Statistics MS Program

The STAT MS program has two tracks:

  • Statistical Science
  • Data Science 

Program Mission 
The program mission of the MS program in Statistics is to equip the  graduates with advanced theoretical knowledge and practical skills in statistics and data science, engage in research activities and contribute to the broader community.

Program Goals
Equip candidates with cutting-edge statistical theory and methods.
Foster research abilities.
Develop analytical ability and skills for data analysis and data-driven decision-making across various fields.
Contribute to the broader national and international community through proactive engagement

Program Learning Outcomes (PLOs)

Knowledge and Understanding:  
K1: Graduates of the MS program in Statistics will gain a deep understanding of probability theory, statistical inference, and mathematical statistics, which is the basis for advanced statistical methods.
K2: Graduates in the MS program in Statistics will be introduced to advanced statistical techniques, including generalized regression models, stochastic processes, Bayesian methods, along with computational and machine learning approaches.
K3: Graduates in the MS program of Statistics will develop advanced knowledge in statistical modeling, analysis, and interpretation of real-world data.

Skills:  
S1: Graduates in the MS program of Statistics will utilize advanced tools and statistical methods and quantitative models for decision-making in business and other industries, required to analyze complex, real-world statistical problems
S2: Graduates in the MS program of Statistics will be able to communicate their data-driven findings clearly and concisely, both in written reports and oral presentations, in the academic and professional setting.
S3: Graduates in the MS program of Statistics will develop skills in specialized areas such as biostatistics, data science, machine learning, through optional courses and projects.
S4: Graduates in the MS program of Statistics will gain experience in using statistical software like R, which enables the implementation of complex analyses and computational methods.

Values, Autonomy, and Responsibility:  
V1: Graduates in the MS program of Statistics will be able to demonstrate adherence to ethical code of conduct, professional standards, and values within statistics and related fields.
V2: Graduates in the MS program of Statistics will be able to engage in self-directed learning and development, performance monitoring, and continuous assessment of one's own learning.
V3: Graduates in the MS program of Statistics is committed to actively participating in the development of the field of statistics, contributing to its advancement, and fostering innovation.

MS Course Requirements

MS students must complete the following requirements:

  • Core Courses (12 credits)
  • Elective Courses (12 credits)
  • Research/Capstone (12 credits)
  • Graduate Seminar (non-credit)
  • Winter Enrichment Program (non-credit)

Core and Elective Courses must be technical courses and cannot be substituted with Research or Internship to fulfill degree requirements.

Core Courses (12 credits)

Core Courses provide students with the background needed to establish a solid foundation in the program area. Students must complete 12 credits (4 Core Courses) and be aware that Core Courses may be offered only once per academic year.
STAT 220Probability and Statistics

3

STAT 230Linear Models

3

STAT 240Bayesian Statistics

3

AMCS 241Stochastic Processes

3

 

Elective Courses (12 credits)

Elective Courses allow students to tailor their educational experience to meet individual research and educational objectives with the permission of the Academic Advisor. STAT students should note the following:

  • STAT 210 can only be taken on a Satisfactory/Unsatisfactory basis and is not counted toward the degree requirements.
  • Only one of AMCS201, AMCS202, and AMCS206 can be used towards the degree requirements

Students on the Data Science Track are required to complete the following additional requirements:

  • CS 229 Machine Learning
  • At least 6 credits of other Elective Courses from the CS 200-level

Elective Courses for all tracks are as follows:

AMCS 206Applied Numerical Methods

3

AMCS 211Numerical Optimization

3

AMCS 215Mathematical Foundations of Machine Learning

3

CS 207Programming Methodology and Abstractions

3

CS 220Data Analytics

3

CS 229Machine Learning

3

CS 245Databases

3

CS 247Scientific Visualization

3

CS 248Computer Graphics

3

CS 249Algorithms in Bioinformatics

3

CS 260Design and Analysis of Algorithms

3

ECE 242Digital Communication and Coding

3

ECE 251Digital Signal Processing and Analysis

3

ErSE 213Inverse Problems

3

ErSE 222Machine Learning in Geoscience

3

ErSE 253Data Analysis in Geosciences

3

Others upon approval of the Academic Advisor.

Graduate Seminar (non-credit)

Students must register for STAT 398 and receive a Satisfactory grade for two Semesters during their MS.

Winter Enrichment Program (non-credit)

All students must complete the Winter Enrichment Program (WE 100) for credit at least once during their studies at KAUST. Students who have previously completed WEP will be exempt from this requirement in their future studies. 

