical package. Emphasis on real-world examples and on planning, proposing, implementing, and reporting.

Fall 2017: STAT UN2103
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 2103 001/12203 M W 6:10pm - 7:25pm
602 Hamilton Hall
Gabriel Young 3 60/60

STAT UN2104 Applied Categorical Data Analysis. 3 points.

Prerequisites: STAT UN2103 is strongly recommended. Students without programming experience in R might find STAT UN2102 very helpful.

This course covers statistical models amd methods for analyzing and drawing inferences for problems involving categofical data.  The goals are familiarity and understanding of a substantial and integrated body of statistical methods that are used for such problems, experience in anlyzing data using these methods, and profficiency in communicating the results of such methods, and the ability to critically evaluate the use of such methods.  Topics include binomial proportions, two-way and three-way contingency tables, logistic regression, log-linear models for large multi-way contingency tables, graphical methods.  The statistical package R will be used.

Spring 2017: STAT UN2104
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 2104 001/82746 M W 2:40pm - 3:55pm
313 Fayerweather
James Landwehr 3 29/60

STAT UN3105 Applied Statistical Methods. 3 points.

Prerequisites: At least one, and preferably both, of STAT UN2103 and UN2104 are strongly recommended. Students without programming experience in R might find STAT UN2102 very helpful.

This course is intended to give students practical experience with statistical methods beyond linear regression and categorical data analysis.  The focus will be on understanding the uses and limitations of models, not the mathematical foundations for the methods.  Topics that may be covered include random and mixed-effects models, classical non-parametric techniques, the statistical theory causality, sample survey design, multi-level models, generalized linear regression, generalized estimating equations and over-dispersion, survival analysis including the Kaplan-Meier estimator, log-rank statistics, and the Cox proportional hazards regression model.  Power calculations and proposal and report writing will be discussed.

Fall 2017: STAT UN3105
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 3105 001/67449 T Th 1:10pm - 2:25pm
703 Hamilton Hall
David Rios 3 29/50

STAT UN3106 Applied Data Mining. 3 points.

Prerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful.

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.

Spring 2017: STAT UN3106
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 3106 001/81399 T Th 2:40pm - 3:55pm
233 Seeley W. Mudd Building
Gabriel Young 3 47/50

Foundation Courses

The calculus-based foundation courses for the core of the statistics major. These courses are GU4203 Probability Theory, GU4204 Statistical Inference, GU4205 Linear Regression, GU4206 Statistical Computing and Introduction to Data Science, and GU4207 Elementary Stochastic processes. Ideally, students would take Probability theory or the equivalent before taking either Statistical Inference or Elementary Stochastic Processes, and would have taken Statistical Inference before, or at least concurrently with taking Linear Regression Analysis, and would have taken Linear Regression analysis before, or at least concurrently, with taking the computing and data science course. A semester of calculus should be taken before Probability, additional semesters of calculus are recommended before Statistical Inference, and a course in linear algebra before Linear Regression is strongly recommended. For the more advanced electives in stochastic processes, Probability Theory is an essential prerequisite, and many students would benefit from taking Elementary Stochastic Processes, too. Linear Regression and the computing and data science course should be taken before the advanced electives in machine learning and data science. Linear Regression is a strongly recommended prerequisite, or at least co-requisite, for the remaining advanced statistical electives.

STAT GU4203PROBABILITY THEORY
STAT GU4204Statistical Inference
STAT GU4205Linear Regression Models
STAT GU4206Statistical Computing and Introduction to Data Science
STAT GU4207Elementary Stochastic Processes

Advanced Statistics Courses

Advanced statistics courses combine theory with methods and practical experience in data analysis. Undergraduates enrolling in advanced statistics courses would be well-advised to have completed STAT GU4203 (Probability Theory), GU4204 (Statistical Inference), and GU4205 (Linear Regression).

STAT GU4221 Time Series Analysis. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

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.

Spring 2017: STAT GU4221
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4221 001/76456 M W 7:40pm - 8:55pm
614 Schermerhorn Hall
Abolfazal Safikhani 3 30/40
STAT 4221 002/29812 Sa 2:40pm - 5:25pm
312 Mathematics Building
Rongning Wu 3 12/40
Fall 2017: STAT GU4221
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4221 001/11723 M W 7:40pm - 8:55pm
203 Mathematics Building
Dan Wang 3 24/30

STAT GU4222 Nonparametric Statistics. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4204 or the equivalent.

Statistical inference without parametric model assumption. Hypothesis testing using ranks, permutations, and order statistics. Nonparametric analogs of analysis of variance. Non-parametric regression, smoothing and model selection.

Spring 2017: STAT GU4222
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4222 001/73339 M W 1:10pm - 2:25pm
209 Havemeyer Hall
Pawel Polak 3 8/35

STAT GU4223 Multivariate Statistical Inference. 0 points.

Prerequisites: STAT GU4205 or the equivalent.

Multivariate normal distribution, multivariate regression and classification; canonical correlation; graphical models and Bayesian networks; principal components and other models for factor analysis; SVD; discriminant analysis; cluster analysis.

Spring 2017: STAT GU4223
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4223 001/73196 T Th 1:10pm - 2:25pm
313 Fayerweather
Pawel Polak 0 5/35

STAT GU4224 Bayesian Statistics. 3 points.

Prerequisites: STAT GU4204 or the equivalent.

