Data Analytics Certificate Program

Who will benefit from this course?

This course is ideal for internal auditors, risk management professionals, or compliance specialists.

Course Objectives

By the end of this course, participants will:

• Understand the design choices behind various data analytics tools and technologies, and develop the ability to select the most appropriate tools for specific analytical tasks, including the use of R and other programming languages.
• Understand the design choices behind various data analytics tools and technologies, and develop the ability to select the most appropriate tools for specific analytical tasks, including the use of R and other programming languages.
• Gain proficiency in recognizing patterns within unstructured data, enabling the extraction of meaningful insights from diverse data sources.
• Develop skills in creating SQL queries, building data models in Excel, and conducting statistical analyses to interpret structured data effectively.
• Learn the principles of visual communication psychology, and create compelling dashboards and advanced visualizations to effectively convey data-driven stories.
• Design and automate quality assurance processes, develop training programs, and utilize AI, LLM, SLM, macros, scripts, and server-based automation techniques to streamline data analytics operations.
• Create reusable tests and analytical models, implement continuous auditing and monitoring practices, and understand the limitations of audits to build a strong business case for collaboration with data scientists.

Unit Topics

Tools & Technology

• Deep dive into the design choices behind various tools and what they can do
• Using R and programming languages for analytics
• How to make a choice on the tools to use
• Evaluating the scalability and integration capabilities of different tools

Analyzing Unstructured Data

• Characteristics of unstructured data
• Pattern recognition
• Challenges in and techniques for analyzing unstructured data

Analyzing Structured Data

• Creating queries with SQL
• Creating and using data models in Excel
• Statistical analysis
• Data cleaning and preprocessing techniques

Storytelling with Data

• The psychology behind visual communication
• Creating dashboards
• Create visuals beyond standard “charts” to communicate the story
• Best practices for data visualization
• Interactive data visualizations and user experience (UX) design

Creating a Data Analytics Program

• Data governance and compliance considerations
• Ethical considerations in data analytics
• Automating quality assurance through data analytics
• Creating a data analytics training program
• Automating data analytics:
  • Using AI / LLM / SLM
  • Automating analytics through macros & scripts
  • Server based automation techniques (RPA, SQL Server)
• Creating reusable tests and analytical models
• Implementing continuous auditing and monitoring
• Understanding internal audit’s limitations and creating a business case for help from data scientists.