Admittance into any course is not guaranteed.  All courses are subject to availability.

The approved courses listed below are available for elective credit in the M.A. in Statistics Program.  The Columbia Course Directory lists the available courses for each semester.

The MA Program requires the equivalent of six (3-pt) elective courses, of which at least three elective courses (9-pt total) must be offered by the Statistics Department.  Details of course requirements for graduation are HERE

All students should consult with their Faculty Adviser prior to selecting electives each semester. It is recommended to use the MA PROGRAM COURSE CHECKLIST to track your requirements and courses.

Topics and seminar courses are generally not approved, although Topics in Modern Statistics GR5293 may be taken once as an approved elective.  Statistics courses offered in other departments, other than the ones listed below, are generally not approved.

## Approved Electives

Statistics

### STATISTICS ELECTIVE COURSES (MUST COMPLETE A MINIMUM OF 3 TOTAL)

 STAT # Course Title STAT GR5206 Statistical Computing and Intro to Data Science STAT GR5207 Elementary Stochastic Processes STAT GR5221 Time Series Analysis STAT GR5222 Nonparametric Statistics STAT GR5223 Multivariate Stat Inference STAT GR5224 Bayesian Statistics STAT GR5231 Survival Analysis STAT GR5232 Generalized Linear Models STAT GR5233 Regression and Multi-Level Models STAT GR5234 Sample Surveys STAT GR5241 Statistical Machine Learning STAT GR5242 Advanced Machine Learning STAT GR5243 Applied Data Science STAT GR5245 Introduction to TensorFlow with Python (1 pt) STAT GR5261 Statistical Methods in Finance STAT GR5262 Stochastic Processes for Finance STAT GR5263 Stat Inf/Time-Series Modeling STAT GR5264 Stochastic Processes Applications I STAT GR5265 Stochastic Methods in Finance STAT GR5399 Statistical Fieldwork STAT GR5293* Topics in Modern Statistics STAT GR5398** MA Mentored Research

### PHD LEVEL COURSES IN STATISTICS

Selected PhD level statistics courses may be open to MA Statistics Students only through an application process. For more information on the process and how to apply, click HERE.

Statistics Electives by Interest Area

Below are electives a student might select based on interest area. These courses are recommended as a guide in course selection; however, they are not required.

Admittance into any course below is not guaranteed.  All courses are subject to availability.

### FINANCE

 GR5221 Time Series Analysis GR5207 Elementary Stochastic Processes GR5264 Stochastic Processes-Applications I GR5263 Stat Inf/Time Series Modelling GR5265 Stochastic Methods in Finance GR5261 Statistical Methods in Finance

### DATA SCIENCE SEQUENCE IN STATISTICS

 GR5206 Stat Computing & Intro to Data Science (prerequisite for GR5241) GR5241 Statistical Machine Learning (prerequisite for GR5242) GR5242 Advanced Machine Learning

*Note: The Data Science sequence includes GR5206 offered in the fall which is a prerequisite for GR5241 offered in the spring. Both courses are a prerequisite for the course, GR5242: Advanced Machine Learning.

### PHARMACEUTICAL INDUSTRY OR PUBLIC HEALTH

 GR5232 Generalized Linear Models GR5222 Non-Parametric Statistics GR5231 Survival Analysis

### ACTUARIAL SCIENCE AND THE INSURANCE INDUSTRY

 GR5231 Survival Analysis PS5823 Actuarial Models GR5207 Elementary Stochastic Processes

