A comprehensive understanding of the key stages data goes through, from collection to reporting.

• Data Collection, Storage, Cleaning and Preparation
• Data Modeling, Analysis, Reporting and Visualization
• Hands-on project from the very first lesson

Basic SQL commands for data analysis, with emphasis on correct writing habits, effective syntax use, and business thinking through intensive practice.

• 8 datasets from various industries
•  Approximately 300 exercises
• Focus on translating business questions into SQL queries

A deep dive into the art and science of data visualization. Transform raw data into compelling visual stories through interactive demonstrations and real-world case studies.

•  Choosing the right chart for the business question
•  Building visualizations that communicate insights, not just colors

A unique module developed by Ram Kedem, focusing on complex queries and real-world problems. Based on the book "Advanced SQL: Practical Techniques and Use Cases" and a decade of experience.

•  15+ different datasets and 200+ exercises
•  Challenging job interview questions
•  Advanced techniques based on real user problems

In-depth knowledge and practical tools for working with Tableau, from data preparation to advanced visualizations and interactive dashboards.

•  Connect to various data sources (Excel, databases, cloud services)
•  Clean and process data, handle missing values, remove duplicates
•  Build hierarchies and analytical models
•  LOD Expressions, custom formulas, quick table calculations
•  Interactive dashboards with navigation, filtering, and dynamic exploration
•  Parameters for dynamic calculations and custom reports
•  Advanced: dual axes, box plots, central tendency measures

This module focuses on the new way data analysts work in an AI-assisted environment.

The goal is not to turn participants into Python developers, but to teach them how to work effectively with tools like Claude Code: how to guide AI, understand the code it generates, validate the results, identify mistakes, and use AI to accelerate data analysis without losing professional control.

Participants will learn the Python and Pandas fundamentals required to read, supervise, and improve AI-generated analysis, while building practical workflows that connect business questions, data, code, validation, and insights.

• How AI tools and large language models fit into modern data analysis workflows.
• Working with Claude Code for data analysis tasks.
• Using AI to generate Python and Pandas code.
• Python and Pandas fundamentals for analysts: reading files, DataFrames, filtering, sorting, calculations, groupby, joins, missing values, and basic transformations.
• Reading and reviewing AI-generated Python/Pandas code.
• Validating results with sanity checks, row counts, sample comparisons, and business logic.
• Using AI for exploratory data analysis, visualization, and insight generation.
• Working with Git and GitHub to manage versions and safely review AI-generated changes.
• Building context files and lightweight semantic layers that help AI understand the data domain.
• Introduction to Skills, MCP, and data agents.

Entirely dedicated to analyzing business use-cases and extracting insights. Understand how to interpret and analyze different cases, applying data analysis techniques to uncover patterns and trends.

•  Various business scenarios with complexity analysis
•  Advanced Microsoft Excel techniques
•  PivotTables, conditional statements, lookup functions
•  Statistical functions and dynamic array formulas
•  50 hours of analytical thinking development

Delivered by experienced interviewers in data analysis, designed to prepare participants for the job market.

•  How to prepare effectively for a data analysis job interview
•  Crafting an effective resume and LinkedIn profile
•  Interview stages: preparation, conduct, closing, follow-up
•  Individual 1:1 feedback meetings with each participant