Statistics
Department of Statistics:
Department website: http://www.stat.columbia.edu
Office location: 1005 SSW (1255 Amsterdam Avenue);
Office contact: 2128512132
Director of Undergraduate Studies:
Ronald Neath, 615 Watson (612 West 115th Street), 2128531398;
Director of Academic Administration:
Dood Kalicharan, 1003 SSW (1255 Amsterdam), 2128531398; dk@stat.columbia.edu
The Study of Statistics
Statistics is the art and science of study design and data analysis. Probability theory is the mathematical foundation for the study of statistical methods and for the modeling of random phenomena. The Statistics major builds on a foundation in probability and statistical theory to provide practical training in statistical methods, study design, and data analysis. The Statistics major is an appropriate background for graduate study in statistics, social science, epidemiology and public health, genetics, and economics; or for professional work in such areas as drug development, health policy, marketing, opinion polling, insurance, banking and finance, and government.
The Department offers several introductory courses.

Students interested in learning statistical concepts, with a goal of being educated consumers of statistics, should take STAT UN1001 INTRO TO STATISTICAL REASONING. This course is designed for students who have taken a precalculus course, and the focus is on general principles.

Students seeking an introduction to applied statistics should take STAT UN1101 INTRODUCTION TO STATISTICS. This course is designed for students who wish to learn to conduct statistical analyses, but do not have a background in calculus; the focus is on the implementation of statistical methods, rather than the underlying theory. It is recommended for premed students, and students considering the applied track of the statistics minor.

Students seeking a more mathematically rigorous treatment of the subject should take STAT UN1201 CALCBASED INTRO TO STATISTICS. This course is designed for students who have taken a semester of college calculus or the equivalent, and the focus is on preparation for further study in probability and statistical theory and methods. It is recommended for students considering the statistics major, or the theoretical track of the minor.

Students seeking a onesemester calculusbased survey of probability and statistical theory 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 an abridged version of the material covered in the twosemester sequence STAT GU4203 PROBABILITY THEORY and STAT GU4204 STATISTICAL INFERENCE. While some mathematically mature students may take the 42034204 sequence as an introduction to the field, it is generally recommended that students prepare for it by taking STAT UN1201 CALCBASED INTRO TO STATISTICS.
The Department offers a Major in Statistics, a Minor in Statistics, and interdisciplinary majors with Computer Science, Economics, Mathematics, and Political Science. The major consists of mathematical and computational prerequisites, an introductory course, five core courses in probability and theoretical and applied statistics, plus three electives. The training provided by the undergraduate major is comparable to a master’s degree in statistics. The applied track of the minor is suitable for students preparing for academic or professional work in fields where data analysis skills are valued; it can be completed without the mathematical prerequisite required for the major. Students who are more mathematically inclined can opt for the theoretical track, and complete a minor by taking courses from the core sequence of the statistics major.
Student Advising
Statistics Major and Minor Advising:
Ronald Neath, 615 Watson (612 West 115th Street); 2128531398; rcn2112@columbia.edu
Data Science Major Advising:
Computer Science: Tim Roughgarden, 410 Mudd; 2128538474; tr@cs.columbia.edu
Statistics: Ronald Neath, 615 Watson; 2128531398; rcn2112@columbia.edu
Economics  Statistics Major Advising:
Economics: Susan Elmes, 1006 IAB; 2128549124; se5@columbia.edu
Statistics: Ronald Neath, 615 Watson; 2128531398; rcn2112@columbia.edu
Mathematics  Statistics Major Advising:
Mathematics: Julien Dubedat, 601 Mathematics; 2128548806; jd2653@columbia.edu
Statistics: Ronald Neath, 615 Watson; 2128531398; rcn2112@columbia.edu
Political Science  Statistic Major Advising:
Political Science: Andrew Gelman, 1016 SSW (1255 Amsterdam); gelman@stat.columbia.edu
Statistics: Ronald Neath, 615 Watson; 2128531398; rcn2112@columbia.edu
Enrolling in Classes
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 minor courses: STAT UN2103 APPLIED LINEAR REG ANALYSIS, STAT UN2104 APPL CATEGORICAL DATA ANALYSIS, STAT UN3104 Applied Bayesian Analysis, STAT UN3105 APPLIED STATISTICAL METHODS, and STAT UN3106 APPLIED MACHINE LEARNING.