Actuarial Science
 Course Course Title PS5821 Actuarial Methods I (Must have demonstrated interest in AS) PS5822 Actuarial Methods II PS5823 Actuarial Models PS5830 Models for Financial Economics (Formerly known as Stochastic Processes for Actuaries) PS5846 Quantitative Risk Management
Biostatistics
 Course Course Title PUBH P8108 Survival Analysis (Prerequisites: PUBH P6103/4, STAT GR5203, GR5204 and GR5205) PUBH P8116 Design of Medical Experiments (Prerequisites: PUBH P6103/4, STAT GR5203, 5204 and 5205) PUBH P8121 Generalized Linear Models (Prerequisites: PUBH P6103/4, STAT GR5203, GR5204, and GR5205) PUBH P8139 Theoretical Genetic Modeling (Prerequisites: PUBH P6103/4) PUBH P8140 The randomized clinical trial I (Prerequisites: PUBH P6103/4) PUBH P8142 The randomized clinical trial II (Prerequisites: PUBH P6103/4, P8140)

Important:

Biostat 6000 level courses are not approved for graduate credit in the MA Program in Statistics.

Instructions for cross-registration:

1. Graduate students interested in taking Columbia Public Health courses must first complete the Cross-Registration Form.

2. Obtain permission from the Public Health department(s) that offers the course(s) for which you are cross-registering. It is important to note that the process of obtaining permission to enroll in a course as a cross-registrant is approved first by the department then by the Office of Enrollment Management (OEM).

3. Forward your completed Cross-Registration Form, as well as any other email correspondences used in lieu of signatures, to the Columbia Public Health OEM team(link sends e-mail). OEM will review and return approved forms to requesting students.

4. If allowed to take a course, the email from OEM will include your completed and approved form(s). Complete the registration for the course through your school’s primary office for registration. Any change thereafter (dropping the course etc.) would also be made through your school of primary registration

Columbia Business School – Finance & Business Economics Courses
 Course Course Title ACCT B8008 Earnings Quality & Fundamental Analysis FINC B8306 Capital Markets & Investments FINC B8307 Advanced Corporate Finance FINC B8308 Debt Markets FINC B8318 Investment Banking Tax Factors FINC B8323 Asset Management FINC B8325 Mergers & Acquisitions FINC B8326 Capital Markets Regulation FINC B8333 Real Estate Capital Markets FINC B8347 Financial Crises and Regulatory Responses FINC B8348 Emerging Financial Markets FINC B8362 Project Finance FINC B8368 Security Analysis FINC B8384 Equity Derivatives FINC B8394 Private Equity: the asset class, its investments & its markets FINC B8396 Institutional Investing: Alternative Assets in Pension Plans FINC B8423 Investor Influence on Corporate Sustainability FINC B8424 Competitive Advantage in Investing BUEC B8250 Global Economic Environment II

Instructions for cross-registration:

Information about cross-registration can be found here. Questions? Email crossreg@gsb.columbia.edu

Economics
 Course Course Title ECON GU4415 Game Theory ECON GU4301 Economic Growth & Development
Industrial Engineering & Operations Research (IEOR)
 Course Course Title IEOR E4403 Advanced Engineering and Corporate Economics IEOR E4404 Simulation IEOR E4407 Game-Theoretical Models of Operations IEOR E4412 Quality Control and Management IEOR E4700 Introduction to Financial Engineering IEOR E4725 Networks: Formation, Contagion, and Epidemics

Instructions for cross-registration:

1. The IEOR department will open up select courses each semester for cross-registration on the first Friday of the semester via the SSOL course waitlist system.
2. All necessary pre-requisite requirements for the course must be satisfied.
3. A list of available courses for non-IEOR students for cross registration will be provided closer to the term start. Courses not listed are closed and/or reserved for IEOR students in a degree program.

Questions? Please contact ieor-cross-reg@columbia.edu

Machine Learning/Data Science/Computer Science

Important Note:  Students may take up to two courses from the list below to count toward graduation.  Students must consult with their Faculty Adviser about potential overlap in Data Science/Machine Learning courses when exploring courses not on the list below.

