9 Topics
5 Days
Programme Overview
Training Description
Who Should Attend
This course is designed for audit professionals who want to enhance their data analysis capabilities and automate audit processes using Python or R, including:
- Internal Auditors
- IT Auditors
- Data Analysts working in Audit
- Audit Managers
- Anyone seeking to automate and enhance audit analysis
Session Objectives
- Understand the benefits of using Python/R for audit automation and analysis.
- Write Python/R code to extract, clean, and transform audit data.
- Perform advanced statistical analysis and modeling on audit data.
- Automate repetitive audit tasks using scripting.
- Visualize audit data to communicate insights effectively.
- Identify risks, trends, and anomalies in large datasets.
- Develop custom audit procedures using Python/R.
- Integrate Python/R with existing audit tools and systems.
- Improve audit efficiency and effectiveness through automation.
- Provide data-driven insights and recommendations to management.
- Enhance their understanding of data analytics best practices.
- Contribute to a more data-driven and strategic internal audit function.
- Stay up-to-date with the latest trends in data analytics for audit.
- Become a more valuable and sought-after audit professional.
- Choose the appropriate language (Python or R) based on specific needs.
About the Course
The "Advanced Data Analytics with Python/R Training Course" is a comprehensive program designed to equip learners with the high-level skills needed for a career in data science and advanced analysis. It typically covers a blend of advanced statistical methods, machine learning, and big data techniques using the industry-leading programming languages, Python and R. Participants will learn to leverage these powerful programming languages to extract, clean, transform, analyze, and visualize data, enabling them to identify risks, trends, and anomalies more efficiently and effectively. This course empowers auditors to move beyond traditional methods and become data-driven strategic advisors, enhancing audit quality and providing deeper insights.
Curriculum & Topics
9 Topics | 5 Days
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Subtopic 1.1:
Why Python/R for Audit? Benefits over traditional tools.
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Subtopic 1.2:
Setting up the development environment (installing Python/R, IDE, libraries).
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Subtopic 1.3:
Basic syntax and data structures (variables, lists, dictionaries/vectors, data frames).
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Subtopic 1.4:
Introduction to key libraries for data manipulation (Pandas/dplyr), analysis (NumPy/base R stats), and visualization (Matplotlib/ggplot2).
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Subtopic 1.5:
Choosing between Python and R for specific audit tasks.
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Subtopic 2.1:
Connecting to various data sources (databases, CSV, Excel, APIs).
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Subtopic 2.2:
Data import and export techniques.
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Subtopic 2.3:
Data cleaning techniques: handling missing values, duplicates, inconsistencies.
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Subtopic 2.4:
Data transformation: filtering, sorting, merging, aggregating data.
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Subtopic 2.5:
Automating data cleaning and preparation steps.
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Subtopic 3.1:
Working with large datasets efficiently.
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Subtopic 3.2:
Advanced data manipulation techniques: string manipulation, regular expressions, date/time handling.
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Subtopic 3.3:
Creating custom functions for data transformation.
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Subtopic 3.4:
Data reshaping and pivoting.
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Subtopic 3.5:
Optimizing data manipulation code for performance.
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Subtopic 4.1:
Descriptive statistics and data summarization.
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Subtopic 4.2:
Hypothesis testing and statistical significance.
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Subtopic 4.3:
Regression analysis and correlation.
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Subtopic 4.4:
Time series analysis for trend identification.
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Subtopic 4.5:
Applying statistical methods to specific audit areas (e.g., fraud detection, risk assessment).
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Subtopic 5.1:
Creating informative and visually appealing charts and graphs.
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Subtopic 5.2:
Data visualization best practices for audit reporting.
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Subtopic 5.3:
Building interactive dashboards for audit insights.
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Subtopic 5.4:
Customizing visualizations for different audiences.
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Subtopic 5.5:
Communicating data-driven narratives through visuals.
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Subtopic 6.1:
Scripting for repetitive audit tasks (e.g., data extraction, report generation).
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Subtopic 6.2:
Building custom audit procedures using Python/R.
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Subtopic 6.3:
Integrating Python/R with existing audit tools and systems.
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Subtopic 6.4:
Scheduling and automating audit scripts.
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Subtopic 6.5:
Developing reusable audit modules and functions.
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Subtopic 7.1:
Anomaly detection and outlier analysis.
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Subtopic 7.2:
Fraud detection using machine learning techniques (if applicable).
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Subtopic 7.3:
Predictive modeling for risk forecasting.
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Subtopic 7.4:
Text mining and sentiment analysis for qualitative data.
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Subtopic 7.5:
Applying advanced analytics to specific audit areas.
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Subtopic 8.1:
Developing small-scale applications for specific audit needs.
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Subtopic 8.2:
Integrating data analytics with audit workflows.
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Subtopic 8.3:
Creating user interfaces for audit applications (basic web frameworks or Shiny for R if chosen).
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Subtopic 8.4:
Deploying and sharing audit applications.
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Subtopic 9.1:
Data governance and security considerations.
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Subtopic 9.2:
Ethical implications of using data analytics in audit.
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Subtopic 9.3:
Staying up-to-date with emerging data analytics technologies and trends.
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Subtopic 9.4:
Best practices for data-driven audit reporting and communication.
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Subtopic 9.5:
The future of data analytics in internal audit.