It is advisable to take STAT UN1201 CALCBASED INTRO 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 PROCESS before embarking on STAT GU4262 Stochastic Processes for Finance, STAT GU4264 STOCHASTC PROCSSESAPPLICTNS I, 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 STAT COMP ＆ INTRO DATA SCIENCE, STAT GU4241 STATISTICAL MACHINE LEARNING, and STAT GU4242 Advanced Machine Learning should be taken in sequence.
Preparing for Graduate Study
The BA/MA option allows current Columbia undergraduate students (Columbia College, SEAS, the School of General Studies, and Barnard) the opportunity to complete both the bachelor’s degree and the master’s degree (BA/MA) in a shorter period of time, thus providing an option that is financially advantageous. The BA/MA in Statistics is open to students from all majors.
Coursework Taken Outside of Columbia
Coursework in fulfillment of a major or minor must be taken at Columbia University unless explicitly noted here and/or expressly permitted by the Director of Undergraduate Studies. Exceptions or substitutions permitted by the Director of Undergraduate Studies should be confirmed in writing by email to the student.
Advanced Placement
Columbia College and the School of General Studies award 3 points of credit for a score of 5 on the AP statistics exam. Students who are required to take STAT UN1101 for their major should check with their major advisor to determine whether this credit provides exemption from their requirement.
Students pursuing a major that requires STAT UN1201 should plan to take that course at Columbia, even if they scored a 5 on the AP statistics exam. AP credit cannot be used to satisfy a requirement for STAT UN1201.
Transfer Courses
When students transfer to Columbia from other institutions, their coursework at their previous institution must first be considered by their school in order to be evaluated for degree credit (e.g., to confirm that the courses will count toward the 124 points of credit that every student is required to complete for the B.A. degree). Only after that degree credit is confirmed, departments may consider whether those courses can also be used to fulfill specific degree requirements toward a major or minor.
No more than two DUSapproved STAT courses toward a Statistics major may be fulfilled with transfer credit.
Not more than one DUSapproved STAT course toward a Statistics joint major or a Statistics minor may be fulfilled with transfer credit.
Study Abroad Courses
Classes taken abroad through Columbialed programs (i.e., those administered by Columbia’s Center for Undergraduate Global Engagement and taught by Columbia instructors) are treated as Columbia courses, equivalent to those taken on the Morningside Heights campus. If they are not explicitly listed by the department as fulfilling requirements in the major or minor, the DUS will need to confirm that they can be used toward requirements in the major/minor.
Classes taken abroad through other institutions and programs are treated as transfer credit to Columbia, and are subject to the same policies as other transfer courses, including limits on the number of approved STAT course that can be applied to the major/minor.
Summer Courses
Summer courses at Columbia are offered through the School of Professional Studies. Courses taken in a Summer Term may be used toward requirements for the Statistics major or minor.
More general policies about Summer coursework can be found in the Academic Regulations section of this Bulletin.
Undergraduate Research
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. Students work under the supervision of Statistics Department faculty mentors. The internship provides a summer housing allowance and a stipend. Applicants should send a brief statement of interest and a copy of their transcript to the Statistics DUS by the end of March to be considered. If summer project descriptions are posted on the Department’s website, please indicate your preferred project(s) in your statement 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 coursework when asking for a project.
Department Honors
Students are considered for department honors on the basis of GPA and the comprehensiveness and difficulty of their coursework in Statistics and related disciplines. Generally, no more than 10% of graduating majors receive departmental honors in a given academic year.
Professors
 David Blei (with Computer Science)
 John Cunningham
 Richard R. Davis
 Victor H. de la Peña
 Andrew Gelman (with Political Science)
 Ioannis Karatzas (with Mathematics)
 Jingchen Liu
 ShawHwa Lo
 Marcel Nutz (with Mathematics)
 Liam Paninski
 Philip Protter
 Daniel Rabinowitz
 Bodhisattva Sen
 Michael Sobel
 Simon Tavaré (with Biological Sciences)
 Zhiliang Ying
 Ming Yuan
 Tian Zheng (Chair)
Associate Professors
 Samory Kpotufe
 Arian Maleki
 Sumit Mukherjee
Assistant Professors
 Marco Avella
 Yuqi Gu
 Cynthia Rush
 Anne van Delft
Term Assistant Professors
 Carsten Chong
 Gokce Dayanikli
 Yongchen Kwon
 Johannes Wiesel
 Chenyang Zhong
Adjunct Faculty
 Demissie Alemayehu
 Mark Brown
 Guy Cohen
 Regina Dolgoarshinnykh
 Hammou El Barmi
 Tat Sang Fung
 Xiaofu He
 Ying Liu
 KaYi Ng
 Ha Nguyen
 Cristian Pasarica
 Kamiar Rahnama Rad
 Ori Shental
 Haiyuan Wang
 Rongning Wu
Lecturers in Discipline
 Banu Baydil
 Anthony Donoghue
 Wayne Lee
 Dobrin Marchev
 Ronald Neath
 Alex Pijyan
 David Rios
 Joyce Robbins
 Gabriel Young
Guidance for Undergraduate Students in the Department
Program Planning for all Students
Selecting a first course in Statistics:

Students interested in learning statistical concepts, with a goal of being educated consumers of statistics, should take STAT UN1001 INTRO TO STATISTICAL REASONING. This course is designed for students who have taken a precalculus course, and the focus is on general principles.

