Computer Science

This course is designed for students wishing to clarify and advance their career goals through a 16-week internship in Thailand. It provides a structured learning environment to help students make the most of their internship experience. While there are no regularly scheduled class meetings, internships are conducted under the close academic supervision of the Social Policy & Development department at Thammasat University. An assigned internship coordinator provides oversight and guidance for the duration of the internship. The course requires a minimum of 128 total work hours. To facilitate the completion of the internship and maximize skill acquisition, students are required to work at least one full day per week at their internships. Graded Pass/No pass only.

This course introduces python programming, software engineering and design techniques to engineering students. It also introduces the use of libraries like SciPy, NumPy and Matplotlib for solving equations (using appropriate numerical methods) and the use of JuPyter Lab notebooks to present calculations.

This course provides a study of artificial intelligence and the application of AI tools and techniques to solve real-world problems. Topics include: knowledge representation techniques; types of searches (blind, heuristic); reasoning under uncertainty (Bayesian inference, Bayesian Networks, Markov Models, fuzzy logic); applied artificial intelligence.

The course examines the process of creating, assessing, and pursuing product opportunities, with emphasis on evaluating future markets shaped by new technologies. The focus is on technology innovation coupled with business planning and development. Other topics include factors that drive continuous creative product innovation, evaluation of new product ideas and risk assessment of commercialization, product development strategies in industrial marketing, understanding the behavior of the buyer and formulation and implementation of an innovative marketing strategy and business plan. This course combines mentor-guided team projects, case studies, and research on the entrepreneurial process. Text: Osterwalder and Pigneur, BUSINESS MODEL GENERATION: A HANDBOOK FOR VISIONARIES, GAME CHANGERS, AND CHALLENGERS. Assessment: exam I (25%), exam II (25%), assignments (10%), class activities (5%), class attendance and participation (5%), term project and presentation (30%).

This course covers the basic technical aspects of dynamic website construction concerning the front end (client) and the back end (server). It develops a springboard for web-related technologies that require data collection, organization and maintenance of accessible websites, through semantic layouts and with an introduction to object orientated coding practices. It focuses on developing skills in using HTML, CSS, jQuery, with PHP and SQL, and covers aspects of data flow and web data management.

The course offers an introduction to programming-based problem solving for social scientists. The course introduces a number of problems and solutions in social science data processing with applications in R. The course begins with a number of general programming topics, followed by efficient processing of different data structures and how data can be combined using SQL and Tidyverse. Secondly, the course looks at special challenges related to space and time. The spatial dimension introduces GIS techniques. Towards the end, the course sees how machine text analysis can be used to automate data collection and to look at how we can effectively visualize different types of data. The course provides a good basis for independent work with social science information. The course requires students to have completed a course in research methods and statistics as a prerequisite.

Operations Research is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc. This course focuses on deterministic optimization techniques, a major part of the field of Operations Research. The course is divided into four modules: (1) Linear programming, (2) Integer programming, (3) Nonlinear programming, and (4) Dynamic programming.

This course introduces the fundamental principles, methodologies and practices of modern software engineering. It covers the software process, and development activities including requirements engineering, software design, testing, deployment and evolution.