91福利社

Course Details

Specialist Diploma in Data Science (Predictive Analytics)

Overview

  • Course Date:

    21 Oct 2025 to 31 Mar 2026

  • Registration Period:

    1 Jun 2025 to 31 Aug 2025

  • Duration/Frequency:

    2 evenings per week, 6.30 - 9.30pm

  • Mode of Training:

    Classroom

Please note that once the maximum class size is reached, the online registration will be closed. You may register your interest and be notified when there is a new run.

Class schedule:

PDC1 (1st semester):

  • Every Tue and Thu
  • 6:3pm 鈥 9:30pm (face-to-face class)
  • 9:30pm 鈥 10:30pm (e-learning*)

PDC2 (2nd semester):

  • Every Wed 7:30pm 鈥 8:30pm (online class**)
  • Every Thu 6:30pm 鈥 9:30pm (face-to-face class)
  • Every Sat 9:00am 鈥 1:00pm (e-learning*)

*e-learning: the students are not required to attend classes during this period.

**online class: the students may attend from any location of their choice.

Modules in this course conduct in-class tests, typically in the last week of each 7-week or 8-week term in the 91福利社 academic calendar. Attendance at these tests is compulsory.

Students are strongly advised against scheduling any travel plans during mid-semestral (MST) or end-semestral (EST) weeks. Detailed information regarding test schedules can be found on the website.

Course Duration: 
240 hours (1 year)

Please refer to the Academic Calendar.

Enquiries
For enquiries, please email to ptenquiry@sp.edu.sg

Course Objective

The need to handle, prepare, analyse and model data of varied structures is prevalent in the modern day industrial setting including industry sectors such as banking and finance, insurance, healthcare, telecommunications, design and manufacturing, and retail. This course provides graduates with fundamental skills in statistical analysis and predictive analytics that are required by jobs that involve managing, analysing and modelling data and extracting information that is useful to the business.

The objectives of the Specialist Diploma in Data Science (Predictive Analytics) are to provide foundational training in the fundamental concepts and methods of statistics and programming for data science, as well as in specialized skill sets in the area of statistical modelling and prediction. Graduates of the course will be competent in preparing data, performing statistical analysis, building and deploying predictive models and quantifying risks associated with the prediction.

More Information

The course is a one-year part-time programme. It consists of 2 Post Diploma Certificates (PDCs) conducted over 2 semesters, 1 PDC per semester. PDC1 starts first, and classes are conducted in the evening.

This course consists of 2 PDCs. Each comprises two modules and their details are as follows:

Please click for the Module Synopsis

PDC Exemptions for Specialist Diplomas in Data Science

Participants who have graduated from this course may be eligible for PDC1 exemption if they plan to register for another Specialist Diploma in Data Science:

Please note exemptions are evaluated on a case-by-case basis. Interested participants may email ptenquiry@sp.edu.sg. Our team will get in touch to address your queries and guide you through a separate application process. 

Module Exemptions

Upon completion of the Specialist Diploma, you may also receive:

  • An exemption of up to 12 subject credits for . 
  • An exemption for 2 modules, namely Data Programming in Python and Data Visualisation, for , awarded by UOL.
  • Module exemption for , subject to satisfactory performance. Suitable graduates will enjoy a waiver for the MTech EBAC Graduate Certificate modules of Statistics Bootcamp II and Predictive Analytics - Insights of Trends. The waiver will apply to all suitable graduates of the Specialist Diploma in Data Science (Data Analytics) programme from October 2021 onwards.

Course Fee

For more information on course fee / or to apply, click on the 鈥淩egister鈥 button.

Course fees are reviewed periodically and adjusted as necessary to cover the cost of education and to enable the polytechnic to continue investing in delivering high-quality education.