Data Science (DS) is concerned with the extraction of useful knowledge from data sets. It is closely related to the fields of computer science, mathematics, and statistics. It is a relatively new term for a broad set of skills spanning the more established fields of machine learning, data mining, databases, and visualization, along with their applications in various fields. In 2012, Harvard Business Review called data science “The Sexiest Job of the 21st Century”.
Artificial Intelligence (AI) is the broad field conceived in 1956 as the automation or simulation of human intelligence. AI has two primary “levels”. The first level, “narrow AI”, concerns perception, statistical inference, and actuation, drawing on data science, sensors, and robotics. The second level, sometimes called “artificial general intelligence (AGI), is concerned with more complex or flexible reasoning and decision-making in less constrained domains.
The AIT Masters in DS&AI was designed in partnership with the Erasmus+ DS&AI consortium, a group of 15 European and Asian universities with the mission of bringing European-standard advanced education to Asia.
Research Focus Area
- Data modeling and management
- Machine learning
- Data mining
- Data science
- Sensors
- Robotics
- Software development
- Artificial intelligence
- Big Data and Deep Learning
Preferred background
To study in the DS&AI field, the students should fulfill one of the following backgrounds.
- Computer Science/Computer Engineering/ICT.
- Engineering background with work experience, mathematical skills, and programming skills.
- Diverse backgrounds such as business, finance, or other non-engineering fields. Candidates must take a foundation course in calculus, discrete mathematics, linear algebra, and basic computer programming.
Course Structure
Major in DS&AI
Master of Science/Master of Engineering in Data Science and Artificial Intelligence.
| Thesis Option | Research Study Option | |
|---|---|---|
| Required Courses | 14 credits (5 courses) | 14 credits (5 courses) |
| Elective Courses | 6 credits (2 courses) | 18 credits (6 courses) |
| Institute Wide Courses | 3 credits (1 course) | 3 credits (1 course) |
| Seminar: Required Pass/Fail | 1 credit | 1 credit |
| Required Internship | 0 credit | 0 credit |
| Total Credits Coursework | 24 credit | 36 credit |
| Thesis/Research Study Credits | 24 credit | 12 credit |
| TOTAL CREDIT REQUIREMENT | 48 credit | 48 credit |
Required courses
- Data Modeling and Management
- Machine Learning
- Business Intelligence and Analytics
- Computer Programming for Data Science and Artificial Intelligence
- Artificial Intelligence: Natural Language Understanding
Elective courses
- Artificial Intelligence: Knowledge Representation and Reasoning
- Computer Vision
- Artificial Intelligence: Problem Solving and Planning
- HCI and Information Visualization
- Recent Trends in Machine Learning
- Multicriteria Optimization and Decision Analysis
- Software Development and Project Management
Minor in DS&AI
Master of Science/Master of Engineering, Minor in Data Science and Artificial Intelligence.
| Research Study Option | ||
|---|---|---|
| Major Required/Elective Courses | 24 credits | |
| Required Courses in DS&AI | 6 credits (2 courses) | · Data Modeling and Management · Machine Learning |
| Elective Courses in DS&AI | 6 credits (2 courses) | Choose 2 courses from 8 options listed: · Business Intelligence and Analytics · Computer Programming for Data Science and Artificial Intelligence · Artificial Intelligence: Problem Solving and Planning · Artificial Intelligence: Knowledge Representation and Reasoning · Computer Vision · Artificial Intelligence: Natural Language Understanding · Recent Trend in Machine Learning · Multicriteria Optimization and Decision Analysis |
| Total Credits Coursework | 36 credits | |
| Research Study Credits | 12 credits | |
| TOTAL CREDIT REQUIREMENT | 48 credits |
