Skip to content

BI & Analytics

Product Analytics & Metrics

  • [[product-analytics-fundamentals]] - analyst role, metrics pyramid, north star metric, vanity vs actionable metrics
  • [[product-metrics-framework]] - DAU/MAU/WAU, stickiness, retention types, user lifecycle stages
  • [[unit-economics]] - CPA/LTV/ARPU formulas, growth levers, payback period, RFM segmentation
  • [[funnel-analysis]] - conversion funnels, optimization process, event maps, SQL funnel patterns
  • [[cohort-retention-analysis]] - cohort types, retention SQL, Tableau LOD cohorts, behavioral analysis

Tableau

  • [[tableau-fundamentals]] - data connections, live vs extract, joins/unions/relations/blending, filter order, RLS
  • [[tableau-calculations]] - row-level, aggregated, table calcs, window calcs, parameters, logical functions
  • [[tableau-lod-expressions]] - FIXED/INCLUDE/EXCLUDE, filter order interaction, performance implications
  • [[tableau-chart-types]] - encoding attributes, chart selection guide, actions, sparklines, KPI factoids
  • [[tableau-performance-optimization]] - performance recorder, server/data/calculation/view-level tuning

Power BI

  • [[powerbi-fundamentals]] - interface, Power Query (M), DAX basics, data model, relationships, filters
  • [[powerbi-advanced-features]] - themes, custom visuals, what-if parameters, drill-through, bookmarks, advanced DAX

Dashboard Design

  • [[dashboard-design-patterns]] - F-pattern, modular layout, Gestalt principles, UX laws, storytelling
  • [[color-theory-visualization]] - categorical/sequential/diverging scales, accessibility, custom palettes
  • [[bi-development-process]] - requirements gathering, dashboard map, metrics registry, stakeholder communication

BI Tool Landscape

  • [[bi-tools-comparison]] - Tableau vs Power BI vs Superset vs DataLens, selection framework

Data Analysis (SQL & Python)

  • [[sql-for-analytics]] - SELECT, JOINs, window functions, CTEs, analytics-specific query patterns
  • [[pandas-data-analysis]] - DataFrame operations, cleaning, groupby, pivot tables, merging, visualization
  • [[python-for-analytics]] - Python basics, NumPy, Jupyter workflow for analysts

Mobile & Web Analytics

  • [[web-marketing-analytics]] - GA, Yandex Metrica, GTM, UTM tags, attribution models, cross-channel analytics
  • [[mobile-analytics-platforms]] - Firebase, AppMetrica, Amplitude SDK integration and reporting
  • [[mobile-attribution-fraud]] - AppsFlyer attribution, deep links, Protect360 fraud detection
  • [[app-store-optimization]] - ASO ranking factors, keyword research, conversion optimization, ratings strategy