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.
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
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.
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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:
- Be a Singaporean Citizen or Singapore Permanent Resident
- 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:
- Be a Singaporean Citizen
- Be at least 40 years old
- 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.
