Data

Steps to clean data

Remove duplicate records, update outdated emails, standardize phone number formatting, and use data validation tools.

Risks of manual data cleaning

Inaccurate reporting and decision-making; automated tools reduce human error and ensure consistency.

Techniques for correcting errors

Use find-and-replace for misspellings, apply conditional formatting, and enforce data validation rules.

Strategies for clean data

Implement automated data updates, schedule periodic audits, and ensure data entry training for employees.

Tools for duplicate identification

Use Excel’s 'Remove Duplicates' function, conditional formatting, or SQL’s DISTINCT function.

Functions for data organization

CONCATENATE merges text; VLOOKUP retrieves corresponding data from another table.

Benefits of filtering & sorting

Filtering isolates relevant data, while sorting arranges data logically for easier analysis.

Improving data accuracy

Automating data entry with templates, validation rules, and macros reduces errors.

When to use SQL

SQL is better for large datasets and complex queries; spreadsheets work best for small-scale manual analysis.

Basic SQL query structure

SELECT * FROM orders WHERE product_category = 'Electronics' AND purchase_date BETWEEN '2024-01-01' AND '2024-12-31';

SQL functions for data retrieval

JOIN connects tables; VLOOKUP finds related records; UNION merges datasets.

Using CAST in SQL

SELECT CAST(customer_id AS INT) FROM customers; converts text IDs into numbers for proper analysis.

Advantages over spreadsheets

Databases manage large, linked datasets efficiently, whereas spreadsheets lack relational capabilities.

Importance of primary & foreign keys

Primary keys uniquely identify records; foreign keys link related data across tables.

Preventing integrity issues

Enforce data constraints, use referential integrity rules, and validate data relationships.