Minor in Data Science

Minor in Data Science Program

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.  The minor program needs to take the following courses equivalent to 12 units of coursework to complete the minor program:

  • Principles of Data Science
  • Data Visualization
  • Data Mining and Statistics
  • Introduction to Machine Learning

Course description and schedule of course offering can be viewed here.

Application for Minor in Data Science

  • Wait for the Call for Application to the Minor in Data Science Program via DLSU Helpdesk Announcement (HDA).

Requirements

  • Applicants must be at least a 2nd year student
  • Online application form (see HDA)
  • Printed (as PDF) MLS grade

Lodging Application

  • All applications must be sent to: [email protected]
  • Attach the required documents. Filename convention:
    • <IDnumber>_mlsgrade.pdf
  • Subject: Minor in Data Science Application
  • Deadline:
    • Every 2nd Monday of Term 3 to 6th Monday of Term 3
    • Monitor the University Helpdesk Announcement for the exact period
  • Notice of Application Approval
    • Every 8th week of Term 3
    • Monitor the University Helpdesk Announcement for the exact period

Course Overlap Rules

  • 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 application will be released on the 8th Monday of the term.

Completing the Minor in Data Science

Requirements to complete

  • All 4 courses passed and completed

Process

  • Fill out the Minor in Data Science Completion form and obtain a signature from your home department chair.
  • Scan and email the completed form to [email protected] with subject: Minor in Data Science Completion.
  • Once approved, the Minor in Data Science will appear in your DLSU Official Transcript of Records. 

Deadline

  • You must submit your Minor in Data Science Completion form on the consultation day of the Expected Graduation Term.
  • We will not accept forms submitted earlier than the term in which you expect to graduate, regardless of when you complete the requirements for the minor. 

M.S. in Data Science

Under construction.

Diploma Courses

Foundations of Data Science (active)

Dr. Andrew L. Tan Data Science Institute

in cooperation with the

Center for Professional Development in Business

cordially invites you to an online course on the

Foundations of Data Science

This 40-hour diploma course provides an overview of the field of data science, the job roles available and the skills necessary for the roles. It covers an introductory level of the data science workflow such as data acquisition, local data management, data wrangling, basic data visualization for exploratory analysis and data ethics. It is carefully designed (1) to upskill professionals and students from various STEM and ABM backgrounds towards a data science career, and (2) to instill a deep sense of professionalism among participants using leading-edge principles in data science ethics. Each topic has hands-on exercises with varying degrees of difficulty to ensure that participants are able to absorb the concepts. The course materials will be delivered through our online learning management system, AnimoSpace.

Every week, asynchronous activities and synchronous sessions are conducted. The synchronous sessions will be held every Saturday 10:00 AM to 12:00 PM and 1:00 PM to 3:00 PM starting August 14, 2021. Please click the following link to see the topics and overview of the schedule of the online course. Limited slots are available.

Schedule here. 

Registration here.

Webinar fees: Limited slots are available.

  • P40,000 for non-DLSU individuals
  • P30,000 for DLSU students (25% discount)
  • P32,000 for DLSU faculty (20% discount)
  • P32,000 for DLSU alumni (20% discount)

Early bird rate: Additional 10% discount if you register on or before July 30, 2021.

Machine Learning (active)

Dr. Andrew L. Tan Data Science Institute

in cooperation with the

Center for Professional Development in Business

cordially invites you to an online course on the

Introduction to Machine Learning

This 40-hour diploma course provides an overview of machine learning, and the theory and skills necessary for its practice. It covers an introductory level of the machine learning algorithms such as linear regression, logistic regression, neural networks, decision trees, ensemble models, support vector machines, and techniques for regularizing each model. The course materials will be delivered through our online learning management system, AnimoSpace.

It is designed for students and professionals from various Science and Technology, Engineering and Mathematics (STEM), and Accounting and Business Management (ABM) backgrounds with basic technical skills in Python programming who want to upskill towards a data science career with a focus on machine learning. Each module has hands-on exercises to ensure that participants are absorbing the concepts.

Pre-requisites: Python programming, descriptive statistics

Schedule here.

Registration here.

Webinar fees: Limited slots are available.

  • P40,000 for non-DLSU individuals
  • P30,000 for DLSU students (25% discount)
  • P32,000 for DLSU faculty (20%)
  • P32,000 for DLSU alumni (20% discount)

Early bird rate: Additional 10% discount if you register on or before July 23, 2021.

Data Visualization (to be offered soon)

This short course is all about turning data into interpretable graphics. It covers the design and the creation of data visualizations based on available data and the required tasks. The course includes evaluation of visualization designs, including the choice of visual encoding and color.

Course coverage

  • Data visualization overview
  • Data and tasks abstraction
  • Marks and Channels
  • Visualization of spatial data, networks
    and trees
  • Data reduction