Learn more about various concentrations, explore the Curriculum adjust your Course Schedule to fit your time, know the required Tuition Fees, and understand the expected learning outcomes All the information you need to start your academic journey is here.
Curriculum
Tuition Fee
Learning Outcomes
Graduates are capable of large-scale data processing, building and managing big data infrastructure, and getting insight through a multidisciplinary approach.
| Learning Outcomes | |
| 1 | Master theoretical or applied concepts in the field of Data Science and Artificial Intelligence. |
| 2 | Have in-depth knowledge of Data Science and Artificial Intelligence-based application development. |
| 3 | Have the ability to process large amounts of data and gain insights by using a multi-disciplinary approach. |
| 4 | Have the ability to develop a solution or innovation in the field of Data Science and Artificial Intelligence that is beneficial to society and contributes to scientific development. |
| 5 | Have the ability to develop an innovative solution using intelligent system-based applications, especially those using machine learning concepts. |
| 6 | Have knowledge of Data Science and Artificial Intelligence applications in industry, society, and government. |
| 7 | Have knowledge of the latest (state-of-the-art) developments of concepts, methods, and technologies in the field of Data Science and Artificial Intelligence. |
| 8 | Have the ability to carry out the learning process and implement the latest concepts/methods/technologies in the field of Data Science and Artificial Intelligence. |
| 9 | Able to conduct academic validation or studies according to their field of expertise in solving problems in society or relevant industries through the development of their knowledge and expertise. |
| 10 | Able to make decisions in the context of solving problems in the development of science and technology that pay attention to and apply humanities values based on analytical or experimental studies of information and data. |
| 11 | Students can explain the basics of linguistic computing and natural language processing (NLP). |
| 12 | Students can apply state-of-the-art pre-processing and parsing methods for natural languages. |
| 13 | Students can describe and use appropriate learning techniques and models for NLP problem scenarios. |
| 14 | Students can design and implement NLP applications. |