Jump to Section

Course Overview

In this subject, you will learn data analytics techniques and gain hands-on experience in using appropriate data analytics tools and software. You will have ample opportunities to integrate and apply data mining knowledge and skills to make data-driven decisions for specific business domains.

 

 

Objectives

 

In this module, you will learn to:

  • Apply data mining knowledge and skills to make data-driven decisions for specific business domains such as Customer Analytics and Marketing Analytics

  • Perform various data mining tasks on commercially available platforms by setting the correct parameters and choosing the right modelling algorithms

  • Make business recommendations based on findings of pattern discovery and predictive modeling

Entry Requirements

Those applying for modular units which are drawn from existing full diploma or post-diploma programmes will be expected to have the same minimum entry requirements as those who apply for full qualifications.


To find out the minimum entry requirements specific to this modular course, please click here.

What You'll Learn

  • Fundamentals of data mining

  • Various models in data mining

  • Pattern discovery

  • Predictive modelling

 

 

Modes of Assessment

 

Coursework with no examinations.

 

 

Certification

 

Upon successful completion of the modular unit, students will be awarded a Statement of Result which can count towards the attainment of the Specialist Diploma in Business Analytics.


For more information on course fee / schedule, or to apply,

Course Contact

  • Monday - Thursday: 8:30am - 6:00pm
    Friday: 8:30am - 5:30pm
     
    Closed during lunchtime, 12:00pm - 1:00pm
    and on weekends and public holidays.

  • https://www.tp.edu.sg/tsa
  • Temasek SkillsFuture Academy (TSA)
    Temasek Polytechnic
    East Wing, Block 1A, Level 3, Unit 109
    21 Tampines Ave 1
    Singapore 529757

  • Temasek Polytechnic reserves the right to alter the course, modify the scale of fee, amend any other information or cancel course with low enrolment.