Data Science emerged as a result of the proliferation of vast data sets in various fields, leading to the need for automated methods to facilitate efficient and effective data analysis by humans. This program equips individuals with the necessary expertise to collect, manage, analyze, and visualize data, enabling them to make informed and ethical decisions driven by data. It draws upon the convergence of computer science, statistics, and domain-specific knowledge to address real-world challenges and find practical solutions.
The program is composed of 36.0 academic units, broken down into 18.0 units of core courses, 12.0 units of elective courses, and 6.0 units of thesis.
The core courses are as follows. Course descriptions are provided in another section.
Elective courses (12.0 units) cover topics in big data, applied machine learning, and analytics.
To be eligible for consideration to the MS in Data Science program, you must hold a Bachelor’s degree from an accredited institution, which includes a minimum of four years of full-time study. Please be advised that admission to a program that is different from your undergraduate field of study may require the completion of bridging / remedial courses before you will be permitted to begin graduate-level coursework. Due to the technical nature of the program, students will be expected to have a strong technical background, typically with an undergraduate degree in STEM.
All applicants to DLSU graduate programs must follow, without exception, the requirements, schedule and procedures set by the Office of the Admissions and Scholarships which can be found here. We only accept students every term 1. The application period usually starts in May every year.
The program is designed to be accomplished in at least 3 years for part-time students. Below is a sample plan of study. Please note that this sample plan of study does not include possible bridging / remedial courses for some students. See section on bridging / remedial courses below for more details.
| Year | Term | Part-Time Study Plan (3 years) Course | Units |
|---|---|---|---|
| 1 | 1 | DAT100M - Principles of Data Science DAT101M - Data Visualization |
3.0 3.0 |
| 2 | DAT203M - Machine Learning for Data Science DAT290M - Research Methods for MSDS |
3.0 3.0 |
|
| 3 | DAT102M - Data Governance, Ethics, and Privacy DAT204M - Big Data and Scalable Computing |
3.0 3.0 |
|
| 2 | 1 | Elective 1 Elective 2 |
3.0 3.0 |
| 2 | Elective 3 Elective 4 |
3.0 3.0 |
|
| 3 | DAT900M - Comprehensive Exam | - | |
| 3 | 1 | Thesis Proposal Defense DAT801M - Thesis Writing 1 for MSDS |
- 6.0 |
| 2 | DAT802M - Thesis Writing 2 for MSDS | - | |
| 3 | Thesis Final Defense DAT803M - Thesis Writing 3 for MSDS DAT910M - Publication Course |
- |
The program is designed to be accomplished in at least 2 years for full-time students. Below is a sample plan of study. Please note that this sample plan of study does not include possible bridging / remedial courses for some students. See section on bridging / remedial courses below for more details.
| Year | Term | Full-Time Study Plan (3 years) Course | Units |
|---|---|---|---|
| 1 | 1 | DAT100M - Principles of Data Science DAT101M - Data Visualization Elective 1 |
3.0 3.0 3.0 |
| 2 | DAT203M - Machine Learning for Data Science DAT290M - Research Methods for MSDS Elective 2 Elective 3 |
3.0 3.0 3.0 3.0 |
|
| 3 | DAT102M - Data Governance, Ethics, and Privacy DAT204M - Big Data and Scalable Computing Elective 4 |
3.0 3.0 3.0 |
|
| 2 | 1 | DAT900M - Comprehensive Exam Thesis Proposal Defense DAT801M - Thesis Writing 1 for MSDS |
- - 6.0 |
| 2 | DAT802M - Thesis Writing 2 for MSDS | - | |
| 3 | Thesis Final Defense DAT803M - Thesis Writing 3 for MSDS DAT910M - Publication Course |
- |
Courses (i.e., subjects) under the MSDS program are usually delivered either using a fully online (synchronous) or a hybrid approach. Sessions for fully online courses are usually held once a week for 3.0 hours per session via online platforms. Other full online courses are offered twice a week for 1.5 hours per session via online platforms.
Courses following the hybrid set-up are composed of both online (synchronous) and face-to-face sessions. Currently, each course in the hybrid set-up meets two (2) sessions a week for 1.5 hours per session, where one session is held online and the other is held face-to-face. For the Monday-Thursday schedule, the Monday session is held online while the Thursday session is held face-to-face. For the Tuesday-Friday schedule, the Tuesday session is held online while the Friday session is held face-to-face.
Courses are usually held on weekdays (Monday to Friday) from 6:00 PM. Some courses are also offered on Saturdays, depending on the schedule set per term.
