Data Science & AI
A Joint Collaboration between Asian Institute of Technology (AIT) & United Nations Institute for Training and Research (UNITAR)
1-YEAR PROGRAM
FOR FAST-TRACK CAREER GROWTH
Gain advanced knowledge & skills in Data Science and AI
Blend of theory & practical applications to solve global challenges
Hybrid learning: In-person classes at AIT, Thailand, and/or online sessions
Join a diverse, global community of AI & data professionals
An internationally accredited Professional Master’s degree from AIT, in collaboration with UNITAR.
Designed to equip students with advanced knowledge and skills in data science and AI applications.
Offers a unique blend of theoretical foundations and practical application, tackle global challenges using data solutions.
Adopts a hybrid mode of instruction, combining physical class sessions at the AIT campus in Thailand with online sessions.
COURSE LIST & DESC RIPTIONS
Computer Programming for Data Science: Tutorial focused on developing practical programming skills and best practices in Data Science.
Data Modeling and Data Management: Emerging data models and technologies for managing diverse data types and characteristics.
Fundamentals of Machine Learning: Machine Learning fundamentals for R&D, tailored for diverse science and engineering backgrounds.
Data Analytics for Business Intelligence: Understanding the principles and practices of business intelligence and data analytics to support organizational decision-making.
Data Privacy, Security and Governance: Comprehensive overview of data privacy, security, and governance principles and their practical application.
AI Regulation and Governance: Understanding how to effectively regulate and govern AI technologies, and the future challenges of AI regulation.
Techplomacy, Ethics and Conflicts in Artificial Intelligence: Ethical challenges posed by AI, emphasizing transparency, accountability, and trust in AI systems.
Strategic Cybersecurity Governance : Global cybersecurity management and governance, threats, laws, and incident response strategies.
COURSE ENROLLMENT
Semester 1
AT85.01: Computer Programming for Data Science
Objective:
The course objective is to provide students hands-on programming skills and best practices related to Data Science. It is a tutorial course in which students will develop programming skills in loading, cleansing, transforming, modeling, and visualizing data.
Learning Outcomes:
Students, on successful completion of the course, will be able to
- Prepare data for further analysis using data analytic tools
- Manipulate data sets programmatically
- Perform exploratory data analysis programmatically
- Apply basic text processing techniques to unstructured data sets
- Visualize data sets effectively
- Perform basic statistical analyses programmatically
- Build data-driven predictive models
AT85.02: Data Modeling and Data Management
Objective:
The course emphasizes on emerging data models and technologies suitable for managing different types and characteristics of data. Professionals will develop skills in analyzing, evaluating, modeling and developing database applications with concerns on both technical and business requirements.
Learning Outcomes:
Students, on successful completion of the course, will be able to
- Explain data modeling and management concepts.
- Design and organize various types of data using a relational and non-relational data models.
- Analyze the characteristics and requirements of data and select an appropriate data model.
- Identify, implement and perform frequent data operations (CRUD: create, read, update and delete) on relational and NoSQL databases.
- Describe the concepts and the importance of big data, data security, privacy and governance.
- Describe the concepts and the importance of data engineering and data visualization.
AT85.03: Fundamentals of Machine Learning
Objective:
The course introduces professionals from a variety of science and engineering backgrounds to the fundamentals of machine learning and prepares them to perform R&D involving machine learning techniques and applications. Students learn to design, implement, and evaluate intelligent systems incorporating models learned from data.
Learning Outcomes:
Students, on successful completion of the course, will be able to:
- Formulate a practical data analysis and prediction problem as a machine learning problem.
- Plan for data acquisition considering the characteristics of the data set required for a particular machine learning problem.
- Train and test supervised regression and classification models, unsupervised learning and density estimation models, and reinforcement learning models.
- Integrate a trained machine learning model into an online software system.
AT85.04: Business Intelligence and Analytics for Professionals
Objective:
Business intelligence (BI) is the process of analyzing business data to obtain business insights and actionable intelligence and knowledge, in order to support better business decision making and capture new business opportunities. This course will give professionals an understanding of the principles and practices of business intelligence and data analytics to support organizations in conducting their business in a competitive environment.
Learning Outcomes:
Students, on successful completion of the course, will be able to:
- Explain the concepts characteristics of BI and data analytics
- Describe multiple business problem/decision making domains requiring BI and data analytics
- Apply BI and data analytic tools and technologies to develop BI applications
- Integrate BI applications with other information systems as part of a business process
- Define a Bl strategy for an organization
- Manage a BI project for an organization
- Describe big data analytics and applications
Semester 2
AT85.9001: Data Privacy, Security and Governance
Objective:
This course will introduce the principles of data governance, the process of managing and controlling an organization’s data assets. It encompasses the policies, procedures, standards, and guidelines for managing and ensuring the quality, accuracy, and security of data. Students will explore in details important data governance frameworks existing to date such as DAMA-DMBOK (Data Management Body of Knowledge) and COBIT (Control Objectives for Information and Related Technology). The course will discuss a structured approach to data governance by defining roles and responsibilities, establishing policies and procedures, and outlining best practices for managing data. Through a combination of lectures, case studies, and practical exercises, students will learn about data governance strategies, data infrastructure, and frameworks used to manage data in an organization.
Learning Outcomes:
Students, on successful completion of the course, will be able to
- Explain principles, concepts and importance of data governance in an organization.
- Identify key stakeholders and explain their roles and responsibilities in organizational data governance.
- Define strategies, policies and procedures that define how data should be used, accessed, stored, and protected, including data security policies, data retention policies, data privacy policies, and other policies and standards.
Selected Topic: AI Regulation and Governance
This course aims to provide a comprehensive understanding of how to effectively regulate and govern AI technologies. It covers key concepts such agile governance, international standards, and ethical principles, and examines how global initiatives and policies can be harmonized to foster responsible AI development. The course also explores the future challenges of AI regulation.
Semester 3
Selected Topic: Strategic Cybersecurity Governance
This course aims to equip students with a deep understanding of the intersection between technology and especially AI-diplomacy, and global security. It explores how tech companies act as geopolitical actors, shaping international relations and influencing global power dynamics. The course also explores the ethical challenges posed by AI in a global manner, focusing on the need for transparency, accountability, and trust in AI systems.
Selected Topic: Techplomacy, Ethics and Conflicts in Artificial Intelligence
This course aims to provide a thorough understanding of how to manage and govern cybersecurity within a global context. It covers the identification and analysis of cyber threat actors and emerging threats, including autonomous weapons and advanced attack vectors. The course also explores international and legal frameworks for cybersecurity governance, exploring global policies, agreements, and national legislation. Finally, it focuses
on modern cyber risk management, including crisis preparation, scenario simulation, and effective incident response strategies.
AT61.046: Master’s Project
The Master’s Project serves as the capstone course of the program, enabling students to apply their technical and analytical skills to address a real-world challenge in Data Science and AI applications. Students will undertake a research-based or practice-oriented project, define a clear problem statement, design an appropriate methodology, and implement data-driven solutions using suitable models, tools, and techniques. Projects are encouraged to incorporate considerations of data governance, ethics, and AI policy where relevant. Deliverables include a comprehensive written report, an oral presentation, and, when applicable, a working prototype or proof-of-concept. The course emphasizes research rigor, practical relevance, and the ability to effectively communicate results to both technical and non-technical audiences.
COURSE ENROLLMENT
Contact Us
Vietnam
- FCC Building, 6th Floor, 45 Dinh Tien Hoang Street, Sai Gon Ward, Ho Chi Minh city
- [email protected]
- +84 98 360 4487
Thailand
- P.O. Box 4, 58 Moo 9, Km. 42, Paholyothin Highway, Klong Luang, Pathum Thani
- [email protected]
- +84 98 360 4487