The Minor in Data Science program aims to produce data literate graduates by equipping the students across the different disciplines with a working knowledge of statistics, probability, and computation enabling them to design and execute precise computational and inferential data analysis for their discipline.
Students of the Minor in Data Science program are expected to:
| Event | Inclusive Dates |
|---|---|
| Application period | May 15, 2024 (W) - June 5, 2024 (W) |
| Evaluation period | June 6, 2024 (H) - July 10, 2024 (W) |
| Notice of results | July 10, 2024 (W) |
Course offering Starts Term 1, AY 2024 - 2025 (September)
| Course | Units | A.Y. 2024-2025 Offering Schedule |
|---|---|---|
| Principles of Data Science (DATA100) | 3 units | Terms 1 and 3 |
| Data Visualization (DATA101) | 3 units | Term 2 |
| Data Mining and Statistics (DATA102) | 3 units | Term 3 |
| Introduction to Machine Learning (DATA103) | 3 units | Term 1 |
| Total | 12 units |
This is an introductory course designed to provide students with the basic concepts of data analysis and statistical computing to explore interesting issues and problems. The course is designed for entry-level students from any major, specifically for students who have not previously taken any statistics or computer science courses.
Pre-requisite: DATA100
This course explores the design and creation of data visualizations based on available data and tasks to be achieved. This process includes basic data modeling, processing, mapping data attributes to graphical attributes, and strategic visual encoding based on known properties of visual perception as well as the task(s) at hand. Students will also learn to evaluate the effectiveness of visualization designs, and think critically about each design decision, such as choice of color and visual encoding.
Pre-requisite: DATA100
This course studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, and generally improve their performance through interaction with data. This course includes data selection, cleaning, and using different statistical techniques. The course will cover all these issues and will illustrate the whole process with examples. Data mining mostly handles tabular data because of its roots in knowledge discovery in databases, but it is not limited to it.
Pre-requisite: DATA100
Machine learning is the automatic induction of new information from large amounts of data to make predictions or decisions without human intervention. This course introduces the students to a broad cross-section of models and algorithms for machine learning, and equips them with skills to discover new information from volumes of data. Data mining and machine learning have overlapping algorithms and methods, but they focus on different things: data mining focuses on finding patterns while machine learning focuses on predictive models.
What are the requirements to complete the minor program?
Students must get a passing grade in all of the courses, i.e., DATA100, DATA101, DATA102, and DATA103, to complete the minor program.
May I take more than one course per term?
Students may opt to take more than one course per term. However, students are not allowed to take DATA100 with the other courses during the same term. This is since DATA100 is a prerequisite to all of the other courses, i.e., DATA101, DATA102, and DATA103.
May I apply to credit an equivalent course towards the minor program?
A maximum of one course offered by other departments may be credited toward the Minor in Data Science, e.g. DATA103 Introduction to Machine Learning under Data Science Institute overlaps with MCLEARN Machine Learning under Software Technology Department.
A student should apply for course overlap as soon as the course grade becomes available in the MLS. The deadline for course overlap application is every 6th Monday of the term. Results of the application will be released on the 8th Monday of the term.
However, note that the course taken should not be credited to an existing course in the original flowchart. Following the example, if your original flowchart requires you to take MCLEARN as part of your original program, you still have to take DATA103 to be able to complete the minor program. You may also consult with the ALTDSI faculty for alternative courses to take.
How do I drop from the minor program?
If you wish to drop from the minor program, you have to send an email to datascience@dlsu.edu.ph.
What happens to the grades for the courses that I took under the minor program after I drop? Will these grades still be included in my transcript of record?
If you drop from the minor program, all completed courses will still be included in your transcript of record and will be counted towards your CGPA.