Bayesian vs frequentist, prior and posterior distributions, conjugate priors, informative and non-informative prior subjective and objective bayes, oneand two sample problems, models for normal data, models for binary data, multivariate normal shrinkage, bayesian linear models, bayesian computation (start early), MCMC algorithms, the Gibbs sampler, hierarchical models, empirical bayes, hypothesis testing, bayes factors, model selection, software: R and WinBUGS

Spring 2017: STAT GU4224
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4224 001/19142 T Th 6:10pm - 7:25pm
833 Seeley W. Mudd Building
Ronald Neath 3 16/40
Fall 2017: STAT GU4224
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4224 001/24790 M W 2:40pm - 3:55pm
329 Pupin Laboratories
Ronald Neath 3 21/20

STAT GU4231 Survival Analysis. 0 points.

BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4205 or the equivalent.

Survival distributions, types of censored data, estimation for various survival models, nonparametric estimation of survival distributions, the proportional hazard and accelerated lifetime models for regression analysis with failure-time data. Extensive use of the computer.

Spring 2017: STAT GU4231
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4231 001/12349 M W 6:10pm - 7:25pm
903 School Of Social Work
Michael Shnaidman 0 2/25

STAT GU4232 Generalized Linear Models. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4205 or the equivalent.

Statistical methods for rates and proportions, ordered and nominal categorical responses, contingency tables, odds-ratios, exact inference, logistic regression, Poisson regression, generalized linear models.

Spring 2017: STAT GU4232
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4232 001/93347 T Th 4:10pm - 5:25pm
207 Mathematics Building
Michael Sobel 3 5/35

STAT W4233 Multilevel Models. 0 points.

BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).
Not offered during 2017-18 academic year.

Prerequisites: STAT GU4205 or the equivalent.

Theory and practice, including model-checking, for random and mixed-effects models (also called hierarchical, multi-level models). Extensive use of the computer to analyse data.

STAT GU4234 Sample Surveys. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4204 or the equivalent.

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.

Fall 2017: STAT GU4234
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4234 001/61950 T Th 6:10pm - 7:25pm
1127 Seeley W. Mudd Building
Ronald Neath 3 13/15

STAT GU4241 Statistical Machine Learning. 0 points.

Prerequisites: STAT GU4206.

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.

Spring 2017: STAT GU4241
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4241 001/29700 T Th 6:10pm - 7:25pm
207 Mathematics Building
Linxi Liu 0 13/25
STAT 4241 002/11922 T Th 7:40pm - 8:55pm
428 Pupin Laboratories
Cynthia Rush 0 13/25

STAT GU4261 Statistical Methods in Finance. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4205 or the equivalent.

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.

Spring 2017: STAT GU4261
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4261 001/73209 Sa 9:10am - 11:40am
501 Schermerhorn Hall
Zhiliang Ying 3 22/25
Fall 2017: STAT GU4261
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4261 001/13009 F 10:10am - 12:55pm
203 Mathematics Building
Hammou ElBarmi 3 20/20

STAT W4263 Statistical Inference and Time-Series Modelling. 3 points.

Prerequisites: STAT GU4204 or the equivalent. STAT GU4205 is recommended.

Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.  This is a core course in the MS program in mathematical finance.

STAT GU4291 Advanced Data Analysis. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4205 and at least one statistics course numbered between GU4221 and GU4261.

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.

Spring 2017: STAT GU4291
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4291 001/14452 F 10:10am - 12:40pm
312 Mathematics Building
Hammou ElBarmi 3 19/25
Fall 2017: STAT GU4291
Course Number Section/Call Number Times/Location Instructor Points Enrollment
STAT 4291 001/13628 F 6:10pm - 8:55pm
501 Schermerhorn Hall
Demissie Alemayehu 3 20/25

Actuarial Sciences Courses

Only students preparing for a career in actuarial sciences should consider the courses in this section. Such students may also be interested in courses offered through the School of Professional Studies M.S. Program in Actuarial Science, but must check with the academic advisors in their schools to know whether they are allowed to register for those courses. Students majoring in statistics and preparing for a career in actuarial science may take STAT GU4282 (Regression and Time Series Analysis) in place of the major requirement STAT GU4205 (Linear Regression Analysis).

STAT GU4281Theory of Interest
STAT GU4282Linear Regression and Time Series Methods

Advanced Data Science Courses

In response to the ever growing importance of ``big data” in scientific and policy endeavors, the last few years have seen an explosive growth in theory, methods, and applications at the interface between computer science and statistics. The Department offers a sequence that begins with the core course STAT GU4206 (Statistical Computing and Introduction to Data Science) and continues with the advanced electives GU4241 (Statistical Machine Learning) and GU4242 (Advanced Machine Learning), and also the advanced elective STAT GU4243 (Applied Data Science). Undergraduate students without experience in programming would likely benefit from taking the statistical computing and data science course before attempting GU4241, GU4242, or GU4243.

STAT GU4241Statistical Machine Learning
STAT Q4242Advanced Machine Learning
STAT GU4243Applied Data Science
STAT W4702Exploratory Data Analysis and Visualization

Advanced Stochastic Processes Courses

The stochastic processes electives in this section have STAT GU4203 (Probability Theory) or the equivalent as prerequisites Most students would also benefit from taking STAT GU4207 (Elementary Stochastic Processes) before embarking on the more advanced stochastic processes electives.

STAT W3201Math Finance in Continuous Time
STAT GU4262Stochastic Processes for Finance
STAT GU4264Stochastic Processes and Applications
STAT W4265Stochastic Methods in Finance
STAT G4266Stochastic Control and Applications in Finance