Khon Kaen University - SDG Alignment for Master of Science Program in Statistics and Data Science

SDG Alignment for Master of Science Program in Statistics and Data Science

Overall SDG Alignment Evaluation:

The updated Master of Science Program in Statistics and Data Science (Revised 2026) at Khon Kaen University is a dynamic, interdisciplinary program that harnesses the power of data to address complex global challenges. The curriculum provides a robust foundation for SDG 3 (Good Health and Well-being) and SDG 13 (Climate Action) by equipping students with the analytical and simulation skills necessary to model epidemiological trends, evaluate health interventions, and forecast climate patterns. At its core, the program acts as a catalyst for SDG 9 (Industry, Innovation and Infrastructure) through its advanced coursework in machine learning, deep learning, and natural language processing. By nurturing high-level analytical talent, it significantly contributes to SDG 8 (Decent Work and Economic Growth). Furthermore, the program is deeply rooted in SDG 4 (Quality Education) through rigorous theoretical frameworks and promotes SDG 17 (Partnerships for the Goals) by fostering collaborative statistical consulting across various scientific domains.

Best Practices

Master of Science Program in Statistics and Data Science of Khon Kaen University (KKU) has driven the role of excellence in Data Science to elevate the economic and industrial capacity of the region, recognizing the challenge faced by Small and Medium-sized Enterprises (SMEs) lacking decision-making mechanisms based on advanced statistical data.
The curriculum thus organized the integrated academic service activity, "Statistical Analysis for Quality Control in Industry," by incorporating intensive knowledge from core courses: SC 637 802 Data Preparation for Processing and Data Mining, SC 637 804 Statistical Quality Control in Industry, and SC 637 805 Big Data Analytics. This enabled doctoral students to work with SME entrepreneurs and community enterprises in the food and agro-processing sectors in the Northeast to apply statistical models and control charts to conduct in-depth analysis of production data and develop an actionable quality control system.
The evident result is that those SMEs successfully implemented the statistical quality control mechanism, significantly achieving a reduction in defective products and a decrease in production downtime in the production process. This led to the widespread sustainable impact of creating a Data-Driven Culture in the local industrial sector, elevating product quality standards for consumer safety, and promoting the most efficient use of resources.
This success underscores the strategic alignment with the Sustainable Development Goals: SDG 11: Sustainable Cities and Communities through developing a comprehensive and effective urban waste management system; SDG 12: Responsible Consumption and Production by promoting waste reduction and the reuse of waste in the value chain; and SDG 17: Partnerships for the Goals, which is the core of the collaboration between the academic institution, the Public Sector (Local Administrative Organizations), and the community to achieve shared sustainable environmental restoration goals.

SDG Related Course(s)

SDG 3: Good Health and Well-being

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Alignment Summary: The program equips students with the advanced statistical modeling and data analysis capabilities required to support medical research, epidemiology, and public health initiatives. Graduates are empowered to analyze complex health data to improve patient outcomes and healthcare policies.

Course Code	Course Title	Alignment Rationale
SC 677 704	Advanced Data Analysis	Provides the sophisticated analytical techniques required for biostatistics and health informatics, directly supporting research that reduces mortality and combats communicable diseases (Target 3.3, 3.4).
SC 677 705	Statistical Consulting	Empowers students to assist medical professionals and public health researchers in designing robust experiments and analyzing clinical trial data to ensure evidence-based health interventions (Target 3.b).

SDG 4: Quality Education

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Alignment Summary: The program provides rigorous, advanced education in statistical theory and data science. It fosters high-level technical skills, critical thinking, and lifelong learning through specialized coursework, intensive seminars, and thesis research.

Course Code	Course Title	Alignment Rationale
SC 677 701	Probability Theory	Provides the essential foundational mathematical knowledge required for advanced statistical reasoning, directly supporting high-level technical skills (Target 4.4).
SC 677 891	Seminar in Statistics and Data Science	Promotes critical thinking and lifelong learning by having students analyze, present, and discuss cutting-edge research in the rapidly evolving field of data science (Target 4.7).
SC 677 898 / 899	Thesis	Fosters advanced research training, leading to the creation of new knowledge and equipping learners with highly specialized skills for sustainable technological development (Target 4.3).

SDG 8: Decent Work and Economic Growth

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Alignment Summary: Data science is a critical engine for the modern economy. By training students in advanced data analytics and specialized topics, the program prepares graduates for highly skilled, decent jobs that directly enhance economic productivity and innovation.

Course Code	Course Title	Alignment Rationale
SC 677 808	Special Topics in Statistics and Data Science	Ensures students are trained in the latest industry-relevant technical skills, promoting youth employment and decent jobs in the global digital economy (Target 8.6).

SDG 9: Industry, Innovation and Infrastructure

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Alignment Summary: The curriculum is heavily invested in the technological tools of the future. Courses covering machine learning, deep learning, NLP, and simulation provide the core competencies needed to build intelligent infrastructure and drive industrial innovation.

Course Code	Course Title	Alignment Rationale
SC 677 703	Data Preparation and Machine Learning Algorithms	Enhances scientific research and innovation capacity by teaching core AI algorithms used to upgrade and automate industrial technologies (Target 9.5).
SC 677 805	Deep Learning for Data Analysis	Drives technological innovation by equipping students with advanced neural network capabilities, which are essential for developing modern intelligent infrastructure (Target 9.b).
SC 677 806	Text and Natural Language Processing	Contributes to technological upgrading by enabling the automated analysis of vast amounts of unstructured text data for commercial and industrial applications (Target 9.5).
SC 677 804	Operations Research	Focuses on mathematical optimization of complex systems, logistics, and processes, directly supporting sustainable and highly efficient industrialization (Target 9.4).

SDG 13: Climate Action

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Alignment Summary: Data science plays a critical role in modeling and mitigating the impacts of climate change. The program's focus on complex modeling and stochastic simulation prepares students to analyze environmental datasets, forecast extreme weather events, and optimize sustainable resource management.

Course Code	Course Title	Alignment Rationale
SC 677 802	Modeling and Stochastic Simulation	Provides the computational tools necessary to simulate environmental changes, assess climate risks, and build predictive models for climate change mitigation and adaptation strategies (Target 13.1, 13.2).
SC 677 898 / 899	Thesis	Offers students the opportunity to conduct intensive research on environmental and climate-related datasets, contributing actionable insights for climate action planning (Target 13.b).

SDG 17: Partnerships for the Goals

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Alignment Summary: The program emphasizes the collaborative nature of data science. It trains students to act as analytical partners across disciplines and ensures their original research contributes to the global scientific community.

Course Code	Course Title	Alignment Rationale
SC 677 705	Statistical Consulting	Fosters multi-stakeholder partnerships by training students to collaborate actively with researchers and professionals across various disciplines to solve real-world problems using robust data methodologies (Target 17.16).