Business Analytics with SQL Training Course

Participants can run well-structured SQL statements and inspect result sets safely.

• SELECT, FROM and WHERE clauses structured for readable retrieval
• Column aliases applied to produce intelligible business-facing output labels
• Result sets limited during exploration using LIMIT and TOP
• SQL comments record assumptions, source choices and calculation rationale
• Statement execution and error messages interpreted within the query editor
• Schema browsers used to inspect tables, columns, types and constraints

Participants can select relevant records using precise filters and data-type handling.

• Boolean logic combines AND, OR and NOT without ambiguous conditions
• IN, BETWEEN and LIKE filters target defined business populations
• NULL handling distinguishes unavailable values from zero and blank values
• ORDER BY sequences outputs for review, ranking and exception analysis
• Date, numeric and text types checked before comparison operations
• CASE expressions classify records into transparent business reporting categories

Participants can calculate grouped measures while preserving the correct level of detail.

• COUNT, SUM, AVG, MIN and MAX calculate business measures
• GROUP BY establishes reporting grain before aggregate calculations occur
• HAVING filters aggregated groups after measure calculation is complete
• Distinct counts prevent duplicate entity reporting in transactional datasets
• Conditional aggregation calculates segmented KPIs within one result set
• Reconciliation totals compare query outputs against control figures

Participants can combine related tables without introducing duplication or excluding required records.

• INNER JOIN returns records with matching keys in both tables
• LEFT JOIN retains the analytical population when related records are absent
• Join cardinality assessed as one-to-one, one-to-many or many-to-many
• Duplicate rows traced through key uniqueness and relationship checks
• Composite keys matched using multiple fields where single keys fail
• Reconciliation queries compare row counts before and after joins

Participants can profile data and document controls that support reliable analysis.

• Completeness checks identify missing identifiers, dates, amounts and categories
• Validity rules test permitted values, ranges and date sequences
• Uniqueness tests locate duplicate customers, transactions and reference records
• Referential integrity exceptions reveal orphaned records across related tables
• Control totals reconcile source extracts with analytical query outputs
• Data-quality findings documented by issue, impact, owner and remediation

Participants can analyse activity over time using consistent reporting periods and date logic.

• Date extraction derives year, quarter, month and weekday reporting attributes
• Calendar tables standardise fiscal periods, working days and period comparisons
• Rolling periods calculate trailing seven-day, monthly and quarterly measures
• Date differences measure customer tenure, processing time and service delays
• Period boundaries prevent partial-month comparisons and misleading trend calculations
• Time-based filters isolate current, prior and comparable reporting periods

Participants can structure multi-step analytical queries that are readable, testable and reusable.

• Common table expressions separate staging, calculation and presentation query steps
• Named query blocks expose business logic for peer review
• Intermediate results tested before combining complex analytical transformations
• Reusable filters centralise reporting periods and population definitions
• Query formatting applies consistent indentation, aliases and logical sections
• Source lineage documented from raw table through derived analytical measure

Participants can calculate rankings, running totals and period comparisons without losing row detail.

• OVER clauses define partitions and ordering for analytical calculations
• ROW_NUMBER identifies latest records and deduplicates event histories
• RANK and DENSE_RANK compare performance within defined business segments
• Running totals track cumulative revenue, volume and target attainment
• LAG and LEAD calculate period-on-period movement and event intervals
• Window frames control rolling calculations across ordered observation sets

Participants can segment customer and product performance using transparent SQL calculations.

• Customer value calculated from revenue, margin, frequency and recency measures
• