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MPS Degree Requirements and Regulations

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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 is the PDF icon MPS Application to Graduate

  1. 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.

  2. All required courses must be taken for a letter grade, except STSCI 5953 which is S/U only.
  3. At most one elective course to be used toward the MPS degree can be taken S/U each semester.

  4. 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.

  5. A grade of C- or better (or S for S/U courses) is required of all courses used to meet MPS requirements.

  6. A GPA of 2.5 or higher in courses used toward the MPS degree is required for graduation.

  7. Electives must be taken from the list below of approved electives.

  8. 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.

  9. Two courses covering similar material and at the same level cannot both be used toward the 30 credit hours for the MPS degree.

  10. The MPS Program in 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. 

Core Required Courses

STSCI 5030: Linear Models with Matrices (4 credits)
STSCI 5080: Probability Models and Inference (4 credits)
STSCI 5953: MPS Career Management (1 credit)
STSCI 5954: Project Development & Professional Communication (2 credits)
STSCI 5955: Realtime Project Management (1 credit)
STSCI 5999: Applied Statistics MPS Data Analysis Project (4 credits)

Additional Required Courses for Option II

STSCI 5045: Python Programming and its Applications in Statistics (4 credits)
STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)
STSCI 5065: Big Data Management and Analysis (3 credits)

Statistical Science Electives

Option I students must take at least 12 credit hours and Option II students at least 4 credits of Statistical Science electives from this list. 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 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 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 or ORIE 5740
STSCI 5750: Understanding Machine Learning (4 credits)
STSCI 5780: Bayesian Data Analysis: Principles and Practice (4 credits)
STSCI 6070: Functional Data Analysis (3 credits)
STSCI 6520: Computationally Intensive Statistical Methods (4 credits)
STSCI 6780: Bayesian Statistics and Data Analysis (3 credits)

 

Other Approved MPS Electives

AEM 7100: Econometrics I (3 credits)
BTRY 6381: Bioinformatics Programming (3 credits)
BTRY 6830: Quantitative Genomics and Genetics (4 credits)
BTRY 6840: Computational Genetics and Genomics (4 credits)
CS 5780: Machine Learning (4 credits)
CS 5786: Machine Learning for Data Science (4 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

PDF icon MPS Application to Graduate

Last updated: October 31, 2022

In This Section

  • Program Options
  • MPS Degree Requirements and Regulations
  • Should You Choose the MPS or PhD Program?
  • MPS Early Admit Option
  • MPS Projects
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  • Computing Resources
  • MPS Careers and Post Graduate Surveys
  • MPS FAQs

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  • MPS Application to Graduate (pdf, 220.12 KB)
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