A 30-lesson degree-level course covering information needs, data representation, retrieval architecture, indexing, SQL, OLTP, OLAP, NoSQL, cloud-scale data, AI-assisted search and responsible data practice.
Lesson 1 · Retrieval foundations
Introduce information retrieval as the design of systems that connect users with useful information, whether the user is searching directly, browsing categories or refining an unclear need.
Lesson 2 · Data forms
Explain the main forms of data encountered in storage and retrieval systems and why the choice of structure changes validation, search, analytics and maintenance.
Lesson 3 · Data representation
Show how text is represented for storage and retrieval, with emphasis on Unicode, UTF-8, code points, encodings and normalisation issues in multilingual collections.
Lesson 4 · Semi-structured data
Develop XML as a semi-structured representation for documents and records, including elements, attributes, document trees, validation, DTD and schema design.
Lesson 5 · Data representation
Explain how image, audio and video content is represented for storage and retrieval, including sampling, compression, codecs, bit rate and metadata extraction.
Lesson 6 · Metadata and semantics
Teach metadata as structured description that makes resources findable, manageable and trustworthy, using examples such as Dublin Core, provenance and controlled vocabularies.
Lesson 7 · Retrieval models
Introduce retrieval models that decide which items match a query and how strongly, including Boolean retrieval, vector space thinking, TF-IDF and BM25.
Lesson 8 · Retrieval architecture
Build a system-level view of information retrieval using crawler or ingestion, parser, normaliser, indexer, query processor, ranker and user interface components.
Lesson 9 · Web acquisition
Explain how crawlers and spiders discover web content, manage frontiers, respect robots.txt and politeness, canonicalise URLs and build searchable collections.
Lesson 10 · Text processing
Teach the text-processing steps that turn documents and queries into comparable terms, including tokenisation, stopword handling, case folding and normalisation.
Lesson 11 · Text processing
Explain how keyword selection, stemming and lemmatisation affect recall and precision, including common stemming errors and domain vocabulary issues.
Lesson 12 · Indexing
Build the core indexing structure used by text retrieval systems: the inverted index, including terms, posting lists, document frequency and positional information.
Lesson 13 · Evaluation
Teach how retrieval systems are evaluated using relevance judgements, precision, recall, F1 and confusion-matrix thinking, while recognising limits of test collections.
Lesson 14 · Query processing
Explain how systems interpret, rewrite and expand queries using normalisation, spelling correction, synonyms, thesauri and pseudo-relevance feedback while managing query drift.
Lesson 15 · Semantics and linked data
Introduce Semantic Web and linked data principles, including URI identifiers, RDF triples, SPARQL-style querying and the relationship between semantics and query expansion.
Lesson 16 · Browsing and classification
Explain faceted classification as a way to support browsing and filtering through independent dimensions such as topic, date, format, creator and location.
Lesson 17 · Relational data
Introduce the relational model, tables, keys, constraints and foundational SQL SELECT queries for structured information retrieval.
Lesson 18 · SQL retrieval
Extend SQL retrieval using JOIN, WHERE, GROUP BY, ORDER BY and aggregation for structured search, reporting and evidence extraction.
Lesson 19 · OLTP modelling
Teach operational database design for transactional systems, including entities, relationships, functional dependency, 1NF, 2NF and 3NF.
Lesson 20 · Operational systems
Explain transaction processing, ACID properties, integrity constraints and isolation issues in systems that support reliable operational storage and retrieval.
Lesson 21 · Analytics systems
Contrast operational normalised systems with analytical systems that use denormalisation, data warehouses, ETL and business intelligence reporting.
Lesson 22 · OLAP modelling
Teach dimensional modelling for analytical systems, including fact tables, dimension tables, star schema, snowflake schema and OLAP-style slice, dice, drill-down and roll-up.
Lesson 23 · Analytics delivery
Explain how analytical data is prepared and delivered through pipelines, data marts, dashboards, KPIs, quality checks and refresh schedules.
Lesson 24 · NoSQL data
Introduce NoSQL data models including key-value, document store, column-family and graph database approaches, with retrieval and scalability trade-offs.
Lesson 25 · Cloud and scale
Explain cloud storage and distributed data processing concepts including object storage, partitioning, replication, eventual consistency and scalable query execution.
Lesson 26 · Big Data
Analyse Big Data through volume, velocity, variety, veracity and value, connecting these challenges to storage, retrieval, processing and governance choices.
Lesson 27 · AI-assisted retrieval
Introduce AI and Large Language Models in information access, including embeddings, semantic search, retrieval-augmented generation, hallucination risk and evaluation.
Lesson 28 · Exemplar applications
Use web search engines and publishing archives as exemplar applications that combine crawling, metadata, indexing, ranking, rights management and user-facing retrieval.
Lesson 29 · Data ethics
Teach responsible data practice across storage and retrieval, including privacy, consent, bias, transparency, security, governance and lifecycle accountability.
Lesson 30 · Capstone design
Synthesize the course by designing an end-to-end information storage and retrieval system from requirements through architecture, indexing, querying, analytics, AI support, evaluation and ethics.
