Module 1 · Unit 2 · 9 tutorials · Week 2
Data and Information
Data types, the distinction between data and information, metadata, and the characteristics of big data. The vocabulary of working with data.
Overview
Data is not the same as information. This unit draws the distinction carefully, because the difference determines what you can do with a given dataset and what tools are appropriate for working with it.
You will learn to classify data as structured, semi-structured or unstructured, to describe the metadata that makes a dataset interpretable, and to characterise big data along the dimensions that matter in practice.
Learning outcomes
By the end of this unit you will be able to
- Distinguish data from information, knowledge and wisdom.
- Classify data as structured, semi-structured or unstructured.
- Describe a dataset using its metadata.
- Characterise big data along volume, velocity, variety and related dimensions.
- Choose appropriate tools for each data type.
Tutorial sequence
Data vs Information
Raw facts, processed facts and the transition from one to the other.
t-02Types of Data
Nominal, ordinal, interval and ratio scales, and their implications.
t-03Structured Data
Tabular data, relational databases and fixed schemas.
t-04Semi-Structured Data
JSON, XML, YAML — self-describing formats with flexible structure.
t-05Unstructured Data
Text, images, audio and video, and the tools used to analyse them.
t-06Metadata
Data about data: schema, provenance, units and quality information.
t-07Big Data Characteristics
Volume, velocity, variety, veracity and value.
t-08Case Study
A real dataset examined across each of the data types introduced.
t-09Unit Summary
Consolidated key points and a forward look to Unit 3.
Key concepts
- Data
- Raw, unprocessed facts and observations with no inherent meaning on their own.
- Information
- Data that has been processed, organised or interpreted so that it has meaning in a given context.
- Structured data
- Data organised in a fixed, predictable format, typically rows and columns with a defined schema.
- Semi-structured data
- Data with some organisational structure but no rigid schema, such as JSON or XML.
- Unstructured data
- Data with no predefined structure, including free text, images, audio and video.
- Metadata
- Information that describes a dataset: its structure, provenance, units, quality and usage conditions.
Assessment alignment
How this unit feeds the assignments
Assignment 1 (Data Collection Project) requires you to classify the dataset you collect and to produce a full metadata record describing it. The vocabulary in this unit is the vocabulary you will use in that submission.