Students seeking an introduction to applied statistics should take STAT UN1101 INTRODUCTION TO STATISTICS. This course is designed for students who wish to learn to conduct statistical analyses, but do not have a background in calculus; the focus is on the implementation of statistical methods, rather than the underlying theory. It is recommended for premed students, and students considering the applied track of the statistics minor.

Students seeking a more mathematically rigorous treatment of the subject should take STAT UN1201 CALCBASED INTRO TO STATISTICS. This course is designed for students who have taken a semester of college calculus or the equivalent, and the focus is on preparation for further study in probability and statistical theory and methods. It is recommended for students considering the statistics major, or the theoretical track of the minor.

Students seeking a onesemester calculusbased survey of probability and statistical theory 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 an abridged version of the material covered in the twosemester sequence STAT GU4203 PROBABILITY THEORY and STAT GU4204 STATISTICAL INFERENCE. While some mathematically mature students may take the 42034204 sequence as an introduction to the field, it is generally recommended that students prepare for it by taking STAT UN1201 CALCBASED INTRO TO STATISTICS.
Course Numbering Structure
The 1000level courses (STAT UN1001, STAT UN1101 and STAT UN1201) are introductory courses. Most students will begin their study of statistics with one of these three courses.
The 2000level courses (STAT UN2102, STAT UN2103 and STAT UN2104) are courses in computational and applied statistics, with STAT UN1101 or STAT UN1201 as a prerequisite. These are important courses in the minor program; students pursuing a statistics major will learn this material by studying the more mathematical treatment given in the 4000level courses.
The 3000level courses (STAT UN3104, STAT UN3105 and STAT UN3106) introduce more specialized statistical methods which build on the material introduced in STAT UN2102 and STAT UN2103. While the statistical methods covered in these courses can be quite advanced, the mathematical level remains modest. Again, these courses are part of the minor curriculum, and students completing a statistics major will learn this material elsewhere in the statistics curriculum.
STAT GU4001 is a onesemester calculusbased course in probability and statistics, intended for students who seek a mathematically rigorous course, but do not intend to major or minor in statistics (for most, this will be a terminal course).
The 4200level courses are intended for students majoring in statistics and related disciplines.
Courses numbered 4203 through 4207 introduce fundamental material in probability theory, statistical inference, data analysis, and statistical computing; these courses comprise the core of the statistics major.
Courses numbered 4221 through 4234 cover specialized statistical data analysis techniques, and are possible electives for students in the statistics major.
Courses numbered 4241 through 4243 introduce modern tools in machine learning and data science.
Courses numbered between 4261 and 4265 cover statistical and probabilistic theory and methods in modern finance.
Undergraduate Programs of Study
Major in Statistics
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 major requires 14 courses, as follows:
Code  Title  Points 

Mathematics Prerequisite (four courses)  
MATH UN1101  CALCULUS I  
MATH UN1102  CALCULUS II  
MATH UN1201  CALCULUS III  
MATH UN2010  LINEAR ALGEBRA  
Computer Science Requirement (one course). Choose one of the following  
Introduction to Computer Science and Programming in Java  
INTRO TO COMP FOR ENG/APP SCI  
Applied Statistical Computing  
Statistical prerequisite (one course)  
STAT UN1201  CALCBASED INTRO TO STATISTICS  
Core courses in probability and statistics (five courses):  
STAT GU4203  PROBABILITY THEORY  
STAT GU4204  STATISTICAL INFERENCE  
STAT GU4205  LINEAR REGRESSION MODELS  
STAT GU4206  STAT COMP ＆ INTRO DATA SCIENCE  
STAT GU4207  ELEMENTARY STOCHASTIC PROCESS  
Electives (three courses):  
An approved selection of three advanced courses in mathematics, statistics, applied mathematics, industrial engineering and operations research, computer science, or an advanced quantitative course in a social science. At least one elective must be a Statistics Department course numbered between 4221 and 4291 
 The mathematics prerequisite can also be satisfied by taking the Honors Mathematics A and B sequence, MATH UN1207 and MATH UN1208.
 Students preparing for graduate study in statistics are encouraged to replace two electives with MATH GU4061 INTRO MODERN ANALYSIS I and MATH GU4062 INTRO MODERN ANALYSIS II .
Major in Data Science
In response to the ever increasing importance of “big data” in scientific and policy endeavors, the last few years have seen an explosive growth in theory, methods, and applications of AI and machine learning. The Department of Computer Science and the Department of Statistics jointly offer a Data Science major that emphasizes the interface between the two disciplines.
The major requires 18 courses, as follows. (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.)
Notes:
The mathematics prerequisite can also be satisfied by taking the Honors Mathematics A and B sequence, MATH UN1207 and MATH UN1208.
Code  Title  Points 

