AI for Data Analysts – Enhancing Analytical
Description
AI reshapes the landscape of data and business, staying relevant and leading the market is more critical than ever.
This workshop is specifically designed to help you integrate advanced AI tools into your daily data analysis workflows while maintaining a unique advantage as human professionals.
AI is revolutionizing data analytics, offering tools that enhance efficiency and accuracy. This course equips data analysts with the knowledge and skills needed to remain competitive in a fast-evolving market by leveraging AI while preserving the irreplaceable qualities of human expertise.
Intended audience
This course is ideal for data analysts, business intelligence professionals, and decision-makers who want to stay ahead of the curve by mastering AI integration in data workflows.
Topics
The Human Advantage Over Machines – “A Breathing Thought”
Will AI replace data analysts within five years?
Advantages of AI and the business challenges it addresses.
What is an Agent? How do LLMs (Large Language Models) become more effective when paired with in-house data?
Limitations of AI.
The unique human advantage: "Breathing Thought."
Enhancing data analysts' efficiency with AI to stay relevant in tomorrow’s job market.
Core AI Applications for Data Analysts
Defining business objectives/KPIs.
Identifying required variables and data sources.
Creating datasets and panels.
Performing data analysis and deriving insights.
Preparing deliverables and presenting results to business stakeholders.
How AI tools integrate into each component of the data analyst's workflow.
Use cases for Agents in insurance processes such as underwriting and claims.
Navigating Challenges: Applying AI Tools in Analytical Business Environments
Overview of the AI principles endorsed by the State of Israel.
Risks of improper AI usage (e.g., AI hallucinations, frozen data).
Addressing privacy, data security, ethics, and bias concerns.
"Safety guidelines" for working with AI tools.
Cloud vs. on-premises environments for AI integration.
Architectural and process flow challenges in organizational AI adoption.
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