SITizens Learning Credits

Overview

Accountants and auditors can use analytics to make sense of vast amounts of financial data to evaluate business performance, identify and manage risk, and analyse customer behaviour to anticipate market trends more efficiently and accurately than ever before. The growing gap between what accountants’ report and what decision-makers need involves a shift from analysing descriptive historical information to analysing predictive information, such as budgets and what-if scenarios. Predictive analytics are in demand now because they provide actionable insights to businesses. Accountants and auditors need to increase their expertise in these areas to add value to their businesses. Predictive analytics integrates data from multiple sources (e.g., enterprise resource planning, point-of-sale, and customer relationship management systems) to predict future outcomes based on statistical relationships found in historical data using regression-based modelling. One of the most common applications of predictive analytics is calculating a credit score, which indicates the probability of on-time future loan payments.

Teaching Team

What You’ll Learn

Introduction to Classical and Machine Learning-based Predictive Models
  • This topic provides a foundation on predictive analytics. Participants will understand the difference between classical and machine learning-based techniques on predictive modelling.
Regression Algorithms
  • Participants will be guided to implement regression models in accounting and auditing contexts.
Classification Algorithms
  • Participants will be guided to implement classification models in accounting and auditing contexts.
Clustering Algorithms
  • Participants will be guided to implement clustering models in accounting and auditing contexts.

Who Should Attend

  • Accountants and Auditors

SITizens Learning Credits (SLC) - Eligible Course

This course is SITizens Learning Credits (SLC) eligible. Please refer to the user guide how to register for courses utilising your SLC.

Find out more about SITizens Learning Credits (SLC).

Certificate and Assessment

A Certificate of Participation will be issued to participants who
  • Attend 75% of the workshop; and
  • Undertake non-credit bearing assessment (during course)

Schedule

Day 1
Topics
Registration & Introduction
Introduction to Predictive Analytics
Introduction to Classical and  Machine Learning Models
Python Revisit (Machine Learning and Visualization Packages)
Lunch 
Regression and Classification Model
Tea Break
Classification and Clustering Model
Quiz/ Q&A
End of Day

Fees

Category Full Fee After SF Funding
Singapore Citizen (Below 40) /
Singapore PR
$1,177.00 $353.10
Singapore Citizen (40 & above) $1,177.00 $133.10
Non-Singaporeans $1,177.00 Not Eligible

Note:
  • All figures include GST. GST applies to individuals and Singapore-registered companies.
  • You can opt for either SF Series Funding or Mid-Career Enhanced Subsidy. Both cannot be combined.

» Learn more about funding types available

Terms & Conditions:

SkillsFuture Series Course Funding


In order to be eligible for the 70% training grant awarded by SkillsFuture, applicants (and/or their sponsoring organisations where applicable) must:
  1. Be a Singaporean Citizen or Singapore Permanent Resident
  2. Not receive any other funding from government sources in respect of the actual grant disbursed for the programme

SkillsFuture Mid-Career Enhanced Subsidy

To be eligible for the 90% enhanced subsidy awarded, applicants (and/or their sponsoring organisations where applicable) must:
  1. Be a Singaporean Citizen
  2. Be at least 40 years old
  3. Not receive any other funding from government sources in respect of the actual grant disbursed for the programme

SIT reserves the right to collect the balance of the programme fees (i.e. the potential grant amount) directly from the applicants (and/or their sponsoring organisations where applicable) should the above requirements not be fulfilled.

SIT reserves the right to make changes to published course information, including dates, times, venues, fees and instructors without prior notice.
SITizens Learning Credits

Key Info

Venue SIT@Dover, 10 Dover Drive S138683
Time 09:00 AM to 06:00 PM
Date 22 Oct 2021 (Fri)
Registration is Closed.

You May Also Like