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Data Science
Streaming Machine Learning – Real-Time Features, Kafka, Flink and Feature Stores
📋 KEY INSIGHTSStreaming machine learning extends the standard batch ML paradigm to settings where data arrives continuously and model predictions or features must be available with low latency —...
Data Science
Vector Databases and Semantic Search – Pinecone, Qdrant, FAISS and ANN Algorithms
📋 KEY INSIGHTSVector databases store, index, and query high-dimensional embedding vectors — the dense numerical representations produced by embedding models (BERT, Sentence-BERT, CLIP, OpenAI text-embedding-3) — using approximate nearest-neighbour...
Data Science
Uplift Modeling and Causal Machine Learning – T-Learner, X-Learner and CATE Estimation
📋 KEY INSIGHTSUplift modeling (also called causal machine learning or heterogeneous treatment effect estimation) estimates the individual-level causal effect of a treatment — a marketing campaign, a drug, a...
Data Science
LLM Fine-Tuning – LoRA, QLoRA and Parameter-Efficient Fine-Tuning (PEFT)
📋 KEY INSIGHTSFull fine-tuning of large language models — updating all parameters of a 7B+ parameter model — requires dozens of GB of GPU VRAM and is prohibitively expensive...
Data Science
Tabular Deep Learning – TabNet, FT-Transformer and SAINT Explained
📋 KEY INSIGHTSGradient-boosted trees (XGBoost, LightGBM, CatBoost) have dominated tabular data benchmarks for a decade, and the common wisdom that "deep learning does not work on tabular data" has...
Data Science
Multi-Armed Bandits and Online Learning – Explore-Exploit, UCB and Thompson Sampling
📋 KEY INSIGHTSThe multi-armed bandit problem formalises the explore-exploit dilemma: an agent repeatedly chooses between K options (arms), observes a reward, and must balance exploiting the arm that has...
Data Science
MLflow and Experiment Tracking – Model Registry, Runs and Lifecycle Management
📋 KEY INSIGHTSMLflow is the de facto standard open-source platform for the ML experiment lifecycle: tracking experiment parameters, metrics, and artefacts; managing the model registry; and serving registered models...
Data Science
Time Series Deep Learning – Temporal Fusion Transformer, N-BEATS and PatchTST
📋 KEY INSIGHTSClassical statistical time series models (ARIMA, ETS, Prophet) make explicit parametric assumptions about trend, seasonality, and noise that work well for single, smooth series with stable dynamics....
Data Science
Modern Data Architecture – Data Mesh, Lakehouse and Medallion Architecture
📋 KEY INSIGHTSThe data architecture landscape has undergone a fundamental shift in the past five years: from monolithic data warehouses owned by centralised data teams, to decentralised, domain-oriented data...
Data Science
Prompt Engineering for Data Scientists – In-Context Learning, Chain-of-Thought and RAG
📋 KEY INSIGHTSPrompt engineering is the practice of systematically designing, structuring, and optimising the inputs to large language models to elicit accurate, consistent, and useful outputs. As LLMs become...
Data Science
Imbalanced Learning – Oversampling, SMOTE, Cost-Sensitive Learning and Threshold Adjustment
📋 KEY INSIGHTSClass imbalance — where one or more classes are dramatically underrepresented in the training data — is the default condition in many of the most important ML...
Data Science
Self-Supervised and Contrastive Learning – SimCLR, BYOL, DINO and Masked Autoencoders
📋 KEY INSIGHTSSelf-supervised learning (SSL) creates supervision signals from the data itself — masking tokens and predicting them, contrasting augmented views of the same image, predicting future frames from...
Data Science
Model Compression – Pruning, Quantisation and Knowledge Distillation
📋 KEY INSIGHTSModel compression reduces the size and computational cost of ML models — enabling deployment on edge devices, reducing inference latency and cost, and making large models practical...
Data Science
Federated Learning – Privacy-Preserving Distributed Machine Learning
📋 KEY INSIGHTSFederated learning trains machine learning models across multiple devices or organisations without centralising the raw data — only model updates (gradients or weights) are shared. This enables...
Data Science
Knowledge Graphs for Data Scientists – Graph Databases, Ontologies and Link Prediction
📋 KEY INSIGHTSA knowledge graph represents information as a network of entities (nodes) and relationships (edges) — (subject, predicate, object) triples such as (Albert_Einstein, bornIn, Ulm) or (Drug_X, inhibits,...
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Supervised vs Unsupervised Learning: 5 Key Differences with Examples (2026)
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The Basics of Automated Data Processing: Methods and Tools
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