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Columbia University Course/Program Name
Application closes on
National :12 May
International :12 May

MA Statistics (Part-Time)

 Course Level
Masters / PG
 Type
Part Time

 Duration
4 Years
 Start month
August

 Tuition fee

International
36048 USD
National
36048 USD

Application fee

International 105 USD
National 105 USD
Department
Department of Statistics
Scores accepted
IELTS (min)7.5
TOEFL-IBT (min)100
TOEFL-PBT (min)600
GRE (avg)315
15

World University Ranking

About this course

The MA Program is a traditional program completed fully on campus. It may be completed on either a full-time or part-time basis. International students must complete the program on a full-time basis. Most courses have at least one evening section in order to accommodate working students. The full-time program is designed to be completed in three semesters – fall and spring semesters of Year 1 and fall semester of Year 2. Students may also opt to take summer courses. Part-time domestic students must take a minimum of two courses per semester and must complete the program within four years of the first semester of registration.

Check further details on University website

Eligibility Criteria

The Admissions Committee looks for applicants with a compelling background and strong grades in quantitative and related fields such as statistics, mathematics and computer science. Applicants need a thorough knowledge of linear algebra and advanced calculus. Successful candidates have experience in theoretical or applied probability and statistics.  Familiarity with computer programming is highly recommended. 

Interested applicants lacking background in these areas should make up the deficiency before applying. We do not offer conditional acceptance into this program.

Due to the large number of applications received, the MA in Statistics staff does not answer inquiries about specific applications nor give advice or direction to applicants before they are admitted. Although we appreciate interest in the program, please note that emails sent to the department expressing interest have no bearing on the outcome of your application being reviewed. Your candidacy is decided upon by your application materials. Any specific interest and rationale should be conveyed in your personal statement within the application. 

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Course Modules

Core Requirements

The MA program requires completion of at least two (2) Residence Units and a minimum of 30 points. A typical course is worth three points, although some are worth four or more points. Students must meet with with their assigned Faculty Adviser for approval of their study plan each semester prior to course registration. It is the responsibility of the student to meet with his or her adviser. During the semester, students struggling with a course should contact their Faculty Adviser immediately (See Good Academic Standing section). Students must confer with their adviser prior to any change in their course registration.

Required Courses

Students must complete four required core courses and six elective courses, with a total of ten minimum classes for the degree. Of the six electives, at least three must be selected from courses offered by the Statistics Department. The other electives may be chosen from a list of approved courses, depending on the student’s area of interest. Students should review this information with their Faculty Adviser. The four core courses are:

  • GR5203: Probability (a 1/2 semester course worth 3 points) immediately followed by…
  • GR5204 Inference (a 1/2 semester course worth 3 points).

GR 5203 and GR5204 are required to be taken in the first semester.

  • GR5205: Linear Regression Models (3 points) – Required to be taken in the first semester.

Required “Capstone Courses” to be taken in the last semester of the program.  Choose one only:

  • GR5291: Advanced Data Analysis (3 points) OR
  • GR5242 Advanced Machine Learning (3 points) – New Capstone Course.  Please see “New Data Science Sequence” below.

Please note:  Core courses cannot be waived regardless of prior background. 

New Data Science Sequence

There will be a new Capstone Course introduced in Fall 2017:  GR5242  Advanced Machine Learning (3 points). 

  • In order to qualify to take GR5242 Advanced Machine Learning, one must have taken the prerequisite:  GR5241 Statistical Machine Learning.
  • In order to qualify to take GR5241 Statistical Machine Learning, one must complete the prerequisite:  GR5206 Statistical Computing. 

New Data Science Sequence:
GR5206 – Offered in Fall 2016 – Statistical Computing
GR5241 – Offered in Spring 2017 – Statistical Machine Learning
GR5242 – Offered in Fall 2017 – Advanced Machine Learning – Capstone Course

Electives

In addition to the four core courses, students must also complete at least six electives approved by their Faculty Adviser. At least three electives must be selected from the Statistics Department, upon approval by the Faculty Adviser.  Electives may be chosen based upon a student’s area of interest. 

For a thorough grounding in data science, an incoming student is strongly advised to take W4300 (GR5206): Statistical Computing and Intro to Data Science (3 points) in the first semester.  A partial list of approved electives may be found here. 

Check further details on University website

How to Apply

Application Process

All prospective students must apply using the online application. Printed applications are not available.

You must specify in your application the department, doctoral program subcommittee, or free-standing master’s degree program in which you wish to study. If required, you must indicate the sub-field of study and the term for which you are applying. A complete application includes:

  • transcripts of all previous post-secondary education
  • a statement of academic purpose
  • a curriculum vitae or résumé
  • three letters of recommendation from academic sources
  • GRE scores and, if applicable, results of the TOEFL or IELTS examination to fulfill the English Proficiency Requirement
  • a sample of scholarly writing, if required by the department or program
  • payment of the application fee

Check further details on University website

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