Predictive Analytics for Accountants and Auditors

Training Provider: SINGAPORE INSTITUTE OF TECHNOLOGY
Course Reference: TGS-2021006991
S$330
Original: S$1,100
Save S$770

About This Course

Course objectives include the following topics:
- 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.

What You'll Learn

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.

Entry Requirements

Understand basic Python programming.

Course Details

Duration 8 hours
Language Not specified
Training Commitment Not specified
Total Enrolled New course
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