WARIN.ME
Teaching Tools & Innovation
Reflective teaching tools, laboratories, and experimental systems for understanding learners, educators, subject matter, and educational decisions.
DATA SYSTEMS · PATH 01
Build the data foundation before building the model
Follow data from transactions to reusable features, analytical storage, orchestration and incremental state.
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 →MACHINE LEARNING · PATH 02
Understand the formula, then the SQL, then the production boundary
This path separates algorithm mechanics, database execution, recommendation, serving and continuous learning.
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 →FEATURE ENGINEERING · PATH 03
Move from reusable structure to specialised domain evidence
Study cost, reuse, representation and domain interpretation before treating a computed value as evidence.
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 →ALGORITHMS & DISTRIBUTED SYSTEMS · PATH 04
Watch state move, converge and become explainable
Interactive laboratories for networks, parallel search, indexing and explainable system behaviour.
Distributed Bellman–Ford
Inspect router inboxes, message exchange and convergence after cost changes.
Continue lesson →Distributed Spanning Tree
Observe root election, BPDU comparison and redundant-port decisions.
Continue lesson →Parallel BFS Knight’s Tour
Study level-synchronous frontiers and feasible messages across simulated cores.
Continue lesson →Scrabble Engine Capstone
Connect data structures, scoring, search, databases, networking and explainable heuristics.
Continue lesson →PSO Search Space Lab
Trace particles across generations, inspect inertia, cognitive and social vectors, and explore four objective landscapes.
Continue lesson →DE Search Space Lab
Build a mutant from vector differences, inspect crossover coordinate by coordinate, and accept or reject each trial solution.
Continue lesson →APPLIED AI & BIOINFORMATICS · PATH 05
Move from transparent evidence to adaptive models
Applied laboratories connecting published methods, local evidence, ensemble learning and reversible updates.
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 →TEACHING DESIGN & REFLECTION · PATH 06
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 →