Data Hygiene & Quality Control

Discipline: Data Analyst | Module: Module 1: Foundational Spreadsheet Architecture & Advanced Analytics | Estimated Study Time: 7 Hours

Welcome to Data Hygiene & Quality Control. This topic delivers foundational and advanced concepts designed for production engineering and real-world workflows.

Key Learning Objectives

1. Text Parsing (LEFT, RIGHT, MID, LEN, FIND, SEARCH, TEXTJOIN)
2. Sanitization (TRIM, CLEAN, UPPER, LOWER, PROPER, REPLACE, SUBSTITUTE)
3. Validation Arrays (Custom Regex, Data Validation Lists, Dropdowns)

Detailed Curriculum Breakdown

Text Parsing (LEFT, RIGHT, MID, LEN, FIND, SEARCH, TEXTJOIN)

Explore the fundamental principles, real-world patterns, and best practices for Text Parsing (LEFT, RIGHT, MID, LEN, FIND, SEARCH, TEXTJOIN). Practice hands-on implementations to master these concepts.

// Code Example: Text Parsing (LEFT, RIGHT, MID, LEN, FIND, SEARCH, TEXTJOIN)
// Implement verified patterns for production use
console.log("Mastering Text Parsing (LEFT, RIGHT, MID, LEN, FIND, SEARCH, TEXTJOIN)");

Sanitization (TRIM, CLEAN, UPPER, LOWER, PROPER, REPLACE, SUBSTITUTE)

Explore the fundamental principles, real-world patterns, and best practices for Sanitization (TRIM, CLEAN, UPPER, LOWER, PROPER, REPLACE, SUBSTITUTE). Practice hands-on implementations to master these concepts.

// Code Example: Sanitization (TRIM, CLEAN, UPPER, LOWER, PROPER, REPLACE, SUBSTITUTE)
// Implement verified patterns for production use
console.log("Mastering Sanitization (TRIM, CLEAN, UPPER, LOWER, PROPER, REPLACE, SUBSTITUTE)");

Validation Arrays (Custom Regex, Data Validation Lists, Dropdowns)

Explore the fundamental principles, real-world patterns, and best practices for Validation Arrays (Custom Regex, Data Validation Lists, Dropdowns). Practice hands-on implementations to master these concepts.

// Code Example: Validation Arrays (Custom Regex, Data Validation Lists, Dropdowns)
// Implement verified patterns for production use
console.log("Mastering Validation Arrays (Custom Regex, Data Validation Lists, Dropdowns)");

Practical Application & Exercises

1. Architecture Review: Evaluate how Data Hygiene & Quality Control integrates with upstream and downstream systems.
2. Implementation Challenge: Build a functional prototype demonstrating each of the subtopics.
3. Validation & Testing: Verify performance and error handling under edge-case scenarios.

Summary Checklist

• Studied foundational architecture for Data Hygiene & Quality Control
• Completed practical coding challenge
• Validated edge cases and error handling routines