Masters in Analytics
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How to Apply

Start Your Application for Summer 2015!The M.S. in Analytics program starts once each year in July (summer admission). The early deadline for admission and scholarship consideration is December 4. The final application deadline is March 1.

To start your application, click here for important application instructions. Please also read the information below for additional instructions specific to this program.

Bachelor's Degree Background

Applicants who hold a Bachelor’s Degree in any field and have fulfilled the admission requirements (see below) are considered for admission. Applicants with academic or professional backgrounds in math, computer science, engineering, finance, economics or equivalent skills are encouraged to apply.

Admission Requirements

These course requirements must be completed at an accredited college or university before starting the graduate program:

  • Inferential Statistics
  • Linear Algebra
  • Programming experience (abilities described below) in one or more of the following: Java, Mathematica, Matlab, Python, C++:
    • Ability to write structured programs in a high-level language (for example: objects, methods, functions)
    • Ability to read/write data from files
    • Facility with the three basic control structures (block, conditional, iteration)
    • Understanding of variables and data types (int, float, string, boolean)
    • Familiarity with basic data structures (list, dictionary, stack, queue)

Other courses or experience (recommended but not required for admission):

  • Calculus
  • Data Structures
  • Programming in R

Statement of Purpose

Upload a 1-2 page statement that covers your educational and work experience, as it relates to the program, and career goals. You can also use this statement to explain any deficiencies in your academic record (e.g., failing or low grades in quantitative courses such as math, economics, computer science, or engineering; a low GPA in one semester, year or degree program; lack of relevant coursework that is compensated by self-study or work experience, etc.), gaps in educational or employment history, reasons for low quantitative or verbal scores on the GRE or GMAT examinations, etc.