Curriculum
M.Sc. in Statistics: Data Science
With an increasing demand on data analysts and data scientists, M.Sc. in Statistics has modified its original program into a program with a right combination of theory and practical component. The M.Sc. in Statistics is offered through both full-time and part-time programs.
Full-time Program (Plan A – Thesis Option)
Class schedule: Monday – Friday, 9AM – 4PM*
Tuition fee: 24,500 Baht per semester (2 semesters per 1 academic year)
Program Code: 3134
*Class schedule is subject to change due to the agreement of both instructor and students.
Part-time Program (Plan B - Special Project Option)
Class schedule: Monday – Friday, 6PM – 9PM and Saturday, 9AM – 4PM*
Tuition fee: 69,500 Baht per semester (2 semesters per 1 academic year)
*Class schedule is subject to change due to the agreement of both instructor and students.
| Full-time (Plan A) |
Part-time (Plan B) |
|
| Total Credits | 36 | 36 |
| Coursework | ||
| - Core | 15 | 15 |
| - Elective (Group 1) | 3 | 3 |
| - Elective (Group 2) | 6 | 6 |
| - Elective (Any) | - | 9 |
| Thesis | 12 | - |
| Special Project | - | 3 |
| Comprehensive Exam | No | Yes |
| Work experience | Not required | 1 year minimum |
Coursework
Core Courses: Foundation (15 Credits)
| Course | Number Course | Title Credit |
| 2603602 | Theory of Probability | 3 |
| 2603603 | Statistical Inference | 3 |
| 2603633 | Data Management for Statistical Application | 3 |
| 2603645 | Applied Statistical Model | 3 |
| 2603646 | Data Visualization | 3 |
Elective Courses (Group 1): Data Acquisition
| Course | Number Course | Title Credit |
| 2603604 | Theory of Sample Surveys | 3 |
| 2603607 | Advanced Experimental Designs | 3 |
| 2603642 | Statistical Computation and Simulation | 3 |
| 2603647 | Fundamental Programming for Data Science | 3 |
| 2603572 | Big Data Computing | 3 |
Elective Courses (Group 2): Statistical Analysis and Data Science
| Course | Number Course | Title Credit |
| 2603520 | Deep Learning | 3 |
| 2603521 | Data Science for Business Analytics | 3 |
| 2603522 | Recommender Systems | 3 |
| 2603536 | Theory of Statistical Learning | 3 |
| 2603537 | Reinforcement Learning | 3 |
| 2603538 | Machine Learning in Risk Analytics | 3 |
| 2603539 | Data Science Approach to Portfolio Optimization | 3 |
| 2603605 | Theory of Linear Models | 3 |
| 2603606 | Multivariate Analysis | 3 |
| 2603616 | Nonparametric Statistics | 3 |
| 2603630 | Time Series Analysis | 3 |
| 2603637 | Machine Learning | 3 |
| 2603638 | Stochastic Processes | 3 |
| 2603640 | Decision Analysis | 3 |
| 2603641 | Statistical Analysis for Categorical Data | 3 |
| 2603648 | Bayesian Analysis | 3 |
| 2603675 | Survival Models | 3 |
| 2603693 | Advanced Data Science Practicum | 3 |
| 2603695 | Special Topics in Statistics | 3 |
Plan of Study
| Full-time (Plan A) |
Part-time (Plan B) |
||||
| Fall Semester, Year 1 | |||||
| 2603602 | Theory of Probability | 3 | 2603602 | Theory of Probability | 3 |
| 2603646 | Data Visualization | 3 | 2603646 | Data Visualization | 3 |
| 2603633 | Data Management for Statistical Application | 3 | 2603633 | Data Management for Statistical Application | 3 |
| Spring Semester, Year 1 | |||||
| 2603603 | Statistical Inference | 3 | 2603603 | Statistical Inference | 3 |
| 2603645 | Applied Statistical Model | 3 | 2603645 | Applied Statistical Model | 3 |
| Elective course (Group 1) | 3 | Elective course (Group 1) | 3 | ||
| Fall Semester, Year 2 | |||||
| Elective course (Group 2) | 3 | Elective course (Group 2) | 3 | ||
| Elective course (Group 2) | 3 | Elective course (Group 2) | 3 | ||
| Elective course (Any) | 3 | ||||
| Spring Semester, Year 2 | |||||
| 2603811 | Thesis | 12 | 2603698 | Master Project | 3 |
| Elective course (Any) | 3 | ||||
| Elective course (Any) | 3 | ||||
| 2603896 | Comprehensive Exam | 3 | |||
Course Description
Course Description (Click Here)