Mathematical Prerequisites  
CALCULUS I  
CALCULUS II  
CALCULUS III  
or MATH UN1205  ACCELERATED MULTIVARIABLE CALC  
LINEAR ALGEBRA  
Statistics Required Courses  
CALCBASED INTRO TO STATISTICS  
PROBABILITY THEORY  
STATISTICAL INFERENCE  
LINEAR REGRESSION MODELS  
STATISTICAL MACHINE LEARNING  
or COMS W4771  MACHINE LEARNING  
Statistics Electives  
Select two of the following courses:  
APPLIED MACHINE LEARNING  
STAT COMP ＆ INTRO 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  
INTRO TO COMP FOR ENG/APP SCI  
COMS W1007  
And select one of the following courses:  
Data Structures in Java  
ESSENTIAL DATA STRUCTURES  
HONORS DATA STRUCTURES ＆ ALGOL  
Computer Science Required Courses  
DISCRETE MATHEMATICS  
ANALYSIS OF ALGORITHMS I  
Computer Science Electives  
Select three of the following courses:  
COMPUTER SCIENCE THEORY  
INTROCOMPUTATIONAL COMPLEXITY  
INTROCOMPUTATIONAL LEARN THRY  
INTRODUCTION TO DATABASES  
COMS W4130  
Any COMS W47xx course EXCEPT W4771 
Major in EconomicsStatistics
Please read Requirements for all Economics Majors, Concentrators, and Interdepartmental Majors in the Economics section of this Bulletin.
The major in EconomicsStatistics 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 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.
The economicsstatistics major requires 18 courses, as follows:
Notes:

The mathematics prerequisite can also be satisfied by taking the Honors Mathematics A and B sequence, MATH UN1207 and MATH UN1208.
Code  Title  Points 

Economics Core Courses  
Complete the Economics core courses.  
Economics Electives  
Select three electives at the 3000level or above, of which no more than one may be a Barnard course.  
Mathematics  
Select one of the following sequences:  
MATH UN1101  MATH UN1102  MATH UN1201  MATH UN2010  CALCULUS I and CALCULUS II and CALCULUS III and LINEAR ALGEBRA  
MATH UN1101  MATH UN1102  MATH UN1205  MATH UN2010  CALCULUS I and CALCULUS II and ACCELERATED MULTIVARIABLE CALC and LINEAR ALGEBRA  
MATH UN1207  MATH UN1208  HONORS MATHEMATICS A and HONORS MATHEMATICS B  
Statistics  
STAT UN1201  CALCBASED INTRO TO STATISTICS  
STAT GU4203  PROBABILITY THEORY  
STAT GU4204  STATISTICAL INFERENCE  
STAT GU4205  LINEAR 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  
COMS W1007  
INTRO TO COMP FOR ENG/APP SCI  
Applied Statistical Computing  
Seminar  
ECON GU4918  SEMINAR IN ECONOMETRICS 
Major in MathematicsStatistics
This major program is designed to prepare students 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.
The major requires 14 courses, as follows. (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.):
Code  Title  Points 

Mathematics  
Select one of the following sequences:  
MATH UN1101  CALCULUS I  
MATH UN1102  CALCULUS II  
MATH UN1201  CALCULUS III  
MATH UN2010  LINEAR ALGEBRA  
MATH UN2500  ANALYSIS AND OPTIMIZATION  
OR  
MATH UN1101  CALCULUS I  
MATH UN1102  CALCULUS II  
MATH UN1205  ACCELERATED MULTIVARIABLE CALC  
MATH UN2010  LINEAR ALGEBRA  
MATH UN2500  ANALYSIS AND OPTIMIZATION  
OR  
MATH UN1207  HONORS MATHEMATICS A  
MATH UN1208  HONORS MATHEMATICS B  
MATH UN2500  ANALYSIS AND OPTIMIZATION  
Statistics required courses  
STAT UN1201  CALCBASED INTRO TO STATISTICS  
STAT GU4203  PROBABILITY THEORY  
STAT GU4204  STATISTICAL INFERENCE  
STAT GU4205  LINEAR REGRESSION MODELS  
And select one of the following courses:  
STAT GU4207  ELEMENTARY STOCHASTIC PROCESS  
STAT GU4262  Stochastic Processes for Finance  
STAT GU4264  STOCHASTC PROCSSESAPPLICTNS I  
STAT GU4265  STOCHASTIC 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  
INTRO TO COMP FOR ENG/APP SCI  
COMS W1007  
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. 

