SQL for Data Science: A Comprehensive Guide

[akash-coded]

SQL for Data Science: A Comprehensive Guide

Part 1: Introduction and Basics

Objectives:

1. Understanding SQL and its role in data science.
2. Setting up an SQL environment for practice.
3. Familiarizing with Data Definition Language (DDL) and Data Manipulation Language (DML).

1.1: What is SQL?

SQL (Structured Query Language) is a domain-specific language used in programming for managing and querying data held in a relational database management system (RDBMS).

1.2: Why is SQL Important for Data Science?

For a data scientist, SQL allows:

• Retrieving data
• Pre-processing and cleaning data
• Analyzing and visualizing data
• Storing processed results

1.3: Setting up SQL Environment

We recommend using SQLite for practice as it’s lightweight, and you can focus on core SQL without worrying about vendor-specific features.

Part 2: Database Modeling & Table Creation

Scenario:

You are tasked with creating a database for a library. The library has books, authors, members, and borrowing transactions.

2.1: Tables Needed:

• Authors
• Books
• Members
• Borrowing Transactions

2.2: Creating Tables

1. Authors Table:

CREATE TABLE authors (
    author_id INTEGER PRIMARY KEY,
    first_name TEXT NOT NULL,
    last_name TEXT NOT NULL
);

2. Books Table:

CREATE TABLE books (
    book_id INTEGER PRIMARY KEY,
    title TEXT NOT NULL,
    author_id INTEGER,
    FOREIGN KEY (author_id) REFERENCES authors(author_id)
);

3. Members Table:

CREATE TABLE members (
    member_id INTEGER PRIMARY KEY,
    first_name TEXT NOT NULL,
    last_name TEXT NOT NULL,
    date_of_birth DATE
);

4. Borrowing Transactions Table:

CREATE TABLE transactions (
    transaction_id INTEGER PRIMARY KEY,
    member_id INTEGER,
    book_id INTEGER,
    borrow_date DATE,
    return_date DATE,
    FOREIGN KEY (member_id) REFERENCES members(member_id),
    FOREIGN KEY (book_id) REFERENCES books(book_id)
);

Part 3: CRUD Operations (DML Statements)

3.1: Inserting Data

To add a new author:

INSERT INTO authors (first_name, last_name)
VALUES ('George', 'Orwell');

3.2: Reading Data

To fetch all books by George Orwell:

SELECT b.title
FROM books b
JOIN authors a ON b.author_id = a.author_id
WHERE a.first_name = 'George' AND a.last_name = 'Orwell';

3.3: Updating Data

To update a member’s last name:

UPDATE members
SET last_name = 'Smith'
WHERE member_id = 1;

3.4: Deleting Data

To remove a book:

DELETE FROM books
WHERE title = '1984';

Part 4: ACID Properties

• Atomicity: This ensures that all operations within a transaction are completed successfully; otherwise, the transaction is aborted and rolled back.

• Consistency: Ensures that a transaction brings the database from one valid state to another.

• Isolation: Ensures that concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially.

• Durability: Once a transaction has been committed, it remains committed even in the case of a system failure.

Part 5: Wrap Up

• Always ensure that you follow best practices while designing databases.
• Be mindful of the ACID properties when working with transactions to maintain data integrity.

Homework:

1. Add additional attributes to the tables.
2. Design a new table for managing book genres.
3. Think about normalization and how you can optimize the design further.
4. Research and implement SQL constraints like UNIQUE, CHECK, etc.

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SQL for Data Science: Advanced Database Modeling and Relationships

Part 6: Advanced Tables and Relationships

Scenario:

Continuing with our library database, let's add more features like book genres, publishers, and book reviews.

6.1: New Tables:

• Genres
• Publishers
• Reviews

6.2: Creating Tables with Relationships:

1. Genres Table:

CREATE TABLE genres (
    genre_id INTEGER PRIMARY KEY,
    genre_name TEXT NOT NULL UNIQUE
);

2. Publishers Table:

CREATE TABLE publishers (
    publisher_id INTEGER PRIMARY KEY,
    publisher_name TEXT NOT NULL UNIQUE
);

3. Reviews Table:

CREATE TABLE reviews (
    review_id INTEGER PRIMARY KEY,
    book_id INTEGER,
    member_id INTEGER,
    review TEXT NOT NULL,
    rating INTEGER CHECK (rating BETWEEN 1 AND 5),
    FOREIGN KEY (book_id) REFERENCES books(book_id),
    FOREIGN KEY (member_id) REFERENCES members(member_id)
);

Part 7: Relationships and ER Diagrams

7.1: Types of Relationships:

1. One to One: e.g., Each book has one publisher. (Though in a real-world scenario, a publisher can have multiple books, for simplification let's assume one book is tied to one publisher).

