The Data Science Nerds

 In Part 1, we explored the foundational SQL command types—DDL, DML, DQL, DCL, and TCL. Now, in Part 2, it’s time to go beyond the basics and dive into how SQL is actually used in real-world data science workflows. This part focuses on writing smarter queries, combining data from multiple sources, and extracting meaningful insights from raw datasets. 1. Advanced SELECT Queries The SELECT statement is much more powerful than just retrieving columns. With additional clauses, you can filter, sort, and refine your data to answer specific questions. Key Clauses: WHERE → Filter records AND , OR , NOT → Combine conditions BETWEEN → Range filtering IN → Match multiple values LIKE → Pattern matching ORDER BY → Sort results LIMIT → Restrict output size Example: SELECT name, salary FROM employees WHERE salary BETWEEN 30000 AND 70000 AND department = 'IT' ORDER BY salary DESC LIMIT 5; 👉 This query helps identify top-paid employees in a specific department within a salary range. 2....

What is a Database ?  A database is a container that stores related data in an organized way. In MySQL , a database holds one or more tables. Think of it like:  Folder analogy :  A database is like a folder. Each table is a file inside that folder. The rows in the table are like the content inside each file.  Excel analogy : A database is like an Excel workbook. Each table is a separate sheet inside that workbook. Each row in the table is like a row in Excel. ---------------------------------------------------------------------------------------------------------------------- Data Types in SQL  : 1. INT : Stores integer type, used for whole numbers 2. VARCHAR(100) : Stores variable length string, upto 100 characters. 3. Date : Stores date values 4. BOOLEAN : Stores TRUE or FALSE 5. ENUM : A string object with a value chosen from a list of permitted values. eg. gender                     ENUM('Male', '...

What is Data Science? Have you ever wondered how Netflix recommends your next favourite show? Or how Zomato knows which restaurant you might like? The answer is Data Science — and Python is the tool that makes it all happen. Data Science is simply about finding useful patterns and insights from large amounts of data. And Python is the most popular programming language to do that — because it is easy to learn, free to use, and super powerful. Why Should You Learn Python for Data Science? Here are 3 simple reasons: •        Easy to read and write: Python code looks almost like plain English. Even beginners can pick it up quickly. •        Huge community: Millions of developers use Python. You will always find help online. •        Lots of ready-made tools: Libraries like NumPy, Pandas, and Scikit-learn do the heavy lifting for you. Your 7-Step Roadmap to Learn Python for Data Science ...