Notes:

The mathematics prerequisite can also be satisfied by taking the Honors Mathematics A and B sequence, MATH UN1207 and MATH UN1208.

Students preparing for doctoral study in mathematics or statistics are encouraged to take MATH GU4061 INTRO MODERN ANALYSIS I and MATH GU4062 INTRO MODERN ANALYSIS II.
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.
The political sciencestatistics major requires a minimum of 6 courses in political science, and 7 or 8 courses in statistics & mathematics, to be distributed as follows:
Code  Title  Points 

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:  
INTRO TO AMERICAN POLITICS  
Comparative Politics:  
INTRO TO COMPARATIVE POLITICS  
International Relations:  
INTERNATIONAL POLITICS  
Political Theory:  
POLITICAL THEORY I  
Additionally, students must take a 4point seminar in their primary subfield.  
Research Methods  
Students must take the following two research methods courses:  
POLS GU4710  PRINC OF QUANT POL RESEARCH 1  
or POLS UN3704  RESEARCH DESIGN: DATA ANALYSIS  
POLS GU4712  PRINC OF QUANT POL RESEARCH 2  
Statistics  
Select one of the following two sequences.  
Sequence recommended for students preparing for graduate study in statistics.  
CALCULUS I  
CALCULUS II  
LINEAR ALGEBRA  
CALCBASED INTRO TO STATISTICS  
PROBABILITY THEORY  
STATISTICAL INFERENCE  
LINEAR REGRESSION MODELS  
STAT COMP ＆ INTRO 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 REG ANALYSIS  
APPL CATEGORICAL DATA ANALYSIS  
APPLIED STATISTICAL METHODS  
APPLIED MACHINE LEARNING  
Statistics elective:  
Students must take an approved elective in a statistics or a quantitatively oriented course in a social science. 
Minor in Statistics
The minor requires five courses, distributed as follows.
Students should select one of the following two tracks.
The requirements for the Applied track of the statistics minor are:
Introduction to statistics (one course): Choose one of the following
STAT UN1101 INTRODUCION TO STATISTICS
STAT UN1201 CALCBASED INTRO TO STATISTICS
Applied statistics core (two courses): Take both of the following
STAT UN2102 Applied Statistical Computing
STAT UN2103 APPLIED LINEAR REG ANALYSIS
Statistics electives (two courses): Choose any two of the following
STAT UN2104 APPL CATEGORICAL DATA ANALYSIS
STAT UN3104 Applied Bayesian Analysis
STAT UN3105 APPLIED STATISTICAL METHODS
STAT UN3106 APPLIED MACHINE LEARNING
The requirements for the Theoretical track are:
Introduction to Statistics (one course)
STAT UN1201 CALCBASED INTRO TO STATISTICS
Probability and statistics core (three courses)
STAT GU4203 PROBABILITY THEORY
STAT GU4204 STATISTICAL INFERENCE
STAT GU4205 LINEAR REGRESSION MODELS
Elective (one course)
One additional STAT course numbered 4206 through 4261
For students who entered Columbia in or before the 202324 academic year
Concentrations are available to students who entered Columbia in or before the 20232024 academic year. The requirements for the Bachelor of Arts degree, and the role of the concentration in those requirements, can be found in the Academic Requirements section of the Bulletin dated the academic year when the student matriculated at Columbia and the Bulletin dated the academic year when the student was a sophomore and declared programs of study.
Concentrations are not available to students who entered Columbia in or after Fall 2024.
Concentration in Statistics
The concentration requires 6 courses in statistics, as follows.
Courses taken for a grade of Pass/D/Fail, or in which the grade of D has been received, do not count towards the concentration.
STAT UN1101 INTRODUCTION TO STATISTICS
STAT UN2102 Applied Statistical Computing
STAT UN2103 APPLIED LINEAR REG ANALYSIS
STAT UN2104 APPL CATEGORICAL DATA ANALYSIS
STAT UN3105 APPLIED STATISTICAL METHODS
STAT UN3106 APPLIED MACHINE LEARNING
(Students may replace courses nominally required for the concentration by approved Statistics Department courses.)
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 Calculusbased Introduction to Statistics. Students seeking a onesemester calculusbased 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 INTRO TO STATISTICAL REASONING. 3.00 points.
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 2024: STAT UN1001