2. One to Many: e.g., One genre can be associated with multiple books.

3. Many to Many: e.g., A book can have multiple authors, and an author can write multiple books. To handle this, we'll need a bridge table.

CREATE TABLE book_authors (
    book_id INTEGER,
    author_id INTEGER,
    PRIMARY KEY (book_id, author_id),
    FOREIGN KEY (book_id) REFERENCES books(book_id),
    FOREIGN KEY (author_id) REFERENCES authors(author_id)
);

7.2: ER Diagram:

To visualize these relationships, an Entity-Relationship (ER) diagram is often used. In the diagram:

• Tables are represented as boxes.
• Relationships are represented as lines connecting boxes.
• Cardinality (like One to Many) is represented as symbols (crow's feet or numbers).

A simple tool like draw.io or Lucidchart can be used to create this diagram.

Part 8: Normalization

Normalization is the process of efficiently organizing data in a database, and its main aim is to eliminate redundancy and ensure data integrity.

8.1: Types of Normal Forms:

1. First Normal Form (1NF):

   • Each table has a primary key.
   • All attributes are atomic (no repeating groups or arrays).
   • Values in each column are of the same domain.

2. Second Normal Form (2NF):

   • All requirements for 1NF must be met.
   • No partial dependencies of columns on the primary key.

3. Third Normal Form (3NF):

   • All requirements for 2NF must be met.
   • No transitive dependencies.

Our library database is already designed in 3NF.

Part 9: Complex Queries leveraging Relationships

9.1: Fetching Books of a particular Genre:

SELECT b.title
FROM books b
JOIN genres g ON b.genre_id = g.genre_id
WHERE g.genre_name = 'Science Fiction';

9.2: Fetching Books written by a specific Author:

SELECT b.title
FROM books b
JOIN book_authors ba ON b.book_id = ba.book_id
JOIN authors a ON ba.author_id = a.author_id
WHERE a.first_name = 'George' AND a.last_name = 'Orwell';

9.3: Finding the Average Rating of a Book:

SELECT b.title, AVG(r.rating) as avg_rating
FROM books b
JOIN reviews r ON b.book_id = r.book_id
GROUP BY b.title
HAVING avg_rating > 4;

Part 10: Wrap Up

• Relationships in SQL tables allow for creating intricate and versatile database designs.
• ER Diagrams help in visualizing and planning these relationships.
• Normalization ensures the database is efficient and maintainable.
• Complex queries can fetch, update, or delete data across multiple related tables.

Homework:

1. Design a table for managing book editions and relate it to publishers.
2. How would you handle a scenario where a book can belong to multiple genres? Create the necessary tables and relationships.
3. Try to de-normalize some tables and understand the trade-offs.

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SQL for Data Science: Dive into Advanced SQL Concepts

Part 11: Advanced SQL Concepts

11.1: Indexing

What is an Index?

Indexes are database structures that improve the speed of operations in a table. Indexes are used to quickly locate and access the data in a database table.

Implementing Indexing:

Consider our books table. If we frequently search for books based on their titles, it's beneficial to have an index on the title column.

CREATE INDEX idx_title
ON books (title);

11.2: Subqueries and Derived Tables

Subqueries, also known as inner queries or nested queries, are used to break down large problems into smaller, manageable pieces.

Using Subqueries:

Find the names of members who've reviewed a book:

SELECT first_name, last_name 
FROM members 
WHERE member_id IN 
    (SELECT DISTINCT member_id FROM reviews);

11.3: Joins and Advanced Relationships

Different Types of Joins:

1. INNER JOIN: Returns records that have matching values in both tables.
2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table, and the matched records from the right table.
3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table, and the matched records from the left table.
4. FULL JOIN (or FULL OUTER JOIN): Returns all records when there's a match in one of the tables.

11.4: Aggregation and Grouping

SQL provides aggregation functions like SUM, AVG, COUNT, etc., to perform operations on data.

Using Group By:

To find the number of books in each genre:

SELECT g.genre_name, COUNT(b.book_id) as number_of_books 
FROM books b
JOIN genres g ON b.genre_id = g.genre_id
GROUP BY g.genre_name;

11.5: Common Table Expressions (CTEs)

CTEs provide a way to create temporary result sets that can be easily referenced within a primary SELECT, INSERT, UPDATE, or DELETE statement.

