About BI Analyst
A business intelligence analyst turns raw company data into the reports, dashboards,
and recommendations that teams use to make decisions, and this course walks through
the craft in the order most analysts actually learn it. It starts with
BI Fundamentals & the Analyst Role, then builds your working
toolkit with Spreadsheets & Data Literacy so you can read, sort,
and sanity-check data from day one.
Next comes the language of data: SQL Fundamentals and
Advanced SQL cover joins, aggregations, CTEs, and window functions,
while Statistics for Analysts gives you the reasoning behind
averages, distributions, correlation, and A/B tests. Data Cleaning &
Preparation then shows how to fix the messy, incomplete data you will meet
in every real project.
With clean data in hand, Data Modeling & Warehousing covers star
schemas and fact and dimension tables, and Data Visualization
Principles teaches you to choose the right chart. You then put it into
practice in Power BI & Tableau, the two most requested BI tools
in job postings, and learn to design Metrics, KPIs & Dashboards
that people actually use.
Finally, ETL & Cloud Data Platforms covers pipelines, dbt, and
warehouses like BigQuery and Snowflake, and Data Storytelling &
Stakeholders covers how to present findings and influence decisions: the
skills that separate someone who builds charts from someone the business relies on.
Content
12 modules
01
BI Fundamentals & the Analyst Role
What business intelligence is, how data flows from source systems to decisions, and how analysts, engineers, and data scientists differ.
BI LifecycleDescriptive AnalyticsAnalyst Workflow
02
Spreadsheets & Data Literacy
Data types, formulas, lookups, and pivot tables in Excel or Google Sheets, plus how to read a dataset critically before you analyse it.
ExcelPivot TablesXLOOKUP
03
SQL Fundamentals
Querying relational data with SELECT, filtering, sorting, grouping, aggregation, and joins across multiple tables.
SELECTJOINGROUP BY
04
Advanced SQL
Writing analysis-grade queries with CTEs, subqueries, window functions, and date logic, and making slow queries faster.
CTEsWindow FunctionsQuery Optimization
05
Statistics for Analysts
Descriptive statistics, distributions, correlation vs causation, sampling, confidence intervals, and running and reading A/B tests.
DistributionsHypothesis TestingA/B Testing
06
Data Cleaning & Preparation
Handling missing values, duplicates, outliers, and inconsistent formats using Power Query, SQL, or Python with pandas.
Power QuerypandasData Quality
07
Data Modeling & Warehousing
Designing analytics-ready data with fact and dimension tables, star schemas, and slowly changing dimensions.
Star SchemaFact & DimensionData Warehouse
08
Data Visualization Principles
Choosing the right chart for the question, using colour and layout with purpose, and avoiding misleading visuals.
Chart SelectionColour TheoryVisual Hierarchy
09
Power BI & Tableau
Connecting data sources, building interactive reports, and writing calculations in the two most widely used BI tools.
Power BIDAXTableau
10
Metrics, KPIs & Dashboards
Defining metrics that mean something, building a KPI framework, and designing dashboards that answer real business questions.
KPI DesignCohorts & FunnelsDashboard Layout
11
ETL & Cloud Data Platforms
How data gets from source systems into a warehouse, with ETL vs ELT, transformation with dbt, and cloud platforms like BigQuery and Snowflake.
ETL / ELTdbtBigQuerySnowflake
12
Data Storytelling & Stakeholders
Framing findings as a clear narrative, presenting to non-technical audiences, and turning analysis into recommendations people act on.
StorytellingPresentingRequirements Gathering
What You Can Do After This Course
By the end of the curriculum you'll have worked through 5 hands-on projects and
covered the core of what a junior to mid-level BI analyst role expects. Concretely,
you'll be able to:
Explain the BI workflow: how data moves from source systems through a warehouse to the dashboards decision-makers rely on.
Query data with SQL: write joins, CTEs, and window functions to answer business questions directly from a database.
Clean and prepare data: fix missing values, duplicates, and inconsistent formats before they distort your results.
Reason with statistics: interpret distributions and correlations, and design and read an A/B test without common pitfalls.
Model data for analysis: structure fact and dimension tables in a star schema that makes reporting fast and consistent.
Build dashboards: create interactive reports in Power BI or Tableau, with clear KPIs and charts matched to the question.
Tell the story: present findings to stakeholders and turn your analysis into recommendations the business can act on.