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 1001  001/13610  M W 2:40pm  3:55pm 602 Hamilton Hall 
Ronald Neath  3.00  75/86 
STAT 1001  002/13674  M W 10:10am  11:25am 903 School Of Social Work 
ShawHwa Lo, Cindy Meekins  3.00  33/50 
STAT 1001  003/13611  T Th 6:10pm  7:25pm 602 Hamilton Hall 
Victor de la Pena  3.00  66/86 
Fall 2024: STAT UN1001


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 1001  001/15145  T Th 10:10am  11:25am 313 Fayerweather 
Pratyay Datta  3.00  40/75 
STAT 1001  002/15159  M W 6:10pm  7:25pm 717 Hamilton Hall 
Anthony Donoghue  3.00  75/75 
STAT 1001  003/15146  M W 8:40am  9:55am 517 Hamilton Hall 
Musa Elbulok  3.00  18/75 
STAT UN1010 Statistical Thinking For Data Science. 4.00 points.
CC/GS: Partial Fulfillment of Science Requirement
The advent of large scale data collection and the computer power to analyze the data has led to the emergence of a new discipline known as Data Science. Data Scientists in all sectors analyze data to derive business insights, find solutions to societal challenges, and predict outcomes with potentially high impact. The goal of this course is to provide the student with a rigorous understanding of the statistical thinking behind the fundamental techniques of statistical analysis used by data scientists. The student will learn how to apply these techniques to data, understand why they work and how to use the analysis results to make informed decisions. The student will gain this understanding in the classroom and through the analysis of realworld data in the lab using the programming language Python. The student will learn the fundamentals of Python and how to write and run code to apply the statistical concepts taught in the classroom
Spring 2024: STAT UN1010


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 1010  001/13612  M W 1:10pm  2:25pm 516 Hamilton Hall 
Anthony Donoghue  4.00  26/86 
STAT 1010  001/13612  W 2:40pm  3:55pm 516 Hamilton Hall 
Anthony Donoghue  4.00  26/86 
STAT UN1101 INTRODUCTION TO STATISTICS. 3.00 points.
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. Datacollection/analysis project with emphasis on study designs is part of the coursework requirement
Spring 2024: STAT UN1101


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 1101  001/13613  M W 8:40am  9:55am 517 Hamilton Hall 
Alexander Clark  3.00  74/86 
STAT 1101  002/13614  T Th 10:10am  11:25am 602 Hamilton Hall 
David Rios  3.00  70/86 
STAT 1101  003/13615  M W 6:10pm  7:25pm 602 Hamilton Hall 
Banu Baydil  3.00  71/86 
Fall 2024: STAT UN1101


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 1101  001/15160  T Th 6:10pm  7:25pm 602 Hamilton Hall 
Dobrin Marchev  3.00  86/86 
STAT 1101  002/15161  M W 8:40am  9:55am 309 Havemeyer Hall 
Alex Pijyan  3.00  84/200 
STAT UN1201 CALCBASED INTRO TO STATISTICS. 3.00 points.
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, chisquare, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, pvalue, confidence intervals, maximum likelihood estimation. Serves as the prerequisite for ECON W3412
Spring 2024: STAT UN1201


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 1201  001/13616  M W 10:10am  11:25am 517 Hamilton Hall 
Pratyay Datta  3.00  80/86 
STAT 1201  002/13617  M W 8:40am  9:55am 602 Hamilton Hall 
Joyce Robbins  3.00  79/85 
STAT 1201  003/13618  T Th 10:10am  11:25am 702 Hamilton Hall 
Joyce Robbins  3.00  90/86 
STAT 1201  004/13619  M W 6:10pm  7:25pm 702 Hamilton Hall 
Sheela Kolluri  3.00  70/86 
Fall 2024: STAT UN1201


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 1201  001/15162  T Th 8:40am  9:55am 501 Northwest Corner 
Banu Baydil  3.00  160/160 
STAT 1201  002/15163  M W 2:40pm  3:55pm 702 Hamilton Hall 
Chenyang Zhong  3.00  86/86 
STAT 1201  003/15164  M W 6:10pm  7:25pm 702 Hamilton Hall 
Tat Sang Fung  3.00  75/75 
STAT UN1202 UNDERGRADUATE SEM/STATISTICS. 1.00 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 2024: STAT UN1202


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 1202  001/15165  F 10:10am  12:00pm 308a Lewisohn Hall 
Ronald Neath  1.00  8/25 
STAT GU4001 INTRODUCTION TO PROBABILITY AND STATISTICS. 3.00 points.
Prerequisites: Calculus through multiple integration and infinite sums. A calculusbased 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 2024: STAT GU4001