Using CTEs:

To find the average rating for books:

WITH BookRatings AS (
    SELECT book_id, AVG(rating) as avg_rating 
    FROM reviews 
    GROUP BY book_id
)
SELECT b.title, br.avg_rating 
FROM books b
JOIN BookRatings br ON b.book_id = br.book_id;

11.6: Window Functions

Window functions perform a calculation across a set of table rows related to the current row.

Using Window Functions:

To find the cumulative number of book reviews:

SELECT book_id, 
       review, 
       rating, 
       SUM(rating) OVER(ORDER BY review_id) as cumulative_rating 
FROM reviews;

Part 12: Advanced SQL Practices

12.1: Optimizing Queries:

• Avoid using SELECT *.
• Be cautious with subqueries; sometimes joins can be more efficient.
• Regularly check and optimize database indexes.

12.2: Transactions:

Transactions ensure a series of operations succeed or fail as a single unit. It's essential for maintaining the integrity of a database.

BEGIN;
UPDATE members SET last_name = 'Smith' WHERE member_id = 1;
UPDATE books SET title = 'New Title' WHERE book_id = 1;
COMMIT;

Part 13: Wrap Up

• Advanced SQL requires a good understanding of the foundational concepts, then branching out into specialized areas.
• With frequent practice and real-world applications, mastery over these advanced concepts becomes attainable.

Homework:

1. Explore advanced topics like PIVOT, UNPIVOT, and database-specific features like JSON handling in SQL.
2. Design a complex query that involves multiple joins, subqueries, and aggregation. Try optimizing it.
3. Understand the use of stored procedures, triggers, and user-defined functions in your SQL vendor of choice.

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SQL for Data Science: Real-world Applications and Scenarios

Part 14: Practical Use Cases

To truly master SQL, one must practice with real-world scenarios. Here are some applications and their respective SQL challenges.

14.1: E-commerce Database

Scenario:

You've been hired as a data analyst for an e-commerce company. The company has tables for customers, products, orders, order_details, and shipments.

Task:

1. Find out the top 5 best-selling products in the last month.
2. Identify customers who haven't made a purchase in the last six months.
3. Calculate the average shipping time.

Sample Query for Task 1:

SELECT p.product_name, COUNT(o.order_id) as sales_count
FROM products p
JOIN order_details od ON p.product_id = od.product_id
JOIN orders o ON od.order_id = o.order_id
WHERE o.order_date BETWEEN DATE('2023-01-01') AND DATE('2023-01-31')
GROUP BY p.product_name
ORDER BY sales_count DESC
LIMIT 5;

14.2: Hospital Database

Scenario:

You're assisting a hospital with their data analytics. They have tables for patients, doctors, appointments, and treatments.

Task:

1. Find doctors with the highest number of appointments in the last week.
2. List patients who've visited more than 3 times in the past month.
3. Identify treatments with a success rate below 50%.

Sample Query for Task 2:

SELECT pa.patient_name, COUNT(a.appointment_id) as visit_count
FROM patients pa
JOIN appointments a ON pa.patient_id = a.patient_id
WHERE a.appointment_date BETWEEN DATE('2023-02-01') AND DATE('2023-02-28')
GROUP BY pa.patient_name
HAVING visit_count > 3;

14.3: School Database

Scenario:

A school wants to use data analytics for their academic planning. They have tables for students, teachers, courses, and grades.

Task:

1. Find the average grade for each course.
2. List students who have failed in more than 2 subjects.
3. Determine teachers whose students have an average grade above 90%.

Sample Query for Task 1:

SELECT c.course_name, AVG(g.grade) as avg_grade
FROM courses c
JOIN grades g ON c.course_id = g.course_id
GROUP BY c.course_name;

Part 15: Conclusion and Further Steps

15.1: Analyzing Real-world Datasets

A plethora of datasets are available online. Websites like Kaggle, UCI Machine Learning Repository, and data.gov offer datasets on various topics. Import these datasets into an SQL environment and practice your querying skills on them.

15.2: SQL in Data Science

SQL plays a critical role in data preprocessing for machine learning. Often, data scientists have to query databases to gather training data. Mastering SQL ensures you can retrieve data efficiently and in the exact format required.

15.3: Continual Learning

Databases and SQL standards continue to evolve. Newer databases support features like handling JSON, arrays, or geospatial data. Staying updated on these changes ensures you remain proficient.

Homework:

1. Design a database schema for a real-world application of your choice and practice queries on it.
2. Attempt SQL challenges on platforms like LeetCode or HackerRank.
3. Integrate SQL with programming languages like Python using libraries like sqlite3 or SQLAlchemy to automate data retrieval processes.

Remember, proficiency in SQL, as with any other skill, is a blend of structured learning, consistent practice, and application in diverse scenarios.