SQL Basics - Data Science Part #1

A simple to learn the basic of SQL

Author

Shailesh Shakya
March 10, 2025

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In today’s newsletter

  1. SQL Quick Notes for Beginners

  2. Complete Basic ROADMAP download (PDF)

SQL Quick Notes for Beginners

1. Understanding Data in SQL

  • Structured Data: Organized in tables like a spreadsheet.

  • Relational Database: Data is stored in tables (relations) with columns (attributes) and rows (tuples).

2. SQL Data Types

  • Numeric: INT, BIGINT, DECIMAL, FLOAT

  • Strings: CHAR(n), VARCHAR(n), TEXT

  • Date & Time: DATE, DATETIME, TIMESTAMP

  • Boolean: BOOLEAN (TRUE/FALSE)

  • Other: JSON, BLOB, UUID

3. Basic SQL Queries

Retrieve all data:

SELECT * FROM books;

Retrieve specific columns:

SELECT title, price FROM books;

Filter data with conditions:

SELECT * FROM books WHERE price > 10 AND in_stock = TRUE;

Sorting:

SELECT title, price FROM books ORDER BY price DESC;

Limiting results (Pagination):

SELECT title FROM books LIMIT 5 OFFSET 10;

4. Keys in SQL (Database Design)

  • Primary Key: Uniquely identifies rows (e.g., book_id in books table).

  • Foreign Key: Links one table to another (e.g., author_id in books referring to authors).

  • Unique Key: Ensures unique values (e.g., ISBN).

  • Composite Key: A key using multiple columns (e.g., order_id, product_id in an order_items table).

5. SQL Relationships

  • One-to-Many (1:N): A customer can place multiple orders.

  • Many-to-Many (M:N): A book can belong to multiple categories, and a category can contain multiple books.

6. Filtering & Logical Conditions

  • Comparison Operators: =, !=, <, >, <=, >=

  • Logical Operators: AND, OR, NOT

  • BETWEEN: price BETWEEN 10 AND 20

  • LIKE: title LIKE 'Harry Potter%' (Finds titles starting with 'Harry Potter')

  • IN: author IN ('J.K. Rowling', 'George Orwell')

Beginner Level (No Prior Experience Needed)

  1. SQL for Data Science – UC Davis

    • Great for learning SQL in a data analysis context.

    • Uses SQLite.

  2. Databases and SQL for Data Science with Python – IBM

    • Covers SQL + Python integration.

    • Uses IBM Db2 (industry-level SQL).

  3. Querying Data with Transact-SQL – Microsoft

    • Best for Microsoft SQL Server users.

  4. Introduction to Querying Databases

    • Covers CRUD operations (SELECT, INSERT, UPDATE, DELETE).

Intermediate Level (Deepen Your Skills)

  1. Data Analysis with SQL – Google

    • Part of Google’s Data Analytics Certificate.

    • Uses BigQuery.

  2. Advanced SQL for Data Scientists – LinkedIn Learning

    • Covers window functions, CTEs, and optimization.

  3. Analyzing Big Data with SQL – Cloudera

    • Focuses on SQL for large datasets (Hive, Impala).

  4. Data Science: Querying with SQL – HarvardX

    • From Harvard, designed for data scientists.

Advanced Level (Database Management & Performance)

  1. Database Design & Basic SQL in PostgreSQL – University of Michigan

    • Covers database structure and normalization.

  2. PostgreSQL for Everybody Specialization – University of Michigan

    • Comprehensive PostgreSQL learning path.

  3. NoSQL Systems – UC San Diego

    • Covers MongoDB, Cassandra, and document-based databases.

  4. Data Management & Visualization – Wesleyan University

    • SQL + data visualization skills.

Suggested Learning Path

✔ Beginner: Start with UC Davis or IBM SQL for Data Science.
✔ Intermediate: Learn SQL for BigQuery or Advanced SQL for Data Scientists.
✔ Advanced: Choose PostgreSQL or NoSQL courses based on your career needs.

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Here is the complete Roadmap

The Basic of SQL + List of courses .pdf1.53 MB • PDF File

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Shailesh