Statistics

Department website: http://statistics.columbia.edu/

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:
Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu
Gabriel Young, 610 Watson; 212-853-1395; gjy2107@columbia.edu

Data Science Major Advising:
Computer Science: Augustin Chaintreau, 610 CEPSR; 212-939-7082; augustin@cs.columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu
Statistics: Gabriel Young, 610 Watson; 212-853-1395; gjy2107@columbia.edu

Economics - Statistics Major Advising:
Economics: Susan Elmes, 1006 IAB; 212-854-9124; se5@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu
Statistics: Gabriel Young, 610 Watson; 212-853-1395; gjy2107@columbia.edu

Mathematics - Statistics Major Advising:
Mathematics: Julien Dubedat, 601 Mathematics; 212-854-8806; jd2653@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu
Statistics: Gabriel Young, 610 Watson; 212-853-1395; gjy2107@columbia.edu

Political Science - Statistic Major Advising:
Political Science: Alessandra Casella, 1030 IAB; 212-854-8059; acasella@columbia.edu
Statistics: Ronald Neath, 612 Watson; 212-853-1398; rcn2112@columbia.edu
Statistics: Gabriel Young, 610 Watson; 212-853-1395; gjy2107@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 Statistics, STAT 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 STOCHASTC PROCSSES-APPLIC, 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 GU4242 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
• Liam Paninski
• Philip Protter
• Daniel Rabinowitz
• Michael Sobel
• Simon Tavaré
• Zhiliang Ying
• Ming Yuan
• Tian Zheng

Associate Professors

• John Cunningham
• Yang Feng
• Jingchen Liu
• Marcel Nutz
• Peter Orbanz
• Bodhisattva Sen

Assistant Professors

• Samory Kpotufe
• Arian Maleki
• Sumit Mukherjee
• Cynthia Rush

Term Assistant Professors

• Marco Avella
• Ruimeng Hu
• Linxi Liu
• Thibault Vatter

Adjunct Professors

• Demissie Alemayehu
• Flavio Bartmann
• Guy Cohen
• Regina Dolgoarshinnykh
• Anthony Donoghue
• Vincent Dorie
• Hammou El Barmi
• Xiaofu He
• Irene Hueter
• Ying Liu
• Ha Nguyen
• Michael Shnaidman
• Larry Wright
• Rongning Wu

Lecturers in Discipline

• Banu Baydil
• Wayne Lee
• Ronald Neath
• David Rios
• Joyce Robbins
• 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 taken for a grade of Pass/D/Fail, or in which the grade of D has been received, do not count toward the major. The requirements for the major are as follows:

Course List

Code	Title	Points
Mathematics and Computer Science Prerequisites
MATH UN1101	Calculus I
MATH UN1102	Calculus II
MATH UN1201	Calculus III
MATH UN2010	Linear Algebra
One of the following five courses
COMS W1007	Honors Introduction to Computer Science
ENGI E1006	Introduction to Computing for Engineers and Applied Scientists
COMS W1005	Introduction to Computer Science and Programming in MATLAB
STAT UN2102	Applied Statistical Computing
COMS W1004	Introduction to Computer Science and Programming in Java
Core courses in probability and statistics
STAT UN1201	Calculus-Based Introduction to Statistics
STAT GU4203	PROBABILITY THEORY
STAT GU4204	Statistical Inference
STAT GU4205	Linear Regression Models
STAT GU4206	Statistical Computing and Introduction to Data Science
STAT GU4207	Elementary 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 .

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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.

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 sat STAT GU4205 Linear Regression for the major would find that they have covered essentially all of the material in STAT UN2103 Applied Linear Regression Analysis.

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.

Course List

Code	Title	Points
STAT GU4203	PROBABILITY THEORY
STAT GU4204	Statistical Inference
STAT GU4205	Linear Regression Models
STAT GU4206	Statistical Computing and Introduction to Data Science
STAT GU4207	Elementary 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).

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).

Course List

Code	Title	Points
STAT GU4281	Theory of Interest
STAT GU4282	Linear 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.

Course List

Code	Title	Points
STAT GU4241	Statistical Machine Learning
STAT GU4242	Advanced Machine Learning
STAT GU4243	Applied Data Science
STAT GU4702	Exploratory 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.

Course List

Code	Title	Points
STAT GU4262	Stochastic Processes for Finance
STAT GU4264	STOCHASTC PROCSSES-APPLIC
STAT GU4265	Stochastic Methods in Finance

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