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 2025 - 2026 (September)
All non-BSCS and non-BSMTHCAP students must complete the following courses to fulfill the 12.0 units required for the Minor in Data Science program.
| Course | Units | Schedule |
|---|---|---|
| DATA100 - Principles of Data Science | 3.0 units | Term 1 (Regular offering) |
| DATA101 - Data Visualization | 3.0 units | Term 2 (Regular offering) |
| DATA102 - Data Mining and Statistics | 3.0 units | Term 3 (Regular offering) |
| DATA103 - Introduction to Machine Learning | 3.0 units | Term 1 (Regular offering) |
BSCS-ST students must complete the following required courses for the Minor in Data Science program.
| Course | Units | Schedule |
|---|---|---|
| DATA100 - Principles of Data Science | 3.0 units | Term 1 (Regular offering) |
| DATA101 - Data Visualization | 3.0 units | Term 2 (Regular offering) |
On the other hand, BSCS-CSE and BSCS-NIS students must complete the following required courses for the Minor in Data Science program.
| Course | Units | Schedule |
|---|---|---|
| DATA100 - Principles of Data Science | 3.0 units | Term 1 (Regular offering) |
| DATA101 - Data Visualization | 3.0 units | Term 2 (Regular offering) |
| DATA103 - Introduction to Machine Learning | 3.0 units | Term 1 (Regular offering) |
In addition to the required courses, all BSCS students must take other data science courses to complete the 12.0 units required for the Minor in Data Science program. BSCS-CSE and BSCS-NIS students are not allowed to take DATA102, while BSCS-ST students are not allowed to take both DATA102 and DATA103 to complete their minor program, since the content of these courses overlaps with existing courses in their curriculum.
BSCS students can choose among the following courses to fulfill the remaining number of units required for the Minor in Data Science program.
| Course | Units | Schedule |
|---|---|---|
| DATA003 - Fundamentals of Statistics | 3.0 units | Term 1 (Usual offering) |
| DATA004 - Probability and Statistics | 3.0 units | Term 2 (Usual offering) |
| DATA111 - Introduction to Big Data | 3.0 units | Term 3 (Regular offering) |
| DATA113 - Ethical Data Science | 3.0 units | Term 3 (Regular offering) |
| DATA400 - Geospatial Analysis | 3.0 units | Elective (Not regularly offered) |
| DATA401 - Geospatial Mapping and Analytics | 3.0 units | Elective (Not regularly offered) |
BSCS students can also take other data science elective courses announced per term by ALTDSI. This provision is not applicable to non-BSCS and non-BSMTHCAP students. These students must complete the required courses to meet the program outcomes.
BSMTHCAP students must complete the following required courses for the Minor in Data Science program.
| Course | Units | Schedule |
|---|---|---|
| DATA102 - Data Mining and Statistics | 3.0 units | Term 3 (Regular offering) |
| DATA103 - Introduction to Machine Learning | 3.0 units | Term 1 (Regular offering) |
In addition to the required courses, BSMTHCAP students must take other data science courses to complete the 12.0 units required for the Minor in Data Science program. BSMTHCAP students are not allowed to take DATA100 and DATA101 to complete their minor program, as the content of these courses overlaps with existing courses in their curriculum.
BSMTHCAP students can choose among the following courses to fulfill the remaining number of units required for the Minor in Data Science program.
| Course | Units | Schedule |
|---|---|---|
| DATA111 - Introduction to Big Data | 3.0 units | Term 3 (Regular offering) |
| DATA113 - Ethical Data Science | 3.0 units | Term 3 (Regular offering) |
| DATA400 - Geospatial Analysis | 3.0 units | Elective (Not regularly offered) |
| DATA401 - Geospatial Mapping and Analytics | 3.0 units | Elective (Not regularly offered) |
BSMTHCAP students can also take other data science elective courses announced per term by ALTDSI. This provision is not applicable to non-BSCS and non-BSMTHCAP students. These students must complete the required courses to meet the program outcomes.