MS Thesis

Students planning to pursue the Thesis option must complete a minimum of 12 credits of Thesis Research (STAT 297).

Thesis Application

Students must complete the application and have it approved by the Program Chair no later than the end of week one of their third Semester. The Thesis Advisor must be a full-time program-affiliated Assistant, Associate, or Full Professor at KAUST. The Thesis Advisor can only become project affiliated for the specific thesis project with the Program Chair’s approval. The application must include a Thesis Proposal endorsed by the Thesis Advisor and a timeline for completion.

MS Thesis students who meet the graduation requirements of the non-thesis track may drop the thesis up until the end of their third Semester. Students not able to complete their thesis after this deadline will face academic dismissal.

Thesis Committee Formation

 The MS thesis defense committee must include a minimum of three members and may have up to four members. The composition of the committee is outlined as follows: 

Member Role  Affiliation
1 Faculty
Primary affiliation within of the student’s program
2 Faculty Primary affiliation within of the student’s program
3 Faculty  Primary affiliation outside of the student’s program
4 Faculty or Research Scientist  Affiliation within or outside of KAUST

Notes:

  • The committee must be approved by the Dean. 
  • Members 1-3 are mandatory, while member 4 is optional. 
  • The student’s advisor serves as the chair of the committee. If the advisor holds a primary affiliation within the student’s program, then they act as member 1. If the advisor has a secondary or one-time affiliation within the student’s program, then they act as member 3.
  • The student’s co-advisor may serve as member 4. 
  • Professors of Practice and Research Professors may serve as members 1-4, depending on their affiliation and whether they are the student’s advisor or co-advisor. 
  • Adjunct Professors and Professors Emeriti may continue serving on committees in the roles they had at the time of their departure but are not permitted to serve on new committees.
  • Visiting Professors may only serve as member 4. 
  • Once approved, any changes to the committee require the approval of both the student's advisor and the Dean.

Petition to Defend Thesis

Students must submit a petition to defend their Thesis by the deadline published in the Academic Calendar. Students are responsible for scheduling the Thesis Defense Date. All committee members must attend the Defense. Students must defend their thesis and obtain the final approval of the Defense within their duration of study (4 Semesters).

Thesis Defense Results

 The format of the Oral Defense is left to the discretion of the Thesis Committee. At the end of the Final Defense, students will be evaluated with one of the following outcomes:

  • Pass: The Committee agrees with no more than one dissenting vote. The Thesis must be archived within two weeks of the defense, and the student must send the Thesis Result Form to the GPSA within two days of the defense.
  • Pass with Conditions: All committee members must agree on the required conditions. If they cannot agree, the Dean will make the final decision. The student has up to three months to meet these conditions, unless the Committee unanimously agrees to change the deadline.
  • Fail with Retake: If conditions cannot be met within three months, or more than one member casts a negative vote, one retake of the defense is allowed. The retake must occur within six months of the original defense unless the Committee unanimously agrees to a shorter timeline. If the student fails the retake, they will be dismissed from the University. The Committee Chair must immediately inform the GPSA to initiate the necessary actions.
  • Fail Without Retake: The decision must be unanimous, resulting in the student’s dismissal from the University. The Committee Chair must inform the GPSA immediately to take the necessary actions. 

Additional Guidelines:

  • Students who have exceeded their duration of study must apply for an extension as per the Time Limit and Extension Policy. All conditions must be fulfilled by the end of the extension period, which takes precedence over the Committee’s set deadlines. 
  • The outcome of the Thesis Defense must be recorded by submitting the Thesis Defense Evaluation Form to the Office of the Registrar within two days of the defense.
  • Additionally, students must submit the Defense Results Form by the deadline published in the Academic Calendar.
  • The required forms are available on the webpage of the Office of the Registrar.  

Thesis Document

Students must follow the Thesis and Dissertation Guidelines available from the KAUST Library when they write their Thesis. Once the Thesis is ready to be examined, students must determine the Defense date with the agreement of all members of the Thesis Committee.

Thesis Archiving

Students must archive the Thesis in the KAUST Library two weeks from the final result form. This must not exceed the duration of study or the deadline published in the Academic Calendar.

MS Non-Thesis

Students wishing to pursue the non-thesis option must complete a total of 12 capstone credits, with 6 credits of directed research (STAT 299). Students must complete the 6 remaining credits through one of the options listed below:

  • Internship: Summer internship (STAT 295) – students can only take one internship
  • Any 200/300-level courses from any degree program at KAUST