For students coming from a non-technical background, we usually require some bridging / remedial courses before you start taking your MSDS core courses. The recommended courses will be based on your application and your entrance exam result. Note that this will affect your plan of study and the number of terms required to complete the program. At a minimum, at least one term will be added to your plan of study to accommodate bridging / remedial courses before you begin your MSDS core courses.
For scholarship opportunities, we recommend you to submit an application to the Engineering Research and Development for Technology (ERDT) scholarship. Visit https://www.dlsu.edu.ph/erdt/ for more information about the qualifications, requirements, terms, privileges, and deadlines of the scholarship.
This course will cover the foundations of data science by understanding the different forms of data available and learning how to ethically handle and analyze them. It will also cover applied statistics to data to ensure proper handling and processing of data prior to visualization and machine learning. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their ethical application to solving problems. To make the learning contextual, real datasets from a variety of disciplines will be used.
This course will give an overview of data visualization as well as the overlapping fields of information and scientific visualization. Students will learn to programatically process and analyze data with Python libraries widely used in statistics, engineering, science and finance. It will also cover the design of effective visualizations. Students will learn to build data visualizations directly using matplotlib (Python) and interactive web-based visualizations using JavaScript and D3. Project groups of students will each propose, design and build a visualization of a data set. The course requires students to have programming experience.
Working with real world data will always have real world implications in terms of governance, ethics, and privacy. Prior to applying any analytical methodology to real world data, individuals must first understand how different data sources may be connected and how they should be organized for easy analysis and solution development. Data ethics and data privacy are also major concerns that every data scientist should be concerned with prior to publishing results of their analysis.
This course introduces the students to a new and actively evolving interdisciplinary field of modern data analysis. Started as a branch of Artificial Intelligence, it attracted the attention of physicists, computer scientists, economists, computational biologists, linguists and others and became a truly interdisciplinary field of study. In spite of the variety of data sources that could be analyzed, objects and attributes that form a particular dataset pose common statistical and structural properties. The interplay between known data and unknown ones give rise to complex pattern structures and machine learning methods that are the focus of the study. In the course we will consider methods of Machine Learning and Data Mining. Special attention will be given to the hands-on practical analysis of the real world datasets using available software tools and modern programming languages and libraries.
The course aims to provide a broad understanding of big data and current technologies in managing and processing them with a focus on the urban environment. General topics include big data ecosystems, parallel and streaming programming model, MapReduce, Hadoop, Spark, Pig, and NoSQL solutions. Hands-on labs and exercises will be offered throughout to bolster the knowledge learned in each module.
This course will cover how to conduct proper and ethical data science research and how to go about the thesis or capstone presentations. Ideally, if the thesis or capstone was designed to be in collaboration with an industry partner, this course should also cover how to work with clients and basic project management and risk management.
What is the duration of the MSDS program?
The MSDS program is typically designed to be completed in 2 years on a full-time basis or 3 years on a part-time basis.
Can I apply to the MSDS program if my undergraduate degree is in a non-technical field?
Yes, our MSDS program welcome applicants from diverse backgrounds. However, some additional bridging / remedial courses may be required to ensure a solid technical foundation. You will be advised if you need to take these prerequisites depending on your admission test performance.
Is work experience required to apply for the MSDS program?
Work experience is not a requirement for admission to the MSDS program.
What topics are covered in the MSDS curriculum?
The MSDS curriculum typically covers a range of subjects including data analysis, machine learning, data visualization, big data processing, and data ethics.
Are there opportunities for research or internships in the MSDS program?
The MSDS program provides opportunities for research projects in ALTDSI. These experiences allow students to apply their knowledge in real-world scenarios and gain practical skills. Faculty members can provide more information on research opportunities and apprenticeships.
Can the MSDS program be pursued on a part-time or online basis?
The MSDS program is designed for both full-time and part-time students. Pure online options cannot be accommodated as there are activities that need to be face-to-face, e.g., major exams, comprehensive exams and defense.
What are the tuition fees for the MSDS program?
It is recommended to contact the Office of the University Registrar or the Finance and Accounting Office for detailed information on tuition fees, or you may estimate the amount using this link: https://www.dlsu.edu.ph/offices/accounting/tuition-and-other-fees/.
Which campus will the MSDS program be run on?
The MSDS program will be conducted at our Manila Campus. This is where students will have access to state-of-the-art facilities, cutting-edge resources, and a vibrant academic community.
Are there any plans to offer the MSDS program on other campuses or online?
Currently, the MSDS program is exclusively offered at our Manila campus. However, we understand the importance of flexibility and adaptability, especially in the current global landscape. As such, certain meetings and classes may be conducted online, depending on the instructor’s discretion and the needs of the program. In addition, the McKinley microcampus, located at the Venice Corporate Center in McKinley, will serve as a dedicated research laboratory for MSDS students and researchers.