Overview

The Data Professional's Dictionary: Volume 2 Go deeper into the language of modern data work with this advanced reference covering 482 terms across fifteen subject areas. Building on the foundational vocabulary of Volume 1, this second volume takes you into the more sophisticated and specialised areas of the data profession where technical depth, analytical rigour, and professional judgement matter most. Every entry is built for practical use. Each term comes with a clear definition written for working professionals, a real workplace example showing the term in context, an explanation of why it matters in practice, the single most common mistake practitioners make, and one immediately actionable tip you can apply straight away. Volume 2 covers the advanced and specialist layer of data work across fifteen chapters. Statistics and Probability covers the mathematical foundations of analytical reasoning including distributions, hypothesis testing, and regression. Machine Learning covers algorithms, evaluation methods, deployment patterns, and the full supervised and unsupervised learning landscape. Deep Learning and AI covers neural networks, transformers, large language models, generative AI, and the safety and governance frameworks that surround them. Mathematics for ML covers the linear algebra, calculus, and optimisation concepts that explain how models learn. MLOps and DataOps covers the operational practices that keep machine learning systems reliable in production. Graph and Network Data covers graph databases, graph algorithms, network centrality, and graph neural networks. Search and Retrieval covers information retrieval, semantic search, vector databases, and modern ranking methods. Data Governance and Quality covers the policies, frameworks, and practices that make data trustworthy and compliant at scale. Data Testing and Observability covers automated quality testing, pipeline monitoring, and the discipline of keeping data reliable. Security and Access covers encryption, access control, authentication, data masking, and protecting data from breach and misuse. Programming and Tools covers the languages, frameworks, and platforms data professionals use daily from Python and SQL to dbt, Spark, and the major BI tools. Networking and APIs covers the protocols, integration patterns, and network concepts that connect data systems to the world around them. Time Series and Forecasting covers the models, evaluation methods, and analytical patterns specific to data ordered in time. Business and Strategy covers the commercial vocabulary of data work including ROI, OKRs, unit economics, and experimentation culture. Soft Skills and Process covers the communication, collaboration, critical thinking, and process skills that turn technical capability into genuine organisational impact. At the back of the book you will find a complete alphabetical reference index listing all 482 terms with their chapter, category, and a one line definition for fast lookup. This is Volume 2 of a two volume series. Volume 1 covers the foundational vocabulary of data work including core data concepts, roles, databases, SQL, data engineering, streaming, cloud infrastructure, architecture, analytics, and data visualisation. If you work with data at any level of the profession, this book belongs on your desk alongside Volume 1.

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