Ph.D. in Data Analytics & Data Science (International Program)

Program Milestones and Requirements

NIDA’s Ph.D. in Data Analytics offers two study plans:

• Plan 1 (Research-Based):
  Students begin research immediately upon advisor approval of their proposal, subject to further endorsement by the Ph.D. Program Committee. Additional non-credit coursework from the master’s curriculum may be required at the advisor’s discretion to support research readiness.
• Plan 2 (Coursework + Research):
  Students without a background in Statistics, Actuarial Science, or related fields must complete foundational courses from the M.S. in Applied Statistics program, as determined by the Ph.D. Committee. If necessary, students may also be required to take extra credit-bearing courses beyond the standard curriculum with approval from the dean or advisor.

The program is designed to be completed within 6 to 10 semesters.

Coursework for Plan 2

Students enrolled under Plan 2 must complete the following coursework:

• Core Courses: 3 credits
• Major (Specialized) Courses: 12 credits
• Elective Courses: Minimum of 9 credits

A full list of course offerings is provided in the curriculum documentation

1. Academic Background:
   Applicants must hold a degree in Mathematics, Statistics, Engineering, Business, Economics, IT, or related fields from an institution accredited by the Thai Commission on Higher Education. Strong academic performance and English proficiency are required.
2. Degree Accreditation (for International Degrees):
• Thai applicants must submit a degree accreditation letter before the interview.
• International applicants may submit the letter after enrollment.
3. English Proficiency:
   Applicants must meet one of the following minimum test scores:
• TOEFL: 550 (PBT) or 79 (iBT)
• TOEFL/ITP: 550
• IELTS: 6.5
  Exemptions apply for native speakers or graduates (within the last 5 years) from English-instructed international institutions.
4. Conditional English Admission:
   Applicants with lower scores (TOEFL 450–549 or IELTS 5.5–6.4) may be conditionally admitted. They must take remedial English courses and achieve a TOEFL/ITP score of at least 550 before graduation. Only NIDA-administered TOEFL/ITP is accepted for admission.

APPLY HERE –> link to registration website ? : https://entrance.nida.ac.th/registrar/applogin.asp

Admission Timeline

We offer admissions twice a year:

• August Intake
  🗓️ Application Period: March – May
  📢 Results Announced: June

• January Intake
  🗓️ Application Period: September – October
  📢 Results Announced: Late November – Early December

Shortlisted applicants will be invited for an interview with the admissions committee.

The total cost over a 5-year period is approximately:

• ฿400,000 for Thai nationals

• Up to ฿500,000 for international students

🎓 Scholarships and grants are available.
For more details, please visit:
👉 General Scholarship Information

🌱 Green and Sustainable Computing

Green and Sustainable Computing explores how data science and analytics can be leveraged to reduce the environmental footprint of digital technologies. This theme focuses on developing energy-efficient algorithms, optimizing resource consumption in data centers, and implementing carbon-aware computing strategies. With the rise of generative AI and large-scale data processing, it becomes increasingly important to align computational advancements with sustainability goals. Students in this theme will engage with topics such as green AI, low-power machine learning, eco-centric cloud architectures, data governance, and lifecycle assessments of digital systems.

This theme encourages interdisciplinary research that integrates environmental science, systems engineering, and data optimization. PhD students can work on projects that model and predict the energy consumption of complex IT infrastructures, design carbon-offset mechanisms for data-intensive operations, or assess the sustainability impacts of digital transformation in various sectors. Close collaborations with environmental agencies, energy companies, and green-tech startups offer opportunities to validate research in real-world contexts.

Graduates from this theme will be well-equipped to lead sustainability-focused data initiatives in government, academia, and industry. Whether contributing to ESG (Environmental, Social, and Governance) reporting standards, building carbon-aware edge computing solutions, or advising policy on sustainable digital transformation, their skills will be in high demand as global pressure mounts to align technological progress with climate commitments.

🎮 Gaming and IoT Applications

The Gaming and IoT Applications theme addresses the convergence of immersive digital environments and sensor-rich ecosystems through data analytics and machine learning. The explosion of IoT devices and the growth of online gaming platforms provide vast, dynamic datasets ripe for intelligent analysis. This theme invites students to explore how data from wearables, smart homes, and interactive gaming systems can be harnessed for personalized experiences, behavior modeling, and real-time optimization.

Projects under this theme span various applications—from developing AI-powered game design tools and adaptive learning environments to implementing predictive maintenance in IoT networks. Students may investigate how physiological signals from gamers or users of IoT devices can be used to create emotionally adaptive systems. Real-time analytics, edge intelligence, and secure data transmission protocols also fall under this domain, especially in scenarios requiring low-latency decision-making.

Graduates will be positioned to work at the intersection of entertainment, smart technologies, and applied AI. Whether designing intelligent recommender systems for games, creating human-in-the-loop interfaces for IoT systems, or contributing to the development of smart cities through behavioral data, PhD holders from this theme will shape how the next generation of connected experiences is built and understood.

📊 Statistical Financial Tools

The Statistical Financial Tools theme centers on using advanced statistical methods and machine learning models to understand, predict, and optimize financial behavior and systems. As global markets become more volatile and complex, robust data analytics has become essential for risk modeling, portfolio optimization, and regulatory compliance. This theme prepares students to design transparent, explainable, and statistically rigorous tools for decision-making in finance.

Research in this area can range from developing high-frequency trading algorithms and fraud detection systems to evaluating ESG investment metrics and stress-testing banking portfolios. Students will dive deep into time series analysis, copula models, Monte Carlo simulations, and Bayesian inference—all with a practical emphasis on financial applications. Collaboration with banks, fintech startups, and regulatory bodies will ensure the relevance and impact of their work.

Graduates from this theme will be prepared for careers in quantitative research, financial regulation, or fintech innovation. They will be equipped to address challenges in pricing derivatives, modeling credit risk, or navigating the statistical uncertainties of decentralized finance (DeFi). Their training will allow them to bring clarity, confidence, and computational rigor to high-stakes financial environments.