தமிழில் – Data Science Candidates #sqlfordataengineer #faangpreparation #interviewquestions #sql

We are given Candidates table which has candidate_id and skill. the combination of candidate_id, skill is the primary key for this table.
Each row includes candidate_id and skill.

We are asked to write a query to find the candidates best suited for a Data Scientist position. The candidate must be proficient in Python, Tableau, and PostgreSQL.
Return the result table ordered by candidate_id in ascending order.

In this video, we delve into three distinct methods for identifying candidates best suited for a Data Scientist role based on their proficiency in Python, Tableau, and PostgreSQL.

*** Approach 1: Brute Force Method ***
This approach adopts a straightforward strategy, employing subqueries to individually verify the presence of each required skill. By selecting distinct candidate_ids where each skill is present and intersecting the results, we obtain the desired candidates sorted in ascending order.

*** Approach 2: Correlated Subquery ***
In this method, we utilize a correlated subquery to dynamically count the distinct skills for each candidate_id. By selecting candidate_ids where the count of distinct skills equals 3, we identify candidates proficient in all three required skills.

*** Approach 3: Aggregate Format ***
This approach focuses on grouping the data by candidate_id and filtering the groups to only include those with all three required skills using the HAVING clause. By grouping candidates based on their skills and ensuring each group contains exactly three distinct skills, we obtain the desired candidates sorted in ascending order.

Each approach offers its unique advantages and considerations, and through this video, we explore their effectiveness and efficiency in identifying suitable candidates for the Data Scientist position.

For a comprehensive understanding of these SQL methodologies and their application, please refer to this explanatory video.