Google Business Intelligence study plan

Define business intelligence and distinguish it from data analysis, data science, and reporting in a one-page written summary.

Map the BI lifecycle (requirements, data collection, processing, analysis, visualization, action) and label where stakeholders, analysts, and engineers act.

Compare at least four common BI roles and their responsibilities using a table you build from official course descriptions.

Identify the core question types BI answers (descriptive, diagnostic, predictive, prescriptive) and write an example for each from a retail or service scenario.

Explain how business objectives, KPIs, and success metrics connect, and convert two sample business goals into measurable KPIs.

Evaluate data quality dimensions (completeness, accuracy, consistency, timeliness, validity) using a small public dataset and document three issues found.

Describe the difference between structured, semi-structured, and unstructured data and give two examples of each relevant to BI.

Outline privacy, security, and governance considerations that constrain BI work, including access control and data retention.

Model a business process using a simple entity-relationship diagram with at least four entities, primary keys, and foreign keys.

Differentiate normalized (3NF) and denormalized (star schema) designs and justify a choice for an analytics use case.

Build a star schema from a flat sales dataset, identifying the fact table, dimensions, and grain.

Write SQL to create the tables from your schema and insert a small sample of rows.

Explain the purpose and stages of the ETL and ELT approaches and identify when each is preferred.

Describe common data pipeline components: ingestion, transformation, orchestration, and loading, and map them to a tool category.

Extract data from two sources (for example, CSV and a public API) and load it into a staging table.

Apply at least three transformation steps (cleaning, deduplication, type casting) and document each with SQL.

Write intermediate SQL using joins, aggregations, subqueries, and window functions to answer five business questions.

Validate pipeline output by reconciling row counts and key totals between source and target tables.

Define the audience, decision, and metric for a dashboard before building it, using a one-page dashboard requirements brief.

Select chart types appropriate to comparison, distribution, trend, and relationship questions and justify each choice.

Build a two-page dashboard in a BI tool that includes at least one KPI card, one trend chart, one comparison chart, and one filter.

Apply layout, color, and labeling principles so the dashboard communicates a single clear message without clutter.

Add interactivity such as date and category filters and verify that all visuals respond correctly.

Write a short narrative that interprets the dashboard findings and states a recommended action.

Design a report structure for an executive audience that leads with the recommendation and supports it with evidence.

Translate dashboard findings into three decision options with trade-offs and a recommended next step.

Identify at least three common cognitive biases or chart pitfalls (for example, truncated axes, cherry-picked ranges) and correct them in your dashboard.

Peer-review a sample dashboard against a checklist of accuracy, clarity, and actionability criteria.

Consolidate notes into a single review sheet covering domain 1 definitions, domain 2 pipeline and modeling steps, and domain 3 dashboard principles.

Complete a timed 40-question self-assessment spanning all three domains and score at least 80%.

Re-teach the three weakest concepts from the self-assessment in writing, with an example for each.

Finalize a portfolio-ready case study that links a business question to a data model, pipeline, dashboard, and decision.