ML Model Monitoring

Data Analysis: Analytical Methods

The analytical-methods stage of the data-analysis discipline — the techniques that turn a prepared dataset into findings, predictions, and decisions. This is the “do the analysis” layer that sits between data acquisition and communication: cleaning and exploring data, fitting statistical and ML models, running experiments and causal estimates, forecasting, detecting anomalies, engineering features, and the specialized modeling disciplines (CLV, survival, Bayesian, conformal/UQ, causal discovery, prescriptive optimization, recommenders).

It does not re-derive the underlying probability and inference theory (that is da-1-foundations-theory) and it does not own the surrounding process, platform, or communication stages — see the cross-hub note below.

How to use this hub

This hub consolidates 16 analytical-methods sub-skills as on-demand references under references/. Treat the routing table as an index, not as the answer:

  1. Identify which method the task calls for.
  2. Find the matching row below.
  3. Read the listed references/<name>.md before giving a deep answer — the table lines are deliberately shallow and exist only to route. For multi-method tasks (e.g. EDA → feature engineering → modeling → evaluation), read each relevant reference in sequence.

Sub-skill routing table

This hub absorbs 16 former standalone skills as on-demand reference files. When a task matches a row, Read the listed references/ file before answering — do not rely on this table alone for depth.

Sub-topic When to load Reference file
da-4-data-cleaning-preparation Cleaning and preparing raw data — missing values, outliers, types, dedup, normalization, the disciplined transform into analysis-ready data references/da-4-data-cleaning-preparation.md
da-5-exploratory-data-analysis Exploratory Data Analysis — the Tukey-rooted discipline of looking at data with summaries and graphics to surface structure and check assumptions references/da-5-exploratory-data-analysis.md
da-6-statistical-modeling Statistical (inference-first) modeling — linear regression (OLS, Gauss-Markov, diagnostics), logistic regression, GLMs, mixed/hierarchical models references/da-6-statistical-modeling.md
da-7-machine-learning Machine learning — the ML taxonomy (supervised / unsupervised / reinforcement), model selection, cross-validation, regularization, evaluation metrics references/da-7-machine-learning.md
da-12-ab-testing-causal-inference A/B testing and causal inference — randomized experiments (sample size, power), DiD, IV, regression discontinuity, propensity-score methods references/da-12-ab-testing-causal-inference.md
da-15-forecasting Forecasting and time-series modeling — ARIMA/ETS, Prophet, backtesting, going deeper than the time-series foundations references/da-15-forecasting.md
da-16-anomaly-detection Anomaly / outlier detection — statistical (z-score, IQR), distance/density, isolation forest, and time-series anomaly methods for the working analyst references/da-16-anomaly-detection.md
da-17-feature-engineering-and-feature-stores Feature engineering taxonomy (numerical transforms, encoding, datetime, interactions, target encoding), automated FE, and feature-store operations references/da-17-feature-engineering-and-feature-stores.md
da-23-customer-lifetime-value Probabilistic / statistical Customer Lifetime Value (CLV) modeling — BG/NBD, Gamma-Gamma, and related expected-value methods references/da-23-customer-lifetime-value.md
da-24-survival-analysis Survival / time-to-event modeling — Kaplan-Meier, Cox proportional hazards, censoring, a discipline distinct from ordinary regression references/da-24-survival-analysis.md
da-25-bayesian-data-analysis Applied Bayesian data analysis and probabilistic-programming workflow — priors, posteriors, MCMC, posterior predictive checks references/da-25-bayesian-data-analysis.md
da-31-conformal-prediction-uq Conformal prediction and distribution-free uncertainty quantification — prediction sets/intervals with finite-sample coverage guarantees references/da-31-conformal-prediction-uq.md
da-32-causal-discovery Causal discovery / structure learning — learning the causal DAG itself from data (constraint-, score-, and functional-based methods) references/da-32-causal-discovery.md
da-33-prescriptive-analytics Prescriptive analytics / decision science — turning predictions and data into optimized decisions via optimization, decision rules, and simulation references/da-33-prescriptive-analytics.md
da-38-recommender-systems-and-ranking Recommender systems and learning-to-rank — collaborative/content filtering, matrix factorization, ranking models and evaluation references/da-38-recommender-systems-and-ranking.md
da-42-ml-model-monitoring Monitoring ML models in production — drift taxonomy (data/concept/prediction/feature) and detection tests (PSI, KS, KL/JS, Wasserstein), streaming detectors (ADWIN/DDM/Page-Hinkley), performance estimation without labels (NannyML CBPE/DLE), train-serve skew, slice/fairness drift, retraining triggers, and the tooling landscape (Evidently, Arize, Fiddler, WhyLabs, Alibi Detect, SageMaker, Vertex) references/da-42-ml-model-monitoring.md

Cross-hub note

This hub is the methods stage of a six-hub data-analytics family. Route elsewhere when the task is not “run the analysis”:

When a request spans stages, start in the hub that owns the decision the user is currently making and hand off explicitly.

Cross-hub map — where every data-analytics topic lives

This family is split across these hubs. If a task’s deep material is not in this hub’s Sub-skill routing table, it is a reference file under a sibling hub below — activate that hub or Read its references/<name>.md directly. Every former standalone skill in this family is now a reference under one of these hubs (nothing was deleted).

Hub Owns Example reference files
da-1-foundations-theory Data Analysis Foundations & Theory (hub) references/da-1-1-definitions-scope.md, references/da-1-1-1-data-analysis-vs-analytics-vs-data.md, references/da-1-1-2-analysis-vs-synthesis.md, references/da-1-1-3-quantitative-vs-qualitative-analysis.md, …
da-2-data-analysis-lifecycle Data Analysis Lifecycle & Process (hub) references/da-2-1-process-frameworks.md, references/da-2-1-1-crisp-dm.md, references/da-2-1-2-kdd.md, references/da-2-1-3-semma.md, …
da-3-data-acquisition-sampling Data Acquisition, Collection & Sampling (hub) references/da-3-1-data-sources.md, references/da-3-1-1-primary-vs-secondary.md, references/da-3-1-2-internal-vs-external.md, references/da-3-1-3-structured-semi-structured-unstructured.md, …
da-analytical-methods Data Analytical Methods (cleaning, EDA, modeling, ML, causal, time-series) references/da-4-data-cleaning-preparation.md, references/da-5-exploratory-data-analysis.md, references/da-6-statistical-modeling.md, references/da-7-machine-learning.md, …
da-data-engineering-platform Data Engineering & Analytics Platform (pipelines, OLAP, modeling, governance) references/da-10-tools-and-languages.md, references/da-13-data-engineering-and-pipelines.md, references/da-14-streaming-analytics.md, references/da-18-semantic-layer-headless-bi.md, …
da-applied-and-communication Applied Analytics, Visualization, Communication & Ethics references/da-8-data-visualization.md, references/da-9-reporting-communication.md, references/da-11-ethics-and-privacy.md, references/da-21-product-analytics.md, …