13 to 14 Apr 2020
16 to 30 Mar 2020
16 hours / 2 days
9:00am to 6:00pm
Mode of Training:
The practice of Data Analytics across industries require data mining, machine learning, and computational modelling skills.
For example, Industries like Telcos use Machine Learning and Computational techniques to identify customer behaviours and offer targeted campaigns and products. Financial services uses Machine Learning and Computational techniques for identification of Fraud and prediction of loan default and Manufacturing industry uses Machine Learning and Computational techniques for Preventive Maintenance and Inventory Optimization. Our aim is to equip participants with the ability to identify and utilise appropriate algorithmic techniques to solve relevant business problems
To equip participants with the ability to identify and utilise appropriate advanced statistical, machine learning, computational algorithms and data models to test hypotheses and derive patterns or solutions.
• Introduction to Computational Modeling
• Hands on exercise on concepts of Computer Modeling
• Pitfalls of Computational Modeling Techniques
• Introduction to Optimisation
• Hands on exercise on simple optimization concepts
• Introduction to Computational Intelligence and Machine Learning algorithms
• Pitfalls of Advanced Analytics, Machine Learning and Computational Modeling Techniques and how to avoid them
• Hands on exercise on Analytics using Computational Intelligence and ML
IT Analysts, Data Analysts, Technical Leads, IT Engineers, Data Engineers, Software Developers
Minimum Entry Requirements / Assumed Skills and Knowledge
1. Knowledge of Introductory Statistics, Analytics, Computational techniques or relevant skill preferred
2. Participants are required to bring their own laptops for this training
3. Basic understanding of a programming language is preferred but not mandatory
Award / Certification / Accreditation
• Certificate of Performance (electronic Certificate will be issued)
A Certificate of Performance will be awarded to participants who pass the assessments and meet at least 75% attendance rate
Course Fees payable:
|Singapore Citizens aged below 40 and Singapore PRs||Singapore Citizens||Others|
|Non-SME sponsored||1Sponsored by SME||2Aged 40 and above||3Aged 35 and above, and earn ≤ $2,000 per month|
Singaporeans aged 25 years and above may use **SkillsFuture Credit balance to offset respective course fees.
1 Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) Scheme. For more information, click here. Please submit the attached “Declaration Form for Enhanced Training Support Scheme for SME” together with your online application.
2 Under the SkillsFuture Mid-career Enhanced Subsidy. For more information, visit the SkillsFuture website here.
3 Under the Workfare Training Support (WTS) scheme. For more information on the scheme, click here.
Please click here for more information on funding incentives.
1. All applications must be made via Online Registration at www.pace.sp.edu.sg
Course fees can be paid by the following payment modes:
a) Credit Cards, Internet Banking, NETS (Not Applicable for company sponsored)
For e-payment using Visa/Master cards and Internet Banking, please click on the ‘Make e-Payment’ button on the acknowledgement page to proceed.
b) For NETS payment, you can pay at:PACE Academy
Please make cheques payable to “Singapore Polytechnic”. Do cross the cheque and write the Registration Reference ID, Applicant Name and NRIC/FIN number at the back of the cheque. When you have completed required details, you may mail the cheque here.
Please note that an administrative charge of $15 will be imposed for any returned cheques from the bank or financial institution.
2. All successful applicants will be notified with a letter of confirmation via email.