Advanced SQL  Queries Essential for Data Science Interviews

10

Combine data from multiple tables using various types of JOINs while filtering or manipulating data with subqueries for detailed analysis.

1

Complex Joins and Subqueries

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Window Functions for Analytical Tasks

Apply functions over a set of rows relative to the current row to perform complex calculations like ranking or running totals.

2

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CTE and Recursive Queries

Define temporary result sets using the WITH clause for clearer, more reusable SQL queries, including handling hierarchical data with recursion. 

3

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PIVOT Tables for Data Transformation

4

Transform rows into columns to summarize data, making it easier to read & analyze distinct values from one column across multiple columns.

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Dynamic SQL for Flexible Queries

Create adaptable SQL queries that dynamically change based on variable conditions or inputs, enhancing query flexibility and reusability.

5

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Utilize functions like COUNT(), SUM(), AVG() with GROUP BY and HAVING for detailed data aggregation and analysis.

Advanced Aggregate Functions

6

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Search for specific patterns within data using LIKE, SIMILAR TO, or regular expressions, essential for data cleaning and text analysis.

Pattern Matching

7

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Temporal Data and Time Series Analysis

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8

Analyze time-series data by aggregating, comparing, and manipulating dates & times, essential for trend analysis and forecasting.

This comprehensive guide will help you prepare for all aspects of the interview process.  Don't miss out on your next opportunity!

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