Make sure to pre-enlist the courses that you will take in future terms to help us estimate the number of sections to open. Since the pre-enlistment period comes before the notice of result, please pre-enlist your data science courses if you are willing to take it next term even if you have not received any results yet about your application.
Enlistment in Minor in Data Science courses are held during inter-college enlistment period only. If you are not from CCS, Animo.Sys will prevent you from enrolling in Data Science courses until the inter-college enlistment period begins. No students, including those from CCS, are permitted to enroll in any Data Science courses before the inter-college enlistment period.
Courses under the minor program should be enlisted and enrolled like a regular course during the enrollment period via Animo.Sys. 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.
The program requires 12.0 units of additional coursework. Tuition will be charged accordingly. All rules and deadlines regarding dropping applies to the courses under the minor program.
Equivalent courses may be taken as advised by ALTDSI per term. Equivalent courses are typically reserved for students within a specific degree program. As such, enrollment in these courses will only be opened and announced once all designated students have secured their slots.
Once you complete all your required courses in the minor program and your degree program, please fill-up the completion form. This is important since this is our basis in granting the minor program title for each student.
If you wish to drop from the minor program, you have to send an email to us. All completed courses will still be included in transcript of records and will be counted towards your CGPA.
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.
Prerequisite: 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.
Prerequisite: 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.
Prerequisite: 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.
Prerequisite: DATA100
This course provides students with foundational knowledge on statistics and probability. It covers topics on sampling, variability, frequency distribution, linear and multiple regression, permutations, combinations, and other probability principles.
Prerequisite: DATA100
This course covers topics in advanced statistics and probability, including joint probability distribution, F-distribution, sampling distribution, chi-square distribution, and analysis of variance. Topics on numerical methods are also introduced, including continuous and discrete functions, non-linear equations, quadratic and cubic functions, interpolation, and regression techniques.
Prerequisite: DATA100
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, data collection, orchestration, parallel and streaming programming models, MapReduce, Spark, and NoSQL solutions. Hands-on labs and exercises will be offered throughout to bolster the knowledge learned in each module.
Prerequisite: DATA100
This course explores the ethical considerations and implications of data science practices. Students will examine the moral responsibilities of data scientists and the impact of data-driven decisions on individuals, communities, and society at large. Throughout the course, students will explore a variety of topics including data privacy, informed consent and data usage, algorithmic bias, explainability, and data sharing.
Prerequisite: DATA100
This course introduces the theoretical and practical knowledge of applied geospatial analysis and some Geographic Information Systems (GIS). Students will learn how to analyze geospatial data, and create map visualizations to communicate results. For their final project, students will create a professional-quality data analysis project using a combination of data identification and collection, map visualization development, and spatial analysis techniques. This course will also prepare them for using geospatial analysis with open source and free software tools.
This course introduces the practical knowledge of spatial analysis using mapping libraries and open source and free software tools. Students will learn how to analyze geospatial data, and create map visualizations to communicate results. For their final project, students will create a professional-quality data analysis project using a combination of data identification and collection, map visualizations, and spatial analysis techniques.
What are the requirements to complete the minor program?
Students must get a passing grade in all of the courses 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.
May I take multiple minor programs?
By default, students will only be awarded one minor program in their diplomas, unless otherwise stated in their degree program.
May I apply to credit an equivalent course towards the minor program?
You may apply to credit an equivalent course towards your minor program as long as it is not a required course for your degree program. For example, CSMODEL is a required course for the BSCS program and is an equivalent course to DATA102. Thus, BSCS students should take CSMODEL to complete their degree program, but are not allowed to take DATA102 anymore for the completion of the minor program since the course content overlaps with CSMODEL. Instead, BSCS students must take other data science courses to complete the 12.0 units required for the Minor in Data Science program. Please see completion requirements above.
How do I drop from the minor program?
If you wish to drop from the minor program, you have to send an email to us.
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.