Using Data Effectively and Reliably with AI Analytics
36mIntermediate2025-03-24
Authors
John Johnson
Professional Economist, Author, Speaker
George Lynch
Economist
Course details
Hearing the phrase AI analytics can sound daunting, but don't fret. In this course, instructors George Lynch and John Johnson show you how to think about AI as a super tool that will make you more efficient and effective in your role. Focus on AI for business professionals tasked with assessing, interpreting, and applying outputs in the context of complex business decisions through targeted case studies. Get started applying foundational data concepts and specific subject matter expertise to add value by evaluating and validity of an AI analysis. By the end of this course, you’ll be prepared to leverage your data smarts in an AI-powered analytic world.
Learning objectives
Discuss the importance of data quality and reliability for AI analytical tools.
Explain data pitfalls in AI analytical tools and the important role of the human analyst in assessing outputs and ensuring informed decision-making.
Use real-world examples of data issues and challenges to make lessons more tangible.
Gather insights from case studies and apply foundational data analytics concepts to assess and evaluate the reliability and AI analytical outputs.
Learning objectives
Discuss the importance of data quality and reliability for AI analytical tools.
Explain data pitfalls in AI analytical tools and the important role of the human analyst in assessing outputs and ensuring informed decision-making.
Use real-world examples of data issues and challenges to make lessons more tangible.
Gather insights from case studies and apply foundational data analytics concepts to assess and evaluate the reliability and AI analytical outputs.
Skills covered
Artificial Intelligence FoundationsData AnalysisArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
0. Introduction
- 01 - AI-powered data - Effective and reliable techniques
- 02 - What you should know
1. AI Analytics in the Business World
- 03 - Growth of AI usage and the changing labor market
- 04 - What is AI analytics
- 05 - AI analytics applications and benefits
2. Data Quality and Reliability in AI Analytics
- 06 - AI reliability and biases
- 07 - Data quality - The analyst's role
- 08 - Success and failures of AI analytics tools
3. Using Foundational Data Concepts to Assess AI Analytics Reliability
- 09 - Introduction of AI Analytics at Red30
- 10 - Case study 1 - On time deliveries
- 11 - Case study 2 - Business expansion
- 12 - Case study 3 - Business performance assessment
Continuing Your AI Analytics Learning Journey
- 13 - Next steps and additional resources