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Learning path

Business Intelligence Analyst learning path: everything to study, topic by topic

Comprehensive business intelligence assessment covering data foundations, SQL and data modeling, ETL and data pipelines, visualization and dashboards, statistical analysis, data governance, and expert BI strategy.

Short answer

To learn Business Intelligence Analyst, work through 7 modules and 30 topics in the order they build on each other. It starts with Data Foundations, then moves on to SQL & Data Modeling and ETL & Data Pipelines. Use the map as a checklist, or measure your level and study only what is missing.

7
modules
30
topics
50
min to assess

What you will study

Topics are grouped by module. Open one to see its objectives and key concepts.

1 · Data Foundations4 topics
  • Relational Database Concepts

    Relational theory, normalization forms (1NF through 3NF), primary and foreign keys, referential integrity, and ACID properties for transactional systems

    Objectives

    • Apply normalization rules to eliminate data redundancy in relational schemas
    • Design entity-relationship diagrams that enforce referential integrity

    Key concepts

    • normalization
    • primary key
    • foreign key
    • referential integrity
    • ACID properties
    • entity-relationship model
  • Dimensional Modeling

    Star schema and snowflake schema design, fact tables, dimension tables, slowly changing dimensions (SCD types), conformed dimensions, and Kimball methodology

    Objectives

    • Design star and snowflake schemas for common business domains
    • Select the appropriate SCD type for a given business requirement

    Key concepts

    • star schema
    • snowflake schema
    • fact table
    • dimension table
    • slowly changing dimensions
    • conformed dimensions
  • Data Warehouse Architecture

    Enterprise data warehouse design patterns, Kimball vs Inmon methodologies, staging layers, ODS, data mart architecture, and warehouse scalability considerations

    Objectives

    • Compare Kimball and Inmon approaches and recommend the right fit for a given scenario
    • Design a layered data warehouse architecture from staging to presentation

    Key concepts

    • Kimball methodology
    • Inmon methodology
    • staging layer
    • ODS
    • data mart
    • enterprise data warehouse
  • BI Data Modeling

    Advanced star schema and snowflake schema design, fact and dimension table optimization, slowly changing dimensions implementation, bridge tables, and degenerate dimensions

    Objectives

    • Design optimized fact and dimension tables for complex business domains
    • Implement slowly changing dimension strategies that balance history preservation with query performance

    Key concepts

    • star schema optimization
    • snowflake schema
    • fact table design
    • dimension table design
    • slowly changing dimensions
    • bridge tables
2 · SQL & Data Modeling3 topics
  • SQL Window Functions

    Analytic window functions including ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, and running aggregates with PARTITION BY and ORDER BY clauses

    Objectives

    • Write window functions to compute rankings, running totals, and period-over-period comparisons
    • Choose the correct window function for a given analytical requirement

    Key concepts

    • ROW_NUMBER
    • RANK
    • DENSE_RANK
    • LAG/LEAD
    • PARTITION BY
    • running aggregates
  • CTEs & Advanced Aggregation

    Common Table Expressions for query readability, recursive CTEs, GROUPING SETS, ROLLUP, CUBE operators, conditional aggregation with CASE, and subquery optimization

    Objectives

    • Refactor complex subqueries into readable CTEs and recursive CTEs
    • Use GROUPING SETS and ROLLUP to produce multi-level aggregation reports

    Key concepts

    • CTE
    • recursive CTE
    • GROUPING SETS
    • ROLLUP
    • CUBE
    • conditional aggregation
  • Semantic Layer Design

    Semantic layer architecture, business metric definitions, calculated measures, metric taxonomy, dbt metrics layer, and universal metric store patterns

    Objectives

    • Design a semantic layer that provides consistent metric definitions across tools
    • Define reusable calculated measures with proper aggregation logic

    Key concepts

    • semantic layer
    • metric definitions
    • calculated measures
    • metric taxonomy
    • dbt metrics
    • universal metric store
3 · ETL & Data Pipelines5 topics
  • ETL Design Patterns

    Extract-Transform-Load vs ELT patterns, incremental vs full load strategies, change data capture, data validation checkpoints, and idempotent pipeline design

    Objectives

    • Select the appropriate load strategy for a given data source and volume
    • Design idempotent ETL pipelines with proper validation checkpoints

    Key concepts

    • ETL vs ELT
    • incremental load
    • full load
    • change data capture
    • idempotent pipelines
    • data validation
  • Data Lake vs Data Warehouse

    Data lake architecture, lakehouse paradigm, schema-on-read vs schema-on-write, Delta Lake and Iceberg table formats, and cost-performance trade-offs

    Objectives

    • Evaluate when to use a data lake, data warehouse, or lakehouse architecture
    • Compare schema-on-read and schema-on-write approaches for different use cases

    Key concepts

    • data lake
    • lakehouse
    • schema-on-read
    • schema-on-write
    • Delta Lake
    • Iceberg
  • Data Pipeline Orchestration

    Workflow orchestration with Airflow, dbt, and Prefect; DAG design, dependency management, retry strategies, SLA monitoring, and pipeline observability

