Program structure
Program content
You will learn multidisciplinary skills to solve high-value industrial problems and utilize essential techniques of data science and AI-driven models to make predictions that support business decision-making.
You will become a graduate with intercultural communications, analytical skills and creative ideas which are required as important skills for the 21st century and be ready for any change, transformation and adaptation.
Students who successfully complete the BSc Industrial Mathematics and Data Science will be able to:
- Solve industrial and business management problems logically and systematically by means of appropriate optimization techniques.
- Make a fact-based mathematical model of trend prediction in industrial and business management to support making data-driven decision with the respect of data privacy, ethics, and protection.
- Conduct an independent project and/or work in the field of industrial mathematics and data science with professional code of conduct.
- Communicate concepts in the field of industrial mathematics and data science clearly and purposefully with respect to the target audience, in English, in both written and oral formats.
- Work with others to achieve team goals based on the roles and responsibilities of an industrial mathematician or a data scientist.
- Develop their academic potential in Industrial Mathematics and Data Science to make themselves competent (a combination of knowledge, skills, and attitudes) and responsible global citizens capable of adapting to changing situations.
Program structure
This section shows the program structure for students who choose to spend the entire 3.5 years at Mahidol University. Details on obtaining the dual degree can be found on the Curtin University webpage.
Students require at least 120 credits to graduate with a BSc Industrial Mathematics & Data Science degree from Mahidol University. The credit structure is as follows:
Students need to complete 24 credits of general education courses.
| Category | Minimum credit requirements |
| Social Sciences and Humanities | 2 |
| Languages | 2 |
| Science and Mathematics | 2 |
Students need to complete 90 credits of specific courses. Minimum credits for each of the following categories are also required.
| Category | Minimum credit requirements |
| Core courses | 51 |
| Major elective courses | 39 |
Students need to complete 6 credits of free elective courses.
Recommended study plan
Students can complete the BSc Industrial Mathematics and Data Science program within a regular period of 3.5 years. Each academic year consists of two regular semesters (16 weeks). Below is the list of core courses in the recommended study plan:
| Code | Course title | Credits |
| SCIM 103 | Mathematics I | 4 |
| SCIM 104 | Mathematics II | 4 |
| SCIM 105 | Fundamentals of Scientific Computing | 3 |
| SCIM 106 | Discrete Mathematics | 3 |
| SCIM 121 | Statistical Data Analysis I | 3 |
| SCIM 122 | Statistical Data Analysis II | 3 |
| Code | Course title | Credits |
| SCIM 204 | Operations Research | 3 |
| SCIM 210 | Professional Skills for Industrial Mathematics and Data Science I | 2 |
| SCIM 211 | Simulation Modelling | 3 |
| SCIM 212 | Mathematical Computing | 3 |
| SCIM 221 | Statistical Data Analysis II | 3 |
| SCIM 222 | Linear Algebra | 3 |
| SCIM 250 | Introduction to Data Science | 3 |
Code | Course title | Credits |
SCIM 310 | Professional Skills for Industrial Mathematics and Data Science I | 2 |
SCIM 381 | Supply Chain Modelling and Optimization | 3 |
SCIM 391 | Data Structure in Mathematics | 3 |
Code | Course title | Credits |
SCIM 497 OR SCIM 498 | Industrial Project OR Internship for Experience | 3 |
Major elective courses
For major elective courses, students can choose to study courses in the following list.
SCIM 201 Ordinary Differential Equations and Mathematical Transforms
SCIM 203 Partial Differential Equations for Engineers and Scientists
SCIM 205 Mathematics for Finance and Economics
SCIM 209 Probabilistic Models in Operations Research
SCIM 223 Calculus of Several Variables
SCIM 252 Database Management
SCIM 254 Data Communications
SCIM 302 Stochastic Processes and Applications in Industry
SCIM 303 Seminar
SCIM 304 Network Optimization
SCIM 305 Logistics Modelling and Optimization
SCIM 306 Game Theory
SCIM 307 Control Theory and Optimization
SCIM 309 Mathematical Statistics
SCIM 311 Statistical Modelling
SCIM 321 Computer Applications in Statistics
SCIM 322 Mathematics for Artificial Intelligence
SCIM 323 Data Mining
SCIM 324 Design and Analysis of Algorithms
SCIM 325 Interactive, Virtual & Immersive Environments
SCIM 326 Machine Learning
SCIM 327 Object-Oriented Programming
SCIM 328 Web Programming
SCIM 329 Mobile Application Programming
SCIM 371 Computational Mathematics
SCIM 372 Analytics for Observational Data
SCIM 373 Data Visualization and Interpretation
SCIM 402 Industrial Modelling and Optimization
SCIM 403 Numerical Optimization
SCIM 404 Applied Mathematical Modelling in Industrial Processes
SCIM 405 Dynamic and Stochastic Modelling and Optimization
SCIM 406 Production Planning and Management
SCIM 431 Big Data Analytics
SCIM 432 Deep Learning
SCIM 441 Heuristic Methods for Optimization
SCIM 471 Advanced Numerical Analysis
SCIM 472 Mobile Cloud Computing
SCIM 473 Advanced Optimization Techniques
Key information
- Duration: 3.5 years
- Location: Phyathai Campus
- Next intake: August
