*Changes may apply
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
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
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)
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
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
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