    Objectives

    • Design DAGs with proper dependency management, retries, and SLA alerts
    • Implement pipeline observability to detect and diagnose data freshness issues

    Key concepts

    • DAG design
    • dependency management
    • retry strategies
    • SLA monitoring
    • pipeline observability
    • orchestration tools
  • BI Performance Optimization

    Query folding and pushdown optimization, aggregation tables and pre-computed summaries, caching strategies, incremental refresh, and report rendering optimization

    Objectives

    • Implement query folding and aggregation strategies to optimize BI report performance
    • Design incremental refresh and caching architectures for large-scale BI deployments

    Key concepts

    • query folding
    • aggregation tables
    • caching strategies
    • incremental refresh
    • report rendering
    • DirectQuery optimization
  • Real-Time BI

    Streaming dashboards and near-real-time refresh architectures, alerting and threshold monitoring, operational BI patterns, event-driven analytics, and real-time vs batch trade-offs

    Objectives

    • Design real-time BI architectures that balance freshness requirements with system cost and complexity
    • Implement alerting and threshold monitoring for operational dashboards

    Key concepts

    • streaming dashboards
    • near-real-time refresh
    • alerting
    • operational monitoring
    • event-driven analytics
    • real-time vs batch
4 · Visualization & Dashboards5 topics
  • Power BI & Tableau Best Practices

    Data modeling in Power BI (DAX, relationships), Tableau data blending and LOD expressions, performance optimization, and enterprise deployment patterns

    Objectives

    • Build performant data models in Power BI with proper DAX measures and relationships
    • Use Tableau LOD expressions and optimize workbook performance for large datasets

    Key concepts

    • DAX measures
    • Power BI data model
    • LOD expressions
    • Tableau calculations
    • performance optimization
    • enterprise deployment
  • KPI Design & Metrics Strategy

    Defining actionable KPIs, leading vs lagging indicators, metric hierarchies, target-setting frameworks, OKR alignment, and metric decomposition trees

    Objectives

    • Design KPI frameworks that connect operational metrics to strategic outcomes
    • Build metric decomposition trees to identify drivers behind top-level KPIs

    Key concepts

    • leading indicators
    • lagging indicators
    • metric hierarchy
    • OKR alignment
    • target setting
    • metric decomposition
  • Dashboard UX & Data Storytelling

    Dashboard layout design, cognitive load management, chart selection principles, annotation and narrative techniques, and storytelling with data for executive audiences

    Objectives

    • Design dashboards that minimize cognitive load and guide user attention to insights
    • Craft data narratives that drive executive decision-making

    Key concepts

    • cognitive load
    • chart selection
    • data-ink ratio
    • annotation
    • narrative arc
    • executive storytelling
  • Advanced DAX & Measures

    Calculated columns vs measures in DAX, filter context manipulation, CALCULATE and iterator functions, time intelligence patterns, and complex measure optimization

    Objectives

    • Write complex DAX measures using CALCULATE with proper filter context manipulation
    • Implement time intelligence patterns for year-over-year and rolling period calculations

    Key concepts

    • calculated columns vs measures
    • filter context
    • CALCULATE function
    • time intelligence
    • iterator functions
    • context transition
  • Data Storytelling with Dashboards

    Narrative structure in dashboard design, executive vs operational dashboard patterns, annotation strategies, insight highlighting, and data presentation for different audiences

    Objectives

    • Design dashboards that tell a coherent story with clear narrative structure and annotations
    • Adapt dashboard design patterns for executive vs operational audiences

    Key concepts

    • narrative structure
    • executive dashboards
    • operational dashboards
    • annotation strategies
    • insight highlighting
    • audience-specific design
5 · Statistical Analysis3 topics
  • Descriptive & Diagnostic Analytics

    Descriptive statistics, distribution analysis, trend decomposition, root cause analysis, cohort analysis, funnel analysis, and anomaly detection techniques

    Objectives

    • Conduct root cause analysis using cohort and funnel decomposition
    • Identify anomalies in business metrics and determine their underlying drivers

    Key concepts

    • descriptive statistics
    • trend decomposition
    • root cause analysis
    • cohort analysis
    • funnel analysis
    • anomaly detection
  • Regression Analysis

    Linear and logistic regression for business forecasting, feature selection, multicollinearity detection, model validation, and interpreting regression outputs for stakeholders

    Objectives

    • Build and validate regression models for business forecasting scenarios
    • Interpret regression coefficients and communicate findings to non-technical stakeholders

    Key concepts

    • linear regression
    • logistic regression
    • R-squared
    • multicollinearity
    • feature selection
    • model validation
  • A/B Testing & Experimentation

    Hypothesis formulation, sample size calculation, statistical significance, p-values, confidence intervals, type I and type II errors, and multi-armed bandit approaches

    Objectives

    • Design A/B tests with proper sample size calculations and success criteria
    • Interpret test results and communicate statistical significance to business teams

    Key concepts

    • hypothesis testing
    • sample size
    • statistical significance
    • p-value
    • confidence interval
    • type I/II errors
6 · Data Governance4 topics
  • Data Quality Frameworks

