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.
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 206 | Applied Numerical Methods | 3 |
| AMCS 211 | Numerical Optimization | 3 |
| AMCS 215 | Mathematical Foundations of Machine Learning | 3 |
| CS 207 | Programming Methodology and Abstractions | 3 |
| CS 220 | Data Analytics | 3 |
| CS 229 | Machine Learning | 3 |
| CS 245 | Databases | 3 |
| CS 247 | Scientific Visualization | 3 |
| CS 248 | Computer Graphics | 3 |
| CS 249 | Algorithms in Bioinformatics | 3 |
| CS 260 | Design and Analysis of Algorithms | 3 |
| ECE 242 | Digital Communication and Coding | 3 |
| ECE 251 | Digital Signal Processing and Analysis | 3 |
| ErSE 213 | Inverse Problems | 3 |
| ErSE 222 | Machine Learning in Geoscience | 3 |
| ErSE 253 | Data 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).
For more details on the Thesis Application, Thesis Committee Formation, Thesis Defense Results, Thesis Document and Thesis Archiving please check the policy page
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