Data-Analysis

Lesson 5 - Subqueries & CTEs Theory + Practice | StreamDB - Indian Streaming Platform Before You Start This lesson introduces StreamDB - a Hotstar-style streaming platform. Run setup.sql to create and populate StreamDB Verify you see 6 tables with the correct row counts Connect to streamdb in pgAdmin before running any query The StreamDB schema: users shows genres ┌────┬───────┬──────┐ ┌────┬────────┬──────────┐ ┌────┬──────────┐ │ id │ name │ plan │ │ id │ title │is_premium│ │ id │ name │ └────┴───────┴──────┘ └────┴────────┴──────────┘ └────┴──────────┘ │ │ │ │ │ show_genres │ │ │ ┌──────────────┘ ┌────────────┘ │ │ show_id | genre_id │ │ └──────────────────────────┘ (many-to-many) │ subscriptions watch_history ┌────┬─────────┬──────────────┐ ┌────┬─────────┬─────────┬─────────┐ │ id │ user_id │ plan │ │ id │ user_id │ show_id │ minutes │ │ │ │ started_on │ │ │ │ watched │ watched │ │ │ │ expires_on │ └────┴─────────┴─────────┴─────────┘ └────┴─────────┴──────────────┘ What you will learn: ...

Lesson 6 - Modifying Data & Schema Changes Theory + Practice | ShopDB Before You Start This lesson uses ShopDB. Connect to shopdb in pgAdmin before running any query. Important: This lesson modifies real data. Some queries will permanently change your database. The section on transactions shows you how to do this safely - read Part 7 before running anything you are unsure about. Quick data check: SELECT 'customers' AS t, COUNT(*) FROM customers UNION ALL SELECT 'products', COUNT(*) FROM products UNION ALL SELECT 'orders', COUNT(*) FROM orders UNION ALL SELECT 'order_items', COUNT(*) FROM order_items; -- Expected: 20, 25, 15, 30 What you will learn: ...

Pandas Practice Sheet - IPL Dataset A complete two-table exploration: from match summaries to ball-by-ball intelligence Why IPL Is the Best Dataset for Pandas After Zomato The Zomato dataset taught you how to clean a single messy table. The IPL dataset teaches you something more important: how to work with two related tables and merge them to answer questions neither table can answer alone. This is how data exists in every real company - not as one giant flat file, but as a set of related tables that you must join, aggregate across levels, and reason about carefully. ...

Pandas Practice Sheet - Titanic Dataset 20 Questions from Zero to Advanced, with Full Solutions Getting the Dataset The Titanic dataset is hosted publicly and can be loaded directly into pandas - no download needed. Direct CSV URL (no login required): https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv Load it in one line: import pandas as pd df = pd.read_csv("https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv") Or load via seaborn (also free, slightly different column names): import seaborn as sns df = sns.load_dataset("titanic") Always run df.head() and df.info() before answering any question - knowing your data is half the battle. ...

Pandas Practice Sheet - Zomato Bangalore Dataset A complete guided exploration: from raw messy data to real business insights Why This Dataset? The Titanic dataset is clean, small, and predictable. The Zomato Bangalore dataset is the opposite - it is genuinely messy in the way real-world data is messy. Ratings stored as strings like "3.8/5". Costs formatted as "450" with comma-separators ("1,200"). The cuisines column has multiple cuisines packed into one cell separated by commas. Several columns have inconsistent values, duplicates, and missing data scattered across them. ...

## Pratice easy, medium and hard questions on cricket dataset. <!DOCTYPE html> SQL Practice: CricketDB - Codeverra Codeverra · SQL Practice SQL Practice Set CricketDB 12 questions on a cricket dataset - players, matches, and ball-by-ball performances. Easy to hard. Every question has a one-line approach, a clean solution, and alternatives where they exist. ...

## Practice SQL questions on the shopping dataset. <!DOCTYPE html> SQL Practice: ShopDB - Codeverra Codeverra · SQL Practice SQL Practice Set ShopDB 12 questions on an Indian e-commerce dataset - customers, products, orders, and line items. Easy to hard. Every question includes the business context, a one-line approach, a clean query, and alternatives where they exist. ...