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4001  001/13625  M 6:10pm  8:40pm 142 Uris Hall 
Pratyay Datta  3.00  76/100 
STAT 4001  002/13626  M W 1:10pm  2:25pm 602 Hamilton Hall 
Hammou El Barmi  3.00  68/86 
Fall 2024: STAT GU4001


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 4001  001/15171  M W 6:10pm  7:25pm 301 Pupin Laboratories 
Arian Maleki  3.00  113/200 
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.
STAT UN2102 Applied Statistical Computing. 3.00 points.
Corequisites: An introductory course in statistic (STAT UN1101 is recommended).
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 projectbased assignments, study more advanced topics, such as conditionals, modular programming, and data visualization. Students will also learn the fundamental concepts in computational complexity, and will practice writing reports based on their data analyses
Spring 2024: STAT UN2102


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 2102  001/13620  T Th 4:10pm  5:25pm 428 Pupin Laboratories 
Alex Pijyan  3.00  79/120 
Fall 2024: STAT UN2102


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 2102  001/15166  T Th 4:10pm  5:25pm 517 Hamilton Hall 
Alex Pijyan  3.00  69/86 
STAT UN2103 APPLIED LINEAR REG ANALYSIS. 3.00 points.
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, nonlinear and logistic models, randomeffects models. Implementation in a statistical package. Emphasis on realworld examples and on planning, proposing, implementing, and reporting
Spring 2024: STAT UN2103


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 2103  001/13621  M W 6:10pm  7:25pm 717 Hamilton Hall 
Daniel Rabinowitz  3.00  24/84 
Fall 2024: STAT UN2103


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 2103  001/15167  M W 2:40pm  3:55pm 602 Hamilton Hall 
Ronald Neath  3.00  40/86 
STAT UN2104 APPL CATEGORICAL DATA ANALYSIS. 3.00 points.
Prerequisites: STAT UN2103 is strongly recommended. Students without programming experience in R might find STAT UN2102 very helpful.
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, twoway and threeway contingency tables, logistic regression, loglinear models for large multiway contingency tables, graphical methods. The statistical package R will be used
Spring 2024: STAT UN2104


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 2104  001/13622  M W 8:40am  9:55am 702 Hamilton Hall 
Ronald Neath  3.00  40/86 
STAT UN3105 APPLIED STATISTICAL METHODS. 3.00 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.
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 mixedeffects models, classical nonparametric techniques, the statistical theory causality, sample survey design, multilevel models, generalized linear regression, generalized estimating equations and overdispersion, survival analysis including the KaplanMeier estimator, logrank statistics, and the Cox proportional hazards regression model. Power calculations and proposal and report writing will be discussed
Fall 2024: STAT UN3105


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 3105  001/15169  M W 2:40pm  3:55pm 717 Hamilton Hall 
Wayne Lee  3.00  23/86 
STAT UN3106 APPLIED MACHINE LEARNING. 3.00 points.
Prerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful.
Prerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful. This course is a machine learning class from an application perspective. We will cover topics including databased prediction, classification, specific classification methods (such as logistic regression and random forests), and basics of neural networks. Programming in homeworks will require R
Spring 2024: STAT UN3106


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 3106  001/13623  T Th 2:40pm  3:55pm 332 Uris Hall 
Alex Pijyan  3.00  51/50 
Foundation Courses
The calculusbased 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 corequisite, for the remaining advanced statistical electives.
Code  Title  Points 

STAT GU4203  PROBABILITY THEORY  
STAT GU4204  STATISTICAL INFERENCE  
STAT GU4205  LINEAR REGRESSION MODELS  
STAT GU4206  STAT COMP ＆ INTRO DATA SCIENCE  
STAT GU4207  ELEMENTARY STOCHASTIC PROCESS 
Advanced Statistics Courses
Advanced statistics courses combine theory with methods and practical experience in data analysis. Undergraduates enrolling in advanced statistics courses would be welladvised to have completed STAT GU4203 (Probability Theory), GU4204 (Statistical Inference), and GU4205 (Linear Regression).
STAT GU4221 TIME SERIES ANALYSIS. 3.00 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 BoxJenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course
Spring 2024: STAT GU4221


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4221  001/13633  Sa 10:10am  12:40pm 301 Uris Hall 
Franz Rembart  3.00  6/25 
Fall 2024: STAT GU4221


