Course times and availability are subject to change. Please check Cornell's Class Roster when determining course availability.
Degree Requirements
Required courses for a student’s chosen option are listed below, as in the MPS Data Science and Applied Statistics Application to Graduate
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Both options require a total of at least 30 credit hours in required courses and electives. The 30 required credit hours must be earned while an MPS student is enrolled in the MPS program; no transfer of credits from undergraduate study or another graduate program, etc. is allowed.
- All required courses must be taken for a letter grade.
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At most one elective course to be used toward the MPS degree can be taken S/U each semester.
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Option I students can take STSCI 5045, STSCI 5060, and STSCI 5065 as electives and so may take these courses S/U. For option II students, these courses are required and must be taken for a letter grade.
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A grade of C- or better (or S for S/U courses) is required for all courses used to meet MPS requirements.
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A GPA of 2.5 or higher in courses used toward the MPS degree is required for graduation.
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Electives must be taken from the list below of approved electives.
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Students can ask the Director of the MPS Program to add courses to the list of electives. With few exceptions, to be approved by the Director, a course must be a technical course numbered 5000 level or higher and have substantial statistics content. To submit a course waiver or application for a new elective to be counted toward your degree credits, please complete the Course Waiver - Application for Elective Form.
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Two courses covering similar material and at the same level cannot both be used toward the 30 credit hours for the MPS degree.
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The MPS Program in Data Science and Applied Statistics at Cornell University is a one-year program. Under special circumstances, before the end of the first year, a student may formally request an extension of their study.
Our MPS program is very demanding. Every year we have MPS students who find that the intensive workload causes them much stress. To improve their academic performance and health (including mental health), they often reduce their workload and complete the MPS degree by extending their programs for a longer time. Such an extension is allowed as long as the university rules allow. However, an MPS student must complete his/her MPS degree in no longer than two years.
Please provide the new graduate date and a compelling academic reason that causes you as the student not to graduate as expected. List out the specific courses you plan to take in the extension semester.
Some examples are:
* "I need to take STSCI 5080: Probability Models and Inferences because it's a Core Required Course and share why you didn't take the course in previous semesters."* "I need to take STSCI 5040: R Programming for Data Science because I need 4 more credits to reach 12 Statistical credits for Option 1 and share why you didn't take the course in previous semesters." or
* "I need to take STSCI 5045: Python Programming and its Applications in Statistics to fulfill the requirements of Option 2 and share why you didn't take the course in previous semesters."
We need this information also listed on the F-1 immigration extension request form because it's a requirement and is kept in the student’s record in case we are audited by the government.
For an extension, regulations require that the delays are caused by compelling academic or medical reasons, such as changes of major or research topics, unexpected research problems, or documented illnesses. An extension cannot be done for sake of employment or for administrative purposes.
If you do not obtain an approved extension before the end date on your I-20 or DS-2019, you’ll be in violation of your immigration status.
Core Required Courses
STSCI 5030: Linear Models with Matrices (4 crs.) Fall
STSCI 5080: Probability Models and Inference (4 crs.) Fall/Spring
STSCI 5954: Project Development & Professional Communication (2 crs.) Fall
STSCI 5955: Realtime Project Management (1 crs.) Spring
STSCI 5999: Applied Statistics MPS Data Analysis Project (4 crs.) Spring
Additional Required Courses for Option II
STSCI 5045: Python Programming and its Applications in Statistics (4 credits) Fall
STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits) Fall
STSCI 5065: Big Data Management and Analysis (3 credits) Spring
Statistical Science Electives
Option I students must take at least 12 credit hours from the list of Statistical Science Elective courses and Option II students at least 4 credits of Statistical Science Electives courses from the list of Statistical Science Elective courses. Option II students cannot use STSCI 5045, 5060, or 5065 as a statistical science elective since these courses are required as core option II courses.
STSCI 5010: Applied Statistical Computation with SAS (4 credits)
STSCI 5040: R Programming for Data Science (4 credits)
STSCI 5045: Python Programming and its Applications in Statistics (4 credits)
STSCI 5050: Modern Regression Models for Data Science (4 credits)
STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)
STSCI 5065: Big Data Management and Analysis (3 credits)
STSCI 5090: Theory of Statistics (4 credits)
STSCI 5100: Statistical Sampling (4 credits)
STSCI 5111: Multivariate Analysis (4 credits)
STSCI 5140: Applied Design (4 credits)
STSCI 5160: Categorical Data (3 credits)
STSCI 5520: Statistical Computing (4 credits)
STSCI 5270: Introduction to Survival Analysis and Loss Models (3 credits)
STSCI 5550: Applied Time Series Analysis (4 credits)
STSCI 5600: Integrated Ethics in Data Science (2 credits)
STSCI 5610: Data Science in Risk Modeling (2 credits)
STSCI 5630: Operations Research Tools for Financial Engineering (4 credits)
STSCI 5640: Statistics for Financial Engineering (4 credits)
STSCI 5740: Data Mining and Machine Learning (4 credits), forbidden overlap with CS 5780, ORIE 5740 or ORIE 5741
STSCI 5750: Understanding Machine Learning (4 credits)
STSCI 5780: Bayesian Data Analysis: Principles and Practice (4 credits)
STSCI 6520: Statistical Methods I (4 credits)
STSCI 6780: Bayesian Statistics and Data Analysis (3 credits)
Other Approved MPS Electives
Other Approved MPS Electives count toward the 30 required credits needed to achieve the MPS Applied Statistics degree but do not count towards the 12 Statistical Science Elective credits needed for Option 1 or the 4 Statistical Science Elective credits required for Option 2.
AEM 7100: Econometrics I (3 credits)
BIOCB 6381: Biomedical Data Mining and Modeling (3 credits) When Offered Fall.
BIOCB 6840: Computational Genetics and Genomic (4 credits) When Offered Fall.
CS 5740: Natural Language Processing (4 credits)
CS 5780: Machine Learning (4 credits)
CS 5777: Principles of Large-Scale Machine Learning (4 credits) On a case-by-case basis - Advisor Approval Needed
CS 6368: Data to Decisions (4 credits) On a case-by-case basis - Advisor Approval Needed
ORIE 5160: Topics in Data Science and OR (3 credits)
ORIE 5510: Introduction to Engineering Stochastic Processes I (4 credits)
ORIE 5580: Simulation Modeling & Analysis (4 credits)
ORIE 5581: Monte Carlo Simulation (2 credits)
ORIE 5600: Financial Engineering with Stochastic Calculus I (4 credits)
ORIE 5610: Financial Engineering with Stochastic Calculus II (4 credits)
ORIE 5741: Learning with Big Messy Data (4 credits)
ORIE 6500: Applied Stochastic Processes (4 credits)
ORIE 6741: Bayesian Machine Learning (3 credits)
Application to Graduate
Complete the MPS Data Science and Applied Statistics Application to Graduate form online when you are ready to confirm the courses you have taken for your degree.
Last updated: August 19, 2024