This catalog focuses on the practical application of Python and SQL, the core tools for data scientists and engineers. It evaluates the ability to transform raw, unstructured data into clean, high-quality inputs suitable for machine learning and business analysis.

Data Extraction and SQL Proficiency

Relational Operations: Tests the ability to retrieve data from databases using complex joins, filtering, and record selection.
Advanced Querying: Evaluates the use of aggregations, grouping, and window functions to extract insights from structured tables.
Data Retrieval: Focuses on combining multiple data sources to create a unified view for analysis.

Python for Data Manipulation

Library Expertise: Expects proficiency in pandas and NumPy for transforming and reshaping datasets.
Transformation Tasks: Includes pivoting tables, calculating statistics, and applying transformations across columns.
Vectorization: Emphasizes using efficient vectorized operations over slow, manual loops.

Performance and Scalability

Large-Scale Handling: Requires an understanding of how to optimize queries for datasets containing millions or billions of rows.
Resource Efficiency: Focuses on selecting the most computationally efficient methods to minimize processing time in production.

Data Cleaning and Preprocessing

Quality Control: Covers handling missing (null) values, removing duplicates, and normalizing text data.
Feature Preparation: Includes scaling numerical features and preparing data structures for downstream machine learning models.

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Python, SQL & Data Manipulation

Assessment Details

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