    Data quality dimensions (accuracy, completeness, consistency, timeliness), quality scoring, data profiling, validation rules, and remediation workflows

    Objectives

    • Design a data quality scoring framework across the six quality dimensions
    • Build automated data profiling and validation workflows for critical datasets

    Key concepts

    • accuracy
    • completeness
    • consistency
    • timeliness
    • data profiling
    • quality scoring
  • Master Data Management

    MDM strategies (registry, consolidation, coexistence), golden record creation, entity resolution, data stewardship, and cross-system synchronization

    Objectives

    • Select the appropriate MDM style for a given organizational maturity level
    • Design entity resolution and survivorship rules for golden record creation

    Key concepts

    • golden record
    • entity resolution
    • data stewardship
    • MDM styles
    • cross-system sync
    • survivorship rules
  • Data Catalog & Lineage

    Metadata management, data catalog implementation, automated lineage tracking, impact analysis, data discovery, and compliance documentation through lineage

    Objectives

    • Implement a data catalog that enables self-service data discovery across the organization
    • Design automated lineage tracking to support impact analysis and compliance audits

    Key concepts

    • metadata management
    • data catalog
    • data lineage
    • impact analysis
    • data discovery
    • compliance documentation
  • BI Governance & Standards

    BI naming conventions and standards, report certification processes, usage analytics and adoption measurement, access control patterns, and content lifecycle management

    Objectives

    • Establish BI governance standards that enforce consistency without impeding productivity
    • Design usage analytics and certification processes to maintain BI content quality

    Key concepts

    • naming conventions
    • certification process
    • usage analytics
    • access control
    • content lifecycle
    • BI standards
7 · Expert BI Strategy6 topics
  • Self-Service BI Enablement

    Self-service BI governance, certified dataset programs, data literacy training, guardrails for ad-hoc analysis, and balancing agility with data trust

    Objectives

    • Design a self-service BI program that balances user autonomy with data governance
    • Build a certified dataset program to ensure analytical consistency across teams

    Key concepts

    • self-service governance
    • certified datasets
    • data literacy
    • ad-hoc guardrails
    • data trust
    • BI enablement
  • Enterprise BI Strategy & Governance

    BI roadmap development, tool consolidation, BI center of excellence, ROI measurement, organizational change management, and executive stakeholder alignment

    Objectives

    • Develop a multi-year enterprise BI roadmap aligned with business strategy
    • Establish a BI center of excellence that drives adoption and governance

    Key concepts

    • BI roadmap
    • tool consolidation
    • BI center of excellence
    • ROI measurement
    • change management
    • stakeholder alignment
  • BI Tool Selection & Architecture

    Evaluating BI platforms against organizational needs, embedded analytics, self-service vs governed BI architecture, build vs buy decisions, and platform scalability considerations

    Objectives

    • Evaluate BI platforms using a structured framework covering capability, cost, and organizational fit
    • Design BI architectures that balance self-service flexibility with governance requirements

    Key concepts

    • BI platform evaluation
    • embedded analytics
    • self-service architecture
    • governed BI
    • platform scalability
    • total cost of ownership
  • Embedded Analytics

    Embedding dashboards in applications, row-level security for multi-tenant environments, API-driven analytics, white-label BI, and embedded analytics architecture patterns

    Objectives

    • Design embedded analytics architectures with proper multi-tenant row-level security
    • Evaluate embedding approaches and select the right pattern for SaaS product analytics

    Key concepts

    • dashboard embedding
    • row-level security
    • multi-tenant analytics
    • API access
    • white-label BI
    • embedded architecture
  • BI for Stakeholder Management

    Requirements gathering for BI projects, iterative dashboard design, user adoption strategies, change management for BI rollouts, and measuring BI program success

    Objectives

    • Conduct effective BI requirements gathering sessions that translate business needs into analytical solutions
    • Design change management strategies that drive BI adoption across the organization

    Key concepts

    • requirements gathering
    • iterative design
    • user adoption
    • change management
    • BI program success
    • stakeholder alignment
  • BI Migration & Modernization

    Migrating from legacy BI tools, cloud BI adoption strategies, hybrid on-premises and cloud architectures, migration risk assessment, and modernization roadmap planning

    Objectives

    • Design BI migration strategies that minimize business disruption and data loss risk
    • Plan cloud BI modernization roadmaps with clear phases and success criteria

    Key concepts

    • legacy migration
    • cloud BI adoption
    • hybrid architectures
    • migration risk assessment
    • modernization roadmap
    • data migration patterns

Levels: Junior, Mid, Senior, and Expert

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Frequently asked questions

Where should I start studying Business Intelligence Analyst?

Start with Data Foundations: Relational Database Concepts, Dimensional Modeling, and Data Warehouse Architecture. The map orders topics by what they depend on, so each one prepares the next.

How long does a full Business Intelligence Analyst assessment take?

A full assessment of this path takes about 50 minutes. Study time depends on where you start; a diagnostic shows which topics you can skip.

Do I need an account to use this page?

No. The map, objectives and key concepts are open. You only need an account to be measured, get your gap by topic and receive a plan.