AI is changing how organizations work with data – not only how data is analyzed, but also how it is extracted, cleaned, enriched, reported, and turned into business decisions.
This instructor-led course teaches participants how to use advanced AI tools to work smarter and faster with data, while maintaining human oversight, critical thinking, and responsible use.
Throughout the course, participants build their own “digital team” of AI assistants to support real data work. They learn to use AI for data cleaning and analysis, research and enrichment, recurring reporting, business communication, and workflow automation.
The emphasis is on practical business outcomes and repeatable processes, not coding. Participants learn not only how to get answers from AI, but also how to validate those answers, reduce the risk of incorrect reasoning, and maintain appropriate human control over AI-supported workflows.
By enrolling in this course, you’ll learn how to turn AI into a practical data partner - helping you analyze information faster, automate recurring work, uncover insights, and communicate results with confidence.
This workshop is ideal for:
By the end of this workshop, participants will:
Head of Data Analysis Programs
Talia has over 10 years of experience in the IT industry, having held the roles of Product Manager, Project Manager, and Data Solutions Manager, where she specialized in data analysis in addition to her other responsibilities.
She is a lecturer at Bar Ilan University, serving as the director of the BI Developer course and also teaching in the Data Science course. In addition, she is an undergraduate lecturer at Ono and the academic director and leading lecturer of the data analyst track at the G-Academy.
Talia is an expert in various development tools, including SQL, Python, Excel, and uniPaaS.
Talia holds a B.A. in Computer Science with Economics and Management from the Academic College of Tel Aviv, Yaffo.
Session 1: How Generative AI Works, and How to Work With It
Participants build a practical foundation for working effectively with generative AI and understanding its role in data-related work.
Session 2: Data Quality and AI-Assisted Validation
Participants learn how AI can support data-quality processes while maintaining the human validation needed for reliable results.
Session 3: Extracting Structured Data from Documents and Images
Learn how AI can help transform information contained in documents and images into structured data that can be used for analysis.
Session 4: AI-Assisted Data Cleaning with Human Oversight
Use AI to accelerate data cleaning while applying human review and validation to maintain accuracy and reliability.
Session 5: Classification, Matching and Reconciliation with AI
Apply AI to practical data tasks involving classification, matching, and reconciliation.
Session 6: Turning Business Needs into Data Questions
Learn how to translate business needs into clear data questions that guide effective analysis and decision-making.
Session 7: Exploring Data and Validating AI-Generated Insights
Use AI to explore data and identify insights while critically validating AI-generated conclusions.
Session 8: Combining Data and Documents with AI
Learn to synthesize information from structured data and documents to develop richer business context and insights.
Session 9: Research and Data Enrichment with AI
Use AI-supported research to enrich existing data and add relevant context to analysis.
Session 10: Building Recurring Reports with AI and Google Sheets
Learn how to create more efficient and consistent recurring reporting processes using AI and Google Sheets.
Session 11: Building and Testing a Custom AI Assistant
Build an AI assistant with a defined role, expected output format, and validation rules, and test its performance against real data tasks.
Session 12: Building a Basic AI Agent
Move beyond individual AI prompts by building an AI agent designed to perform defined tasks and support practical workflows.
Session 13: Building a No-Code Agentic Workflow
Learn how to connect tasks into a no-code agentic workflow and automate repeatable work across tools.
Session 14: Ethics, Privacy and Responsible AI
Understand key considerations around privacy, security, responsible AI use, and the additional risks introduced by AI agents and automated workflows.
Session 15: Data Storytelling and Communicating AI-Supported Results
Turn analysis into clear, structured, executive-ready communication that explains insights, context, and business implications.
Session 16: Final Project Presentations and Demo Day
Participants bring together what they have learned throughout the course and present their final projects, demonstrating practical applications of AI-supported data work.
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Wawiwa bridges the tech skills gap by reskilling people for tech professions in high demand. There are millions of tech vacancies and not enough tech professionals with the relevant knowledge and skills to fill them. What the industry needs of employees is not taught in long academic degrees. Wawiwa helps partners around the world to reskill, and upskill people for tech jobs through local tech training centers or programs. The company utilizes a proven training methodology, cutting-edge content, digital platforms for learning and assessment, and strong industry relations, to deliver training programs that result in higher employability and graduate satisfaction. This, in turn, also creates a strong training brand and a sustainable business for Wawiwa’s partners.
