🧽 Data Cleaning & Preparation
0.5.dev0+git.20260720.c8f33de - July 20, 2026 16:01 UTC

🧽 Data Cleaning & Preparation

Section 4 of the Data Analytics hub — 32 of 32 lessons.

Finding and fixing dirty data: missing values, duplicates, outliers, and validation for trustworthy inputs.

↑ Back to the Data Analytics hub

🧱 Data Integrity & Sampling

Why clean data matters, integrity risks, sampling, power, and margin of error.

• 001 · The Importance of Clean Data — why clean data is the non-negotiable foundation every analysis stands on

• 002 · Data Integrity and Its Risks in Data Analysis — keeping data accurate and consistent through its life — and what threatens it

• 003 · Aligning Data with Business Objectives — checking that the data you have actually fits the question you must answer

• 004 · Handling Insufficient Data in Data Analysis — recognising when there is not enough data, and the honest options when there isn’t

• 005 · Population, Sample Size, and Random Sampling — the whole vs. the part: population, sample, and how to sample fairly

• 006 · Statistical Power in Data Analysis — the chance of detecting a real effect when there is one — and why 0.8 is the norm

• 007 · Sample Size and Data Integrity — how much data is enough: confidence, precision, and the cost of too little

• 008 · Margin of Error — the plus-or-minus around a sample estimate, and how to read it honestly

🧹 Dirty Data & Spreadsheet Cleaning

Recognising dirty data and cleaning it with spreadsheet tools and functions.

• 009 · Dirty Data vs. Clean Data — the concrete contrast: what makes data dirty, and what clean looks like beside it

• 010 · The Importance of Clean Data (revisited) — the business cost of dirty data — why cleaning is worth the time it takes

• 011 · Common Issues in Dirty Data — the recurring defects — duplicates, missing, inconsistent, wrong type, outliers

• 012 · Data Cleaning with Spreadsheets — the practical spreadsheet cleaning workflow, defect by defect

• 013 · Cleaning and Merging Multiple Datasets — combining data from several sources cleanly — matching keys, reconciling formats

• 014 · Spreadsheet Tools for Data Cleaning — the built-in cleaning toolkit: dedupe, split, find/replace, validation, formatting

• 015 · Using Spreadsheet Functions for Data Cleaning — TRIM, CLEAN, UPPER, SUBSTITUTE, VALUE and friends — cleaning by formula

• 016 · Viewing Data Differently for More Effective Data Cleaning — sort, filter, pivot, conditional formatting as lenses that reveal hidden defects

• 017 · Data Mapping and the Big Picture of Clean Data — matching fields between sources so merged data stays coherent — cleaning’s big picture

🐬 Cleaning with SQL

SQL from first queries to CAST, COALESCE, and advanced cleaning functions.

• 018 · Introduction to SQL — why SQL matters for cleaning: transformations that scale and stay reproducible

• 019 · Spreadsheets vs. SQL — when to reach for a sheet and when for a query — strengths, limits, and the handoff

• 020 · Core SQL Queries for Data Cleaning and Analysis — SELECT, WHERE, DISTINCT, GROUP BY — the query patterns that clean and summarise

• 021 · Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables — DISTINCT, GROUP BY, and string functions to dedupe and standardise text in SQL

• 022 · Using CAST to Clean and Format Data in SQL — converting a value from one data type to another — SQL’s type-fixing tool

• 023 · Advanced SQL Functions for Data Cleaning — CASE, SUBSTR, TRIM, and pattern tools for the harder cleaning jobs

• 024 · COALESCE — returning the first non-null value — SQL’s standard handler for missing data

✅ Verification, Documentation & Next Steps

Verifying and reporting cleaning work, documenting changes, and moving forward.

• 025 · Verifying and Reporting Data Integrity — confirming data is sound after cleaning — and telling stakeholders it is

• 026 · Verifying Data-Cleaning Efforts — checking that each cleaning step did what it should, and nothing it shouldn’t

• 027 · Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors — concrete checks — counts, distinct values, ranges — that catch recurring defects

• 028 · Documenting Data-Cleaning Changes — recording what was changed and why, so cleaning is reproducible and reviewable

• 029 · Reporting Data-Cleaning Results — communicating what cleaning found and fixed, so stakeholders can trust the data

• 030 · Using Feedback from Data Cleaning to Improve Data Quality — closing the loop: feeding cleaning lessons back to fix problems at the source

• 031 · Refining a Resume for Data Analytics Roles — shaping a resume to show analytical skill — cleaning your own professional data

• 032 · Exploring Data Analyst Job Opportunities — where analyst roles are, and how to read them against your skills and goals

🔤 Every lesson, A–Z index

🔠 Open the full alphabetical index

Advanced SQL Functions for Data Cleaning Aligning Data with Business Objectives Cleaning and Merging Multiple Datasets Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables COALESCE Common Issues in Dirty Data Core SQL Queries for Data Cleaning and Analysis Data Cleaning with Spreadsheets Data Integrity and Its Risks in Data Analysis Data Mapping and the Big Picture of Clean Data Dirty Data vs. Clean Data Documenting Data-Cleaning Changes Exploring Data Analyst Job Opportunities Handling Insufficient Data in Data Analysis Introduction to SQL Margin of Error

Population, Sample Size, and Random Sampling Refining a Resume for Data Analytics Roles Reporting Data-Cleaning Results Sample Size and Data Integrity Spreadsheet Tools for Data Cleaning Spreadsheets vs. SQL Statistical Power in Data Analysis The Importance of Clean Data The Importance of Clean Data (revisited) Using CAST to Clean and Format Data in SQL Using Feedback from Data Cleaning to Improve Data Quality Using Spreadsheet Functions for Data Cleaning Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors Verifying and Reporting Data Integrity Verifying Data-Cleaning Efforts Viewing Data Differently for More Effective Data Cleaning

Tags: purpose: reference topic: data analytics