Data Analytics Cookbook

Practical Data Analysis with BigQuery & Pandas

Learn how to solve the same problems using two approaches: SQL and Python🚀 Get Started in 5 Minutes

Build Dashboards Like These!

📊 E-Commerce Analytics Dashboard

After completing this curriculum, you’ll be able to build a comprehensive business intelligence dashboard covering sales analysis, customer segmentation, marketing performance, and churn prediction.

📊 KPI Cards🗺️ World Map👥 RFM Analysis📈 Cohort Retention🚨 Churn Prediction🧪 A/B Testing📉 Hypothesis Testing📐 Regression Analysis

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💰 Finance Analytics Dashboard

The core of financial data analysis! Learn analytical techniques used in financial industry practice, from stock price forecasting to portfolio optimization and risk analysis.

📈 Stock Analysis💼 Portfolio⚠️ Risk Analysis📅 Time Series Forecasting

💰 View Finance Demo

💎 Premium Starter Kit

Includes fully executable Jupyter Notebooks, exercise solutions, and practical utility functions. Learn advanced topics such as memory optimization, Copy-on-Write, and large-scale data processing.

📓 Jupyter Notebooks✅ Exercise Solutions🔧 Practical Utilities📊 85 Interview Questions

💎 View Interview Questions

Choose Your Learning Track

🗄️ SQL Track

SQL-based data analysis using BigQuery. Ideal for large-scale data processing and analysis in cloud environments.

Run directly in BigQuery Console
Serverless large-scale processing
Standard SQL syntax

Start SQL Track →

🐼 Pandas Track

Local data analysis using Python Pandas. Ideal for flexible data manipulation and building analysis pipelines.

Start immediately with CSV files
Leverage the rich Python ecosystem
Flexible data transformations

Start Pandas Track →

Common Sections

📊 Visualization

Data visualization with Matplotlib, Seaborn, and PlotlyGo →

📈 Statistical Analysis

Hypothesis testing, regression analysis, A/B testingGo →

🤖 Machine Learning

Clustering, predictive models, time series analysisGo →

Datasets Used

This Cookbook is based on Google BigQuery’s thelook_ecommerce public dataset.

Table	Description	Key Columns
`src_orders`	Order information	order_id, user_id, created_at, status
`src_order_items`	Order items	order_id, product_id, sale_price
`src_products`	Product information	product_id, category, brand, retail_price
`src_users`	Customer information	user_id, age, gender, country
`src_events`	Web events	session_id, user_id, created_at
`events_augmented`	Session data (augmented)	channel_key, device_key, landing_page
`cs_tickets_dummy`	CS tickets	ticket_id, issue_type, satisfaction_score
`mkt_campaigns_dummy`	Marketing campaigns	campaign_id, channel_key, budget

📦 Download Sample Data (Free)

Quick Start

🚀 Get Started in 5 Minutes

BigQuery Setup or Local Environment Setup
Choose your track (SQL or Pandas)
Follow the first recipe

Difficulty Guide

Level	Description
Beginner	Basic syntax, simple aggregations
Intermediate	Joins, subqueries, window functions
Advanced	Complex analysis, optimization, ML integration