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 4221  001/15182  T Th 2:40pm  3:55pm 312 Mathematics Building 
Rongning Wu  3.00  23/35 
STAT GU4222 NONPARAMETRIC STATISTICS. 3.00 points.
CC/GS: Partial Fulfillment of Science Requirement
Prerequisites: STAT GU4204 or the equivalent.
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. Nonparametric regression, smoothing and model selection
Spring 2024: STAT GU4222


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4222  001/13678  M W 10:10am  11:25am 501 Schermerhorn Hall 
Alberto Gonzalez Sanz  3.00  0/25 
STAT GU4223 MULTIVARIATE STAT INFERENCE. 3.00 points.
Prerequisites: STAT GU4205 or the equivalent.
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
STAT GU4224 BAYESIAN STATISTICS. 3.00 points.
Prerequisites: STAT GU4204 or the equivalent.
This course introduces the Bayesian paradigm for statistical inference. Topics covered include prior and posterior distributions: conjugate priors, informative and noninformative priors; one and twosample problems; models for normal data, models for binary data, Bayesian linear models; Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software. Prerequisites: A course in the theory of statistical inference, such as STAT GU4204 a course in statistical modeling and data analysis, such as STAT GU4205
Spring 2024: STAT GU4224


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4224  001/13634  T Th 7:40pm  8:55pm 501 Schermerhorn Hall 
Dobrin Marchev  3.00  18/25 
Fall 2024: STAT GU4224


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 4224  001/15183  M W 6:10pm  7:25pm 428 Pupin Laboratories 
Ronald Neath  3.00  31/35 
STAT GU4231 SURVIVAL ANALYSIS. 3.00 points.
Prerequisites: STAT GU4205 or the equivalent.
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 failuretime data. Extensive use of the computer
STAT GU4232 GENERALIZED LINEAR MODELS. 3.00 points.
CC/GS: Partial Fulfillment of Science Requirement
Prerequisites: STAT GU4205 or the equivalent.
Prerequisites: STAT GU4205 or the equivalent. Statistical methods for rates and proportions, ordered and nominal categorical responses, contingency tables, oddsratios, exact inference, logistic regression, Poisson regression, generalized linear models
STAT GU4233 Multilevel Models. 3 points.
Prerequisites: STAT GU4205 or the equivalent.
Theory and practice, including modelchecking, for random and mixedeffects models (also called hierarchical, multilevel models). Extensive use of the computer to analyse data.
STAT GU4234 SAMPLE SURVEYS. 3.00 points.
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
Spring 2024: STAT GU4234


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4234  001/13635  T Th 2:40pm  3:55pm 312 Mathematics Building 
Rongning Wu  3.00  2/7 
STAT GU4241 STATISTICAL MACHINE LEARNING. 3.00 points.
Prerequisites: STAT GU4206.
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 2024: STAT GU4241


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4241  001/13636  M W 10:10am  11:25am 503 Hamilton Hall 
Samory Kpotufe  3.00  15/50 
STAT GU4261 STATISTICAL METHODS IN FINANCE. 3.00 points.
Prerequisites: STAT GU4205 or the equivalent. A fastpaced 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. Handson experience with financial data
Spring 2024: STAT GU4261


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4261  001/13638  Sa 10:10am  12:40pm 501 Schermerhorn Hall 
Zhiliang Ying  3.00  23/25 
Fall 2024: STAT GU4261


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 4261  001/15185  F 10:10am  12:40pm 301 Pupin Laboratories 
Hammou El Barmi  3.00  20/35 
STAT GU4263 STAT INF/TIMESERIES MODELLING. 3.00 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
Fall 2024: STAT GU4263


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4263  001/15186  T Th 6:10pm  7:25pm 301 Pupin Laboratories 
Alberto Gonzalez Sanz  3.00  5/35 
STAT 4263  002/15187  Sa 10:10am  12:40pm 301 Uris Hall 
Franz Rembart  3.00  0/35 
STAT GU4291 ADVANCED DATA ANALYSIS. 3.00 points.
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 handson 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 nonstandard 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 2024: STAT GU4291


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 

STAT 4291  001/13640  F 10:10am  12:40pm 301 Uris Hall 
Gabriel Young  3.00  5/25 
Fall 2024: STAT GU4291


Course Number  Section/Call Number  Times/Location  Instructor  Points  Enrollment 
STAT 4291  001/15149  F 5:10pm  7:40pm 417 International Affairs Bldg 
Demissie Alemayehu  3.00  9/35 
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).
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.
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.
Code  Title  Points 

STAT GU4262  Stochastic Processes for Finance  
STAT GU4264  STOCHASTC PROCSSESAPPLICTNS I  
STAT GU4265  STOCHASTIC METHODS IN FINANCE 