Causal Inference for Data Scientists – Potential Outcomes, DiD and Observational Studies
📋 KEY INSIGHTSCorrelation is not causation — a model that predicts churn well does not tell you what intervention will reduce churn. Causal inference...
Anomaly Detection in Machine Learning – Isolation Forest, Autoencoders and Statistical Methods
📋 KEY INSIGHTSAnomaly detection (also called outlier detection) is an unsupervised or semi-supervised task — labels for anomalies are rare or nonexistent in most...
Transformers and Attention Mechanism Explained – BERT, GPT and Self-Attention from Scratch
📋 KEY INSIGHTSThe Transformer architecture (Vaswani et al., 2017) replaced RNNs for sequence modelling by using self-attention — every token can directly attend to...
Data Science Career Guide 2026 – Skills, Portfolio and Interview Preparation
📋 KEY INSIGHTSData science roles in 2026 are increasingly specialised — job titles now distinguish ML Engineers, Data Scientists, Analytics Engineers, MLOps Engineers, and...
Feature Selection Techniques – Filter, Wrapper and Embedded Methods for Machine Learning
📋 KEY INSIGHTSFeature selection reduces dimensionality, speeds up training, reduces overfitting, and can improve model generalisation — but the right method depends on the...
