Title: Data Analyst Syllabus - Google Reichman AI Tech School

URL Source: https://graitechschool.co.il/data-analyst-syllabus

Markdown Content:
*Changes may apply

![Image 1](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Mask-Group-9042.png)

![Image 2](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_130.jpg)

Module 1: Introduction to Statistics with AI

Pre-course requirement: Consumption of the mandatory podcast/blog reading list on AI fundamentals.

*   Introduction to Statistics & AI Agentic Prep in Sheets _(Setup of Gemini/Co-pilot assistants; learning to audit data rather than write manual formulas)_
*   Introduction to Probability
*   Central & Dispersion Measures: The Logic of Loss
*   Percentiles and Z-Scores: Feature Scaling for ML
*   Common Distributions & Monte Carlo Simulations with Gemini
*   Sampling Distribution & Hypothesis Testing: AI Benchmarking
*   Synthetic Data Generation & Interview Question Simulations _(Using AI to generate synthetic datasets to practice technical problem-solving and interview logic)_
*   One-Sample Test & Model Bias: Evaluating & Benchmarking AI Outputs _(Statistical approaches to LLM Evals, measuring Text-to-SQL accuracy, hallucination rates, and semantic drift via sampling)_
*   Confidence Intervals & Two-Samples Tests: Model Reliability
*   Correlations & ML Feature Selection
*   Linear Regression: The Neural Network Ancestor
*   Causal Inference: Measuring Real Lift
*   A/B Testing: 0 to 100 with AI Optimization

![Image 3](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_146-e1671960198187.jpg)

Module 2: Exploratory Data Analysis using SQL, BigQuery & Vibe Coding

*   Database Architecture & Cloud Infrastructure _(Self-directed research with AI co-pilot; findings shared in class)_
*   SQL Fundamentals, GCP Console, & Basic Querying _(Pre-read required: SQL functions with Gemini)_
*   Distinct,Where, Logical Operators, & Data Types
*   Advanced Functions & Multimodality _(Analyzing unstructured images, text, and video data inside BigQuery)_
*   Group By, Having, & Joins
*   Building Your SQL Co-pilot _(Setting up AI code assistants and prompt patterns for query generation)_
*   Data Modeling for LLMs & Semantic Layers (Understanding how table structure, column naming, metadata, business definitions, and relationships affect AI-generated SQL and analysis)
*   Union, Table Wildcards, & Exporting BQ Results
*   EDA via Vibe Coding _(Automated EDA, query generation, and automated ERD creation)_**_–_**
*   Subqueries & CASE Statements
*   Window Functions
*   Arrays, Structs, & Query Performance Optimization with AI
*   Unstructured Data Cleaning & Synthetic Case Generation
*   SQL Execution Flowcharts & AI Troubleshooting
*   Common Pitfalls & Case Studies
*   Vibe Coding for Data Visualization (Using AI to generate visualizations from BigQuery results and iterating based on analytical insights)
*   EDA Final Project Workshop _(Integrating Gemini workflow tools; 1 hour dedicated to AI co-pilot integration)_
*   Arrays, Structs, Query Performance
*   Data Cleaning
*   SQL Flowchart
*   Common Pitfalls and Case Studies Practice
*   My first AI Agent
*   Data Visualization and EDA Fundamentals (Vibe code your visualization)
*   EDA Project A-Z

![Image 4](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%A0%D7%99%D7%A1%D7%90-%D7%95%D7%A8%D7%95%D7%A2%D7%99-scaled.jpg)

Module 3: Python Workshop & Automation

*   Introduction to Python & AI-Assisted Scripting
*   Exploratory Data Analysis with Python & Pandas
*   Automated A/B Testing Pipelines
*   Agentic Architecture & Function Calling _(Using Gemini API and Python function calling to build autonomous, multi-step execution workflows_
*   Building AI-Powered Conversation Analysis with Gemini & Python _(Processing unstructured text and audio logs)_

![Image 5](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_146-e1671960198187.jpg)

Module 4: Modern Data Visualization & GenAI BI

*   Real Use Cases of BI in Product Organizations
*   How Generative AI is Transforming BI & Visual Storytelling
*   Conversation-Driven Dashboards: GenAI Chat inside BI platforms & Talking to Dashboards via Gemini Live
*   Advanced BI Integrations: MCPs (Data Studio + MCP Toolbox) &NotebookLM for Strategy Brainstorming

![Image 6](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%90%D7%A0%D7%92%D7%9C%D7%99-scaled.jpg)

Module 5: Product Analysis & Unstructured Data Apps

*   Programmatic Advertising & Metric Selection 
*   **Traditional & AI-Native Product Analytics AI Studio for Analytics: Building Rapid, Robust Unstructured Data Analysis Apps for calculating Retention, Churn, Funnels and more**
*   Scoping UX Research, Business KPIs, & “Aha!” Moments
*   Final Capstone Project Guidance & AI Workflow Review

![Image 7](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%92%D7%9C-%D7%95%D7%90%D7%93%D7%99%D7%A1%D7%9C%D7%90%D7%9D-scaled.jpg)

Module 6: Power Skills, Industry Mentorship & Personal Branding

*   Personal Branding & Content Creation: Immediate post-statistics workshop on creating technical content and sharing learnings on LinkedIn
*   Industry Mentorship: Direct matching with our network of active industry mentors for career guidance, resume reviews, and portfolio feedback
*   AI-Powered Interview Practice: Prompting frameworks to reverse-engineer interview questions for specific companies and target roles
*   Professional Execution: Public speaking, storytelling with data, stakeholder negotiation, and driving business decisions with VPs of Product and Finance

*Changes may apply