Computer Science courses open to students in other departments, including Statistics students, during the Change of Program period. Enrollment is determined by the CS Department and are not guaranteed. CS course registration policy and further information can be found at: https://www.cs.columbia.edu/cs-course-registration-policy/

Non-Data Science students will be able to join a waitlist via SSOL.  Space permitting, Non-DS students will be moved from the waitlist to the class roster during the first week of classes. Questions about Data Science courses? Email DataScience-Registration@columbia.edu

 Course Course Title U6506 Data Science & Public Policy APMA E4990 Introduction to Data Science in Industry BINF G4006 Translational Bioinformatics COMS W4111 Introduction to Databases* COMS W4121 Computer Systems for Data Science (Spring course only) COMS 4705 Natural Language Processing COMS 4775 Causal Inference 1 COMS 4995* Causal Inference for Data Science COMS E 6998** Cloud Computing & Big Data CSOR W4231 Analysis of Algorithms I CSOR W4246 Algorithms for Data Science CSEE W4119 Computer Networks ECBM E4040 Neural Networks & Deep Learning ECBM E6040 Neural Networks and Deep Learning (Research) EECS E6720 Bayesian Mod Machine Learning EECS E6893 Big Data Analytics EECS E6895 Adv. Big Data Analytics EECS E6894 Deep Learning for Computer Vision and Natural Language Processing

*COMS 4995 Sections (if not specifically included on the list above) must be individually approved by the Faculty Adviser

**Only COMS E 6998 Cloud Computing & Big Data counts, other topics do not count towards graduation and require Faculty Adviser’s approval

Mathematics
 Course Course Title MATH GU4061 Introduction to Modern Analysis I MATH GU4062 Introduction to Modern Analysis II MATH GU4155 Probability Theory MATH GR5010 Introduction to the Theory of Mathematical Finance MATH GR5030 Numerical Methods in Finance MATH GR5220 Quantitative Methods in Investment Management MATH GR5280 Capital Markets & Invest MATH GR5300 Hedge Funds Strategies & Risk MATH GR5320 Financial Risk Management & Regulation MATH GR5340 Fixed Income Portfolio Mgmt MATH G5360 Math Mthds-Fin Price Analysis MATH GR5380 Multi-Asset Portfolio Mgmt MATH GR5400 Non-Linear Option Pricing MATH GR6151 Analysis & Probability I MATH GR6307 Algebraic Topology MATH GR6308 Algebraic Topology II

### APPLIED MATHEMATICS COURSE

APMA 4300 – Introduction to Numerical Methods

Political Science
 Course Course Title POLS GU4720 Applied Regression and Causal Inference POLS GU4724 Experimental Methods
Other

INAF U6506 Data Science & Public Policy – A managed waitlist will become available to MA Statistics students up to seven days before the first week of classes.  SIPA students will receive priority, but if spaces are available, MA Statistics students will be admitted.

Approval Request for courses not listed on this page

For courses not listed:

• The first step is to obtain the syllabus and share it with your Faculty Adviser for approval.  A Faculty Adviser may approve a course for a particular student’s schedule to count for graduation.  However, it is recommended that the faculty adviser seek input from the MA Program Faculty Directors.
• The second step is to fill out and submit the ELECTIVES APPROVAL FORM.

## NON-APPROVED COURSES

The following courses are NOT approved for the Statistics MA degree requirement:

• 3000 level courses
• Courses offered by the Applied Analytics Program in the School of Professional Studies
• STAT GU4281 or K5281 – Theory of Interest
• STAT GU4282 – Linear Regression and Time Series Models
• Courses for Data Science Institute students only:  STAT GR5701, 5702, 5703
• SIEO W4150 – Intro Prob & Stat Inference
• IEOR E4106 – Stochastic Models
• IEOR E4540 – Data Mining for Engineers
• ECON W4412 – Ad Econometrics or other 4000-level Econometrics courses
• ECON W4413 – Econometrics of Time Series
• QMSS W5067 – Natural Language Processing
• Finance 6000 – level courses
• Biostat 6000 – level courses

Many topics and seminar courses are not approved to fulfill the MA degree requirement.