tech-studies

BI Analyst

2 min read

BI Analyst

roadmap.sh: https://roadmap.sh/bi-analyst

Suggested path through the BI Analyst nodes. Each node links to its lesson when written.

Nodes

Foundations

  • What is Business Intelligence
  • Role of a BI Analyst
  • BI vs Data Analytics vs Data Science
  • Data-driven decision making
  • Key stakeholders and business domains
  • BI project lifecycle
  • Descriptive vs Diagnostic vs Predictive vs Prescriptive

Data fundamentals

  • Structured vs Unstructured data
  • Databases vs Data Warehouses vs Data Lakes
  • OLTP vs OLAP
  • Relational data model
  • Primary and Foreign keys
  • Normalization and Denormalization
  • Star and Snowflake schemas
  • Fact and Dimension tables
  • Slowly Changing Dimensions

SQL

  • SELECT, WHERE, ORDER BY
  • Aggregations and GROUP BY
  • HAVING
  • JOINs (inner, left, right, full)
  • Subqueries
  • Common Table Expressions (CTEs)
  • Window functions
  • CASE expressions
  • Views
  • Stored procedures
  • Query performance and indexing

Data preparation

  • ETL vs ELT
  • Data extraction from sources
  • Data cleaning
  • Handling missing values
  • Data transformation and shaping
  • Data validation and quality checks
  • Data modeling for analytics

Excel / Spreadsheets

  • Formulas and functions
  • Lookup functions (VLOOKUP, XLOOKUP, INDEX/MATCH)
  • Pivot tables
  • Data validation
  • Conditional formatting
  • Power Query
  • Charts in spreadsheets

Statistics

  • Descriptive statistics
  • Measures of central tendency and dispersion
  • Distributions
  • Correlation vs Causation
  • Sampling
  • Hypothesis testing basics
  • Confidence intervals

BI tools

  • Power BI
  • Tableau
  • Looker
  • Qlik
  • Google Data Studio / Looker Studio
  • Metabase
  • Data connections and gateways
  • DAX basics (Power BI)
  • LOD expressions (Tableau)

Data visualization

  • Principles of effective visualization
  • Choosing the right chart type
  • Color and accessibility
  • Dashboard design
  • KPIs and metrics
  • Interactivity and filters
  • Storytelling with data
  • Avoiding misleading charts

Reporting and delivery

  • Building reports
  • Scheduling and distribution
  • Self-service BI
  • Data governance
  • Row-level security
  • Documentation and data dictionaries

Soft skills and business acumen

  • Requirements gathering
  • Communicating insights
  • Domain knowledge (finance, marketing, sales, ops)
  • Translating business questions to data questions
  • Presenting to stakeholders

Resources

See resources.md.

Project ideas

  • Build an end-to-end sales dashboard in Power BI from a raw CSV, including a star-schema data model, DAX measures, and row-level security per region.
  • Design a self-service Tableau dashboard tracking marketing-funnel KPIs (CAC, conversion, retention) with drill-downs by channel and cohort.
  • Write a SQL reporting layer (views + CTEs + window functions) over a sample e-commerce database that powers a weekly executive KPI report.

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