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

📦 Data Preparation

Section 3 of the Data Analytics hub — 25 of 25 lessons.

Sourcing, structuring, and organising data before analysis: types, formats, databases, and sampling.

↑ Back to the Data Analytics hub

🧬 Data Types & Structure

How data is generated, its types and formats, and structured vs tabular shapes.

• 001 · How Data Is Generated and Collected — where data comes from: first/second/third-party sources and how it is produced

• 002 · Choosing the Right Data to Collect — relevance, coverage, and cost: deciding what data a question actually needs

• 003 · Understanding Data Types and Data Formats — nominal, ordinal, discrete, continuous — and the file formats data travels in

• 004 · Structured Data and Data Models — structured vs semi- vs unstructured, and the models that give data its shape

• 005 · Data Types in Spreadsheets — text, number, date, boolean — why a spreadsheet cares what type a cell holds

• 006 · Data Tables (Tabular Data) — the row-and-column table: the workhorse shape of analysable data

• 007 · Wide Data vs. Long Data — two tidy layouts of the same data, and when each one serves

⚖️ Bias & Data Ethics

Recognising bias, judging sources with ROCCC, and the ethics of data use.

• 008 · Understanding Bias in Data Analysis — what bias is, where it hides, and why unbiased data is the goal not the default

• 009 · Sampling Bias and Unbiased Data — when the sample misrepresents the population — and how random sampling guards against it

• 010 · Common Types of Data Bias — sampling, observer, interpretation, and confirmation bias, with how each distorts

• 011 · Identifying Good Data Sources (ROCCC Framework) — Reliable, Original, Comprehensive, Current, Cited — the marks of trustworthy data

• 012 · Identifying Bad Data Sources (When Data Does Not ROCCC) — reading ROCCC in reverse to spot the data you should not trust

• 013 · Data Ethics in Data Analysis — ownership, transaction transparency, consent, and currency — the ethics of using data

• 014 · Data Privacy in Data Ethics — protecting people’s information: what privacy requires of an analyst

• 015 · Open Data and Openness in Data Ethics — when data should be freely available — and the tension with privacy

🗄️ Databases & Data Sources

Relational databases, metadata and governance, and accessing data.

• 016 · Databases and Relational Database Concepts — tables, keys, and relationships: how organisational data is really stored

• 017 · Metadata in Databases — data about data: descriptive, structural, and administrative context that makes data usable

• 018 · Metadata Repositories and Data Governance — where metadata is catalogued, and the governance that keeps data trustworthy

• 019 · Accessing Data: Internal and External Sources — getting to the data: what lives inside the organisation versus outside

🔢 Spreadsheets, SQL & Organization

Importing, sorting, querying with SQL, organising, and securing data.

• 020 · Importing Data into Spreadsheets — getting external data into a sheet cleanly — and the type traps to watch

• 021 · Sorting and Filtering Data in Spreadsheets — ordering and narrowing rows: the two most-used moves for making data legible

• 022 · BigQuery Account Types — sandbox, free tier, and paid — how to access a cloud data warehouse for practice

• 023 · Querying Data with SQL — SELECT, FROM, WHERE: retrieving exactly the rows and columns a question needs

• 024 · Organizing Data for Personal and Work Projects — folder, file, and naming conventions that keep a data project findable and safe

• 025 · Data Security in Spreadsheets — protecting a shared sheet: access control, protected ranges, and safe sharing

🔤 Every lesson, A–Z index

🔠 Open the full alphabetical index

Accessing Data: Internal and External Sources BigQuery Account Types Choosing the Right Data to Collect Common Types of Data Bias Data Ethics in Data Analysis Data Privacy in Data Ethics Data Security in Spreadsheets Data Tables (Tabular Data) Data Types in Spreadsheets Databases and Relational Database Concepts How Data Is Generated and Collected Identifying Bad Data Sources (When Data Does Not ROCCC) Identifying Good Data Sources (ROCCC Framework)

Importing Data into Spreadsheets Metadata in Databases Metadata Repositories and Data Governance Open Data and Openness in Data Ethics Organizing Data for Personal and Work Projects Querying Data with SQL Sampling Bias and Unbiased Data Sorting and Filtering Data in Spreadsheets Structured Data and Data Models Understanding Bias in Data Analysis Understanding Data Types and Data Formats Wide Data vs. Long Data

Tags: purpose: reference topic: data analytics