Data Analytics for Internal Auditors
Master the art of leveraging data analytics within internal auditing to drive organizational success. This course offers a deep dive into advanced analytical techniques and tools that transform traditional audit methodologies. Enhance your ability to conduct insightful audits through hands-on exercises in R and Python while learning to navigate common challenges in adopting data analytics.
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What you'll learn
- Gain expertise in building predictive models and conducting time series analysis.
- Perform customer segmentation using clustering techniques in Python and R.
- Identify KPIs relevant to data analytics initiatives aligned with international standards.
- Learn how to apply descriptive, predictive, and prescriptive analytics in internal auditing.
- Analyze case studies to identify best practices in risk modeling and reporting.
- Implement a strategic framework for adopting data analytics within internal audit processes.
- Understand the benefits of integrating data analytics into internal audit functions.
- Develop skills in hypothesis testing and variance analysis using statistical software R.
Why should you attend?
Data Analytics for Internal Auditors is a comprehensive course designed to equip audit professionals with the necessary skills to integrate data analytics into their internal audit (IA) functions. The curriculum delves into the transformative potential of data analytics in enhancing performance, cost efficiency, accuracy, and innovation within an organization's audit processes. Participants will explore various analytical techniques such as descriptive, predictive, and prescriptive analytics and how they can drive an insight-driven audit model. Through case studies, hands-on exercises, and practical applications, auditors will learn risk modeling, IT cost containment strategies, and effective reporting methods. The course emphasizes the importance of embedding data analytics across IA functions while addressing common challenges to adoption. It introduces frameworks like BADIR to streamline this integration and a 4-phase approach for deploying data analytics effectively. Additionally, learners will gain proficiency in hypothesis testing and variance analysis using R, constructing linear regression models, time series forecasting with ARIMA models in R, and predictive analytics using Python. The program also covers customer segmentation techniques and highlights the roles & responsibilities required for a robust data analytics team within IA groups. Tools such as Data Lakes with Kylo/Ni-Fi, Big Data Storage with Hadoop, Spark processing, and visualization with Tableau are reviewed. Finally, practical applications of data analytics in various IA contexts like accounts payable/receivable, payroll, compliance are discussed alongside key performance indicators that align with international professional practices framework standards.
Course Syllabus
Day 1 - Data Analytics Foundations
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Day 2 - Statistical Analysis Techniques
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Day 3 - Implementation and Standards
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 3
FAQs
Why should you attend?
Data Analytics for Internal Auditors is a comprehensive course designed to equip audit professionals with the necessary skills to integrate data analytics into their internal audit (IA) functions. The curriculum delves into the transformative potential of data analytics in enhancing performance, cost efficiency, accuracy, and innovation within an organization's audit processes. Participants will explore various analytical techniques such as descriptive, predictive, and prescriptive analytics and how they can drive an insight-driven audit model. Through case studies, hands-on exercises, and practical applications, auditors will learn risk modeling, IT cost containment strategies, and effective reporting methods. The course emphasizes the importance of embedding data analytics across IA functions while addressing common challenges to adoption. It introduces frameworks like BADIR to streamline this integration and a 4-phase approach for deploying data analytics effectively. Additionally, learners will gain proficiency in hypothesis testing and variance analysis using R, constructing linear regression models, time series forecasting with ARIMA models in R, and predictive analytics using Python. The program also covers customer segmentation techniques and highlights the roles & responsibilities required for a robust data analytics team within IA groups. Tools such as Data Lakes with Kylo/Ni-Fi, Big Data Storage with Hadoop, Spark processing, and visualization with Tableau are reviewed. Finally, practical applications of data analytics in various IA contexts like accounts payable/receivable, payroll, compliance are discussed alongside key performance indicators that align with international professional practices framework standards.
What you'll learn
- Gain expertise in building predictive models and conducting time series analysis.
- Perform customer segmentation using clustering techniques in Python and R.
- Identify KPIs relevant to data analytics initiatives aligned with international standards.
- Learn how to apply descriptive, predictive, and prescriptive analytics in internal auditing.
- Analyze case studies to identify best practices in risk modeling and reporting.
- Implement a strategic framework for adopting data analytics within internal audit processes.
- Understand the benefits of integrating data analytics into internal audit functions.
- Develop skills in hypothesis testing and variance analysis using statistical software R.
Course Syllabus
Day 1 - Data Analytics Foundations
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 1
Day 2 - Statistical Analysis Techniques
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 2
Day 3 - Implementation and Standards
Short Break
15 minsShort Break
15 minsRecap and Q&A
15 minsLunch
1 hourShort Break
15 minsShort Break
15 minsShort Break
15 minsRecap and Q&A
15 minsEnd of Day 3
Public Pricing
Corporate Pricing
Pax:
Training Provider Pricing
Pax:
Features
Subsidies
FAQs
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