Analytics, Data Science & AI Landscape
Understand how data analytics, business intelligence, data science, artificial intelligence, machine learning, deep learning, data engineering and MLOps overlap and differ. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Data AnalyticsData analytics turns raw observations into evidence for questions, decisions and operational improvement. It spans descriptive, diagnostic, predictive and prescriptive analysis. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02Business IntelligenceBusiness intelligence focuses on governed reporting, dashboards, semantic metrics and repeatable decision support, often over enterprise data warehouses. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03Data ScienceData science combines statistics, programming, domain knowledge and experimental reasoning to extract insight and build data-driven systems. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Artificial IntelligenceArtificial intelligence is the broad field of building systems that perform tasks associated with perception, reasoning, planning, language, learning or decision making. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Machine LearningMachine learning learns reusable patterns from examples or interaction rather than encoding every decision rule manually. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
06Deep LearningDeep learning uses multi-layer neural networks to learn hierarchical representations, often directly from high-dimensional raw data. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
07Data EngineeringData engineering builds reliable systems for ingesting, transforming, storing and serving data so analytics and ML receive reproducible inputs. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
08MLOpsMLOps applies software, data and operational engineering practices to the lifecycle of machine-learning systems. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.