SQL Queries for Data Science | AIML End-to-End Session 57
Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?
Welcome to Session 57 of our End-to-End AIML series! In this session, we focus on writing SQL queries that are essential for Data Science workflows. SQL is a powerful language for querying and analyzing data from relational databases, making it an indispensable skill for data scientists.
What You'll Learn:
Key SQL Queries for Data Science:
SELECT and Filtering Data: Master the SELECT statement to retrieve data, and use WHERE clauses to filter records based on specific conditions.
Aggregating Data: Learn how to use GROUP BY to aggregate data and perform operations like COUNT, SUM, AVG, MIN, and MAX.
JOIN Operations: Combine data from multiple tables using various types of JOINs (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN) to build complex datasets.
Data Manipulation with SQL: Learn how to use INSERT, UPDATE, and DELETE to manage and manipulate data.
Subqueries: Write subqueries to create more complex SQL queries for advanced data retrieval.
Window Functions: Explore window functions like ROW_NUMBER, RANK, and OVER() to analyze data across specific partitions.
Common Table Expressions (CTEs): Simplify complex queries using CTEs for better readability and maintainability.
Real-World Use Cases: See how SQL queries are used in data cleaning, data exploration, and feature engineering for machine learning models.
Hands-On Coding Example: Follow along as we demonstrate SQL queries using a sample dataset, showing practical applications in data science projects.
Best Practices for SQL in Data Science: Learn tips for writing efficient SQL queries, optimizing performance, and handling large datasets effectively.
By the end of this session, you’ll be confident in writing SQL queries tailored to data science tasks, enabling you to manage and analyze large datasets for meaningful insights.
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