Master of Arts in Statistics: Specialized Curriculum Tracks

Beginning in Fall 2026, the Department of Statistics at Columbia University offers three specialized tracks within the prestigious Master of Arts in Statistics program. These carefully curated pathways are designed to provide students with the ultimate flexibility to tailor their academic journey, enabling highly specialized, focused experiential learning that directly aligns with their unique career aspirations.

Regardless of the chosen path, all Master of Arts in Statistics candidates complete a rigorous, 10-course, minimum 30-credit curriculum. The standard timeline for full-time students is three semesters to graduation. To satisfy the degree requirements, students in all tracks complete:

• Three Statistics Core Courses: Probability Theory, Statistical Inference, and Linear Regression Models—which establish a bulletproof theoretical foundation.
• Track-Specific Core Requirements: Focused foundational coursework unique to each track’s specialized domain.
• Track-Specific Capstone Experience: Hands-on, experiential projects that translate theoretical models into real-world, high-impact data solutions.
• General & Track Electives: A broad selection of courses drawing from the department’s world-class offerings to build custom technical depth.

1. General Statistics Track

The General Statistics Track is designed for students seeking a well-rounded and versatile statistical foundation. This track offers the maximum academic flexibility, enabling students to explore a broad range of statistical methodologies and applications across diverse industry and academic domains. By allowing students to curate their own list of electives, it is perfect for individuals with multidisciplinary interests or those preparing for advanced doctoral research.

Curricular Framework & Requirements

Students must complete 10 courses (minimum 30 credits) with a minimum cumulative GPA of 3.0 to graduate. The course requirements are divided as follows:

General Statistics Track Designation Requirements

Graduating with the General Statistics Track Designation on the degree certificate requires students to achieve a cumulative overall GPA of 3.0 or higher at the time of graduation, in addition to completing all standard program, elective, and capstone requirements.

2. Advanced Machine Learning Track

This track bridges fundamental statistical theory and cutting-edge machine learning and data science while preparing candidates for technical leadership roles in industry and expertise in modern analytics.

Curricular Framework & Requirements

Students must complete a 10-course (minimum 30-credit) curriculum. The structural breakdown is detailed in the table below:

Table 1: Approved Electives For The Track

Students must select at least one course from this list. Note: Enrollment in Topics in Modern Statistics (STAT GR5293) or any track-specific electives offered by external programs not listed below requires explicit prior approval from the student’s faculty advisor.

Advanced ML Track Designation Requirements

Students must follow the following criteria: 

• Good Academic Standing: By the end of the second semester, earn a grade of B+ or higher in EACH of the following five required core and technical courses:
    • STAT GR5203 (Probability Theory)
    • STAT GR5204 (Statistical Inference)
    • STAT GR5205 (Linear Regression Models)
    • STAT GR5206 (Programming for Data Science)
    • STAT GR5241 (Statistical Machine Learning)
• Graduation GPA Requirement: Achieve a cumulative overall GPA of 3.3 or higher at the time of graduation, in addition to completing all standard program, track-specific, elective, and capstone requirements.

Academic Safeguard: Students initially enrolled in the ML Track who do not meet these specific grade or GPA performance thresholds will still graduate with the standard Master of Arts in Statistics degree, provided they maintain a cumulative GPA of at least 3.0 in the 10 courses required for the other curriculum tracks.

3. Risk & Financial Modeling Track

Leveraging the Department of Statistics’ renowned quantitative expertise, the Risk & Financial Modeling Track is meticulously designed for students targeting high-impact careers in finance, Fintech, quantitative finance, risk analysis, and insurance. It provides a potent combination of advanced statistical modeling and state-of-the-art computational finance techniques.

Curricular Framework & Requirements

Students must complete 10 courses (minimum 30 credits) with a minimum cumulative GPA of 3.0 to graduate. The course requirements are divided as follows:

Table 2: Risk & Financial Modeling Approved Track-Specific Electives

Students must select at least two courses from this list. Note: Enrollment in Topics in Modern Statistics (STAT GR5293) or any track-specific electives offered by other programs not listed below requires explicit prior approval from the student’s faculty advisor.

Risk & Financial Modeling Track Designation Requirements

Graduating with the Risk & Financial Modeling Track Designation on the degree certificate requires students to achieve a cumulative overall GPA of 3.0 or higher at the time of graduation, in addition to completing all standard program, track-specific, elective, and capstone requirements.

4. Enrollment, Academic Standing, and Transfer Pathways

To ensure students are set up for academic and professional success, the Department of Statistics maintains strict standing guidelines and structured pathways for transferring between curriculum tracks. These mechanisms guarantee that all candidates possess the prerequisite computational and mathematical maturity required by their chosen pathways.

Official Inter-Track Change Pathways

With the exception of certain project-oriented courses that are reserved for specific tracks due to space limitations, students in any track may enroll in most courses offered across the MA program, including Advanced ML courses, provided they meet the relevant prerequisites.

Students initially enrolled in one track may request an official transfer into another specialized track after completing their second semester in the program.

Because space is limited in some project-oriented Advanced ML courses, transfer into the Advanced ML track is available only to students who earn a grade of B+ or higher in each of the following five foundational courses:

• STAT GR5203 Probability Theory 
• STAT GR5204 Statistical Inference 
• STAT GR5205 Linear Regression Models 
• STAT GR5206 Programming for Data Science 
• STAT GR5241 Statistical Machine Learning 

Track change requests will be reviewed according to the program’s established eligibility requirements after the completion of the second semester through the official Track Change Request form.

Students interested in changing tracks are strongly encouraged to plan their first-year coursework carefully, with particular attention to the required transition courses for their intended track. Students should consult their faculty advisor or the Academic and Student Affairs team early to assess feasibility and ensure they are on an appropriate path.

Quick Links:

• General Statistics Track – Undeclared
• Advanced Machine Learning Track
• Theory & Methods Honors Track
• Risk & Financial Modeling Track
• Advanced Statistical Practice Track

Master of Arts in Statistics Tracks

Beginning in Fall 2025, the Department of Statistics will offer Master of Arts in Statistics Tracks, providing students with flexibility in selecting highly specialized, carefully curated, and focused experiential learning pathways. All of the elective courses, as well as the topic-specific Capstone Experiences are derived from the department’s extensive and rich range of existing course offerings. This concentrated curriculum affords students the opportunity to master the specific knowledge and skills they seek in the most highly-sought after fields for aspiring statisticians. 

The traditional MA in Statistics requires students to complete:

• Three Statistics Core (Probability Theory, Statistical Inference, Linear Regression),
• Six elective courses (at least three offered by the Statistics Department), and
• One capstone course with a project-based component.

Under the new Tracks model, students complete the same shared Statistics Core, followed by a combination of:

• General Electives,
• Track-Specific Electives, and
• a Track-Specific Capstone Experience.

Track electives and capstone options draw from the department’s broad existing course offerings, allowing students to build depth in the areas most aligned with their academic and professional goals.

Time to Degree: All MA degrees must be completed within three semesters for full-time students.

MA Track Declaration and Change Guidelines

Students have two opportunities to declare an  MA track:

First Opportunity: Track selection at initial enrollment

• Students are admitted directly into a track at the time of initial enrollment.

Second Opportunity: Track declaration after the second semester

• Students may request a track change after completing their second semester. The Track Change Request Form is distributed and processed only at the beginning of the Fall and Spring semesters during the designated track change period. 

Eligibility for Changing Tracks:

• Students may only request a change to a track after completing the second semester.
• Students who complete the program in two semesters are not eligible to request a track change. 
• Students may not transfer into the Theory and Methods track from other tracks.
  
• A change request in the General Statistics track is automatically approved.
• Students who do not declare a track will graduate with a General Statistics track degree (with no track designation listed on their transcript).

Rule for Changing Tracks:

To switch into a different track, students must demonstrate the ability to complete the intended Track within 3 semesters. This requires completion of either:

• two Track-specific electives, or
• one Track-specific elective and the track-specific capstone experience,

with a minimum grade of B+ in each required course.

Theory and Methods Qualifying Exam

Students who do not pass the Theory and Methods qualifying exam must apply to change into another track. If they do not apply, they will be automatically placed into the General Statistics track. For this automatic change, students must have completed one track-specific elective with a grade of A- or higher.

Important Note on Time to Degree

A track declaration and/or a change of track declaration DOES NOT qualify a student for a program extension.

General Statistics Track – Undeclared

MA in Statistics students in the General Statistics (Undeclared) track must complete the following requirements.

Course Requirements

Statistics Core (3 Courses)

• Probability Theory (STAT GR5203)
• Statistical Inference (STAT GR5204)
• Linear Regression Models (STAT GR5205)

General Electives (6 Courses)

• Six (3-credit) general elective courses from Statistics or Approved Electives list.

* Of the six total electives, at least three must be offered by the Statistics Department.

Capstone Experience (1 Course)

Complete one of the following:

• Advanced Data Analysis (STAT GR5291), or
• Advanced Machine Learning (STAT GR5242).

Students must complete 10 courses (minimum 30 credits) to graduate. For additional details on overall program requirements, please visit this PAGE.

Advanced Machine Learning Track

The Advanced Machine Learning Track is designed for students with interests in technology, machine learning, and data science. This track equips MA in Statistics Students with fundamental statistical knowledge while offering advanced training in the fields of statistical machine learning and data science.

Completion of these requirements satisfies the 10-course (minimum 30-credit) graduation requirement. For full program details, please visit this page.

Course Requirements

Statistics Core (3 Courses)

• Probability Theory (STAT GR5203)
• Statistical Inference (STAT GR5204)
• Linear Regression Models (STAT GR5205)

Track Specific Electives and General Electives (6 Courses)

Students must complete six electives (3 credits each), consisting of:

 1) Track Required Electives (2 Courses)

• Stat Computing & Intro to Data Science (STAT GR5206)
• Statistical Machine Learning (STAT GR5241)

*GR5206 and GR5241 are the prerequisites for STAT GR5242.

2 ) Track Specific Electives and General Electives (4 Courses)

• One track-specific elective (selected from the approved list in Table 1; see below), and
• Three (3-credit) general electives (selected from Statistics or the Approved Electives list).

* Of the six total electives, at least three must be offered by the Statistics Department.

Capstone Experience (1 Course)

•  Advanced Machine Learning (STAT 5242)

Notes: Enrollment in Topics in Modern Statistics (STAT GR5293) and any track-specific electives offered by other programs that are not listed in Table 1 requires approval from the student’s faculty advisor.

Table 1: Advanced Machine Learning Approved Track-Specific Electives

Theory & Methods Honors Track

The Theory and Methods Honors Track offers students rigorous training in probability theory and mathematical statistics, along with valuable research experience. A key requirement is the Theory & Methods Core Competency Exam, which students must pass before their final semester. Success in this exam is necessary to graduate from the Theory & Methods Track and enables students to collaborate with faculty on an honors capstone project.

Course Requirements

Completion of these requirements satisfies the 10-course (minimum 30-credit) graduation requirement. For full program details, please visit this page.

Statistics Core – Honors (3 Courses)

• Honors Probability Theory
• Honors Statistical Inference
• Honors Linear Regression Models

Track Specific Electives and General Electives (6 Courses)

Students must complete six electives (3 credits each), consisting of:

• Three approved track-specific electives (selected from the approved list in Table 2; see below), and
• Three (3-credit) general electives (selected from Statistics or the Approved Electives list).

* Of the six total electives, at least three must be offered by the Statistics Department.

Capstone Experience (1 Course)

• Advanced Data Analysis (STAT GR5291, Section 003)

Theory & Methods Honors Core Competency Exam

The Core Competency Exam is administered at the end of the second semester (typically in late May).

Table 2: Theory & Methods Approved Track-Specific Electives 

Risk & Financial Modeling Track

The Risk & Financial Modeling Track is designed for students interested in finance, fintech, quantitative finance, insurance, and other risk-related fields. It offers MA in Statistics students foundational statistical knowledge along with advanced training in risk and financial modeling, an area of strength in the Department of Statistics at Columbia.

Course Requirements

Completion of these requirements satisfies the 10-course (minimum 30-credit) graduation requirement. For full program details, please visit this page.

Statistics Core (3 Courses)

• Probability Theory (STAT GR5203)
• Statistical Inference (STAT GR5204)
• Linear Regression Models (STAT GR5205)

Track Specific Electives and General Electives (6 Courses)

Students must complete six electives (3 credits each), consisting of:

 1) Track Required Electives (1 Courses)

• Statistical Methods in Finance (STAT GR5261)

2 ) Track Specific Electives and General Electives (5 Courses)

• Two track-specific electives (selected from the approved list in Table 3; see below), and
• Three (3-credit) general electives (selected from Statistics or the Approved Electives list).

* Of the six total electives, at least three must be offered by the Statistics Department.

Capstone Experience (1 Course)

• Advanced Data Analysis (STAT GR5291; Section 001)

Notes: Enrollment in Topics in Modern Statistics (STAT GR5293) and any track-specific electives offered by other programs that are not listed in Table 3 requires approval from the student’s faculty advisor.

Table 3: Risk & Financial Modeling Approved Track-Specific Electives 

Advanced Statistical Practice Track

The Advanced Statistical Practice Track offers traditional statistical training and is best suited to students with industry experience or a background in computing and applied research. This track is especially suitable for working professionals who wish to pursue the MA program on a part-time basis.

Course Requirements

Completion of these requirements satisfies the 10-course (minimum 30-credit) graduation requirement. For full program details, please visit this page.

Statistics Core (3 Courses)

• Probability Theory (STAT GR5203)
• Statistical Inference (STAT GR5204)
• Linear Regression Models (STAT GR5205)

Track Specific Electives and General Electives (6 Courses)

Students must complete six electives (3 credits each), consisting of:

• Three track-specific electives (selected from the approved list in Table 4; see below), and
• Three (3-credit) general electives (selected from Statistics or the Approved Electives list).

* Of the six total electives, at least three must be offered by the Statistics Department.

Capstone Experience (1 Course)

• Advanced Data Analysis (STAT GR5291; Section 002)

Notes: Enrollment in Topics in Modern Statistics (STAT GR5293) and any track-specific electives offered by other programs that are not listed in Table 4 requires approval from the student’s faculty advisor.

Table 4: Advanced Statistical Practice Approved Track-Specific Electives