WARIN.ME
Teaching Tools & Innovation
Reflective teaching tools, laboratories, and experimental systems for understanding learners, educators, subject matter, and educational decisions.
COURSE COLLECTIONS
Start from the course you are learning.
Each laboratory below has a clear home in one course.
COURSE 07
Data Science and Data Engineering
Data systems, machine learning and feature engineering
Views for Data Engineering
Begin with reusable semantics, governance and the interface between application and analytics teams.
Continue lesson →Bulk UPDATE versus Rebuild
See when row versions, logs, indexes and random writes make read–transform–new table a better candidate.
Continue lesson →Operational data becomes features
See why row-oriented transactions are necessary—but expensive for repeated analytical computation.
Continue lesson →OLAP Feature Engineering
Move recurring windows and aggregates into analytical structures designed for reuse.
Continue lesson →Feature Store Laboratory
Version, share and serve features while controlling training-serving skew.
Continue lesson →Airflow Data Workflow Lab
Orchestrate dependencies, retries, schedules and observable data pipelines.
Continue lesson →Data Systems Architecture Labs
Compare OLTP, embedded OLAP, columnar OLAP, materialization and IVM under one data contract.
Continue lesson →PostgreSQL to DuckDB Analytical Bridge
Export bounded monthly snapshots, practise dual write, and compare equivalent analytical queries across both engines.
Continue lesson →Three-tool Screen Scraping Laboratory
Learn selectors, static HTML parsing and browser automation step by step through one trusted-source task.
Continue lesson →From ML formulas to MariaDB SQL
Derive kNN, Naive Bayes, regression, k-means and Z-score before translating operators into SQL.
Continue lesson →Machine Learning with Pure SQL
Open the black box across six model families and compare the DuckDB edition with MariaDB.
Continue lesson →Recommendation Systems with SQL
Build popularity, co-occurrence, collaborative, content, temporal and hybrid ranking.
Continue lesson →NextGenEd Data Foundation
Design a questionnaire, versioned consent, participant-controlled CRUD, simulation assumptions and ERD before introducing a model.
Continue lesson →Declarative Machine Learning
Distinguish SQL-native algorithms from SQL-callable ML services and commercial platforms.
Continue lesson →In-Database ML with MADlib
Use a mature library after understanding what its SQL interface hides.
Continue lesson →Declarative ML Live Lab
Compare embedded and server OLAP execution boundaries with the same analytical intent.
Continue lesson →Three Common Ways of Online ML
Separate online inference, online features and true online learning, including delayed labels and drift.
Continue lesson →Composite Features and Shared Computation
Find expensive shared structure and avoid recomputing it across dependent features.
Continue lesson →RFM Analysis and Churn Signals
Select a customer, derive RFM snapshots and interpret multi-period slopes without mistaking a heuristic for probability.
Continue lesson →Representation as Features
Compare PCA, SVD, NMF, projections, embeddings and model-derived scores with leakage control.
Continue lesson →Bioinformatics Feature Laboratory
Trace sequence and structural features, their complexity and the value of storing reusable results.
Continue lesson →Financial Feature Laboratory
Interpret trend, momentum, volatility, volume, liquidity and risk without turning indicators into advice.
Continue lesson →Domain Feature Engineering Atlas
Compare feature meaning, computation cost and interpretation across twelve specialist domains.
Continue lesson →Deep Learning Changes the Boundary
Understand what representation learning automates—and what information it cannot recover.
Continue lesson →COURSE 01
Network Computing / Computer Networks
Routing and loop-free network topology
COURSE 04
Parallel and Distributed Systems
Parallel search and communication between workers
COURSE 05
Data Structures and Algorithms
Indexes, search, scoring and explainable heuristics
COURSE 06
Artificial Intelligence and Machine Learning
Applied AI, bioinformatics and reversible online learning
PlantLoc PSO Laboratory
Optimise weighted classifier evidence for plant protein localisation.
Continue lesson →HumanLoc PSO Laboratory
Study multilabel localisation and transparent ensemble voting.
Continue lesson →Online Psychosegment Laboratory
Learn, unlearn and relabel with a complete revision audit.
Continue lesson →Fiction Gene Laboratory
Inspect story certificates and reversible online learning across model versions.
Continue lesson →Admission Reaction Laboratory
Recover the published six-reaction classifier, then learn, unlearn, restore and relabel evidence with a revision audit.
Continue lesson →CROSS-COURSE COLLECTION
Use across courses and teaching contexts.
These tools support teaching decisions, reflection and assessment rather than one subject area.
GENERAL TEACHING TOOLS
Turn teaching judgment into inspectable evidence
Tools for educational beliefs, teaching decisions and multi-objective assessment.
EBI · Educational Belief Explorer
Surface beliefs behind pedagogical choices.
Continue lesson →TDI · Teaching Decision Inventory
Examine how educators decide in concrete teaching situations.
Continue lesson →Multi-objective Learning Assessment
Inspect trade-offs across Data Science, Data Engineering and Online ML outcomes.
Continue lesson →