Programs
Admissions
Ateneo De Manila University
Designing Tomorrow
MS Big Data Science (Queen Mary University in London)
Launch in SY 2019-2020
Master of Science in Data Science /Master of Science in Big Data Science is a dual degree program jointly developed and delivered by Ateneo de Manila University and Queen Mary University of London. An 18 to 21-month program on a full-time basis, it allows the student to spend up to four months in London and obtain an MS Data Science degree from Ateneo de Manila and an MSc Big Data Science degree from Queen Mary.
MS Data Science from Ateneo de Manila is a research-oriented degree that provides foundation courses in data science and a range of electives to pursue specific areas of interest. It has a thesis requirement where students will embark on practical data science projects using real datasets. The student must likewise prepare a manuscript of his/her thesis work that is ready for submission to a reputable national or international journal or conference.
MS Big Data Science from Queen Mary University of London leverages the world-leading expertise in research at Queen Mary with their strategic partnership with IBM and other leading IT sector companies to offer students a foundational MSc on the field of Data Science.
Curriculum
The Data Science curriculum has a total of 36 units. Two (2) Ateneo courses will be credited to the Queen Mary degree and Three (3) Queen Mary courses will be credited to the Ateneo degree. There is an option to start during Intersession or the First Semester as shown below.
The dual degree requires the successful completion of two projects – one each for the Ateneo and Queen Mary degree. Though distinct, these two projects can be related and/or be an expansion of the other under the guidance of mentors from each institution.
INTERSESSION INTAKE
|
ATENEO COURSES |
|
QUEEN MARY COURSES |
|---|---|---|
|
Intersession (June – July) |
||
|
Programming with Databases |
→ |
Programming with Databases (Module Elective 1) |
|
Data Visualization |
|
|
|
First Semester (August – December) |
||
|
Applied Statistics |
→ |
Applied Statistics |
|
Data Mining |
↔ |
Data Mining (offered online by Queen Mary and team-taught by faculty of both universities) |
|
Big Data Processing |
↔ |
Big Data Processing (offered online by Queen Mary and team-taught by faculty of both universities) |
|
Methods and Domains Course 1 |
|
|
|
Second Semester (January – March) at Queen Mary |
||
|
Methods and Domains Course 2 |
← |
Module Elective 2 |
|
Methods and Domains Course 3 |
← |
Module Elective 3 |
|
Methods and Domains Course 4 |
← |
Module Elective 4 |
|
|
|
Module Elective 5 |
|
|
|
QMUL Project |
|
Intersession (June – July) |
||
|
Methods and Domains Course 5 |
|
|
|
Thesis I |
|
|
|
First Semester (August – December) |
||
|
Thesis II |
|
|
FIRST SEMESTER INTAKE
|
ATENEO COURSES |
QUEEN MARY COURSES |
|
|---|---|---|
|
First Semester (August – December) |
||
|
Programming with Databases |
→ |
Programming with Databases (Module Elective 1) |
|
Applied Statistics |
→ |
Applied Statistics |
|
Data Mining |
↔ |
Data Mining (offered online by Queen Mary and team-taught by faculty of both universities) |
|
Big Data Processing |
↔ |
Big Data Processing (offered online by Queen Mary and team-taught by faculty of both universities) |
|
Second Semester (January – March) at Queen Mary |
||
|
Methods and Domains Course 1 |
← |
Module Elective 2 |
|
Methods and Domains Course 2 |
← |
Module Elective 3 |
|
Methods and Domains Course 3 |
← |
Module Elective 4 |
|
Module Elective 5 |
||
|
QMUL Project |
||
|
Intersession (June – July) |
||
|
Data Visualization |
||
|
Methods and Domains Course 4 |
||
|
First Semester (August - December) |
||
|
Thesis I |
||
|
Methods and Domains Course 5 |
||
|
Second Semester (January – May) |
||
|
Thesis II |
||
Ateneo Electives: Business Intelligence, Computational Science, Pattern Recognition, Machine Learning, Natural Language Processing, Social Computing, Affective Computing, Financial Applications, Modeling and Simulation, Geographic Information Systems and Geospatial Analytics, Big Data Project Management, or other courses that cover methods or domains in data science
Queen Mary Electives: Introduction to Computer Vision, Introduction to Object-Oriented Programming, Machine Learning, Semi-structured Data and Advanced Data Modelling, Business Technology Strategy, Could Computing, Data Analytics, Digital Media and Social Networks, Information Retrieval, Natural Language Processing, Techniques for Computer Vision, The Semantic Web
Retention Policy
Aside from the standard Ateneo policies for retention of graduate students, MS Data Science – MSc Big Data Science would require a minimum grade of B+ in each of the foundational courses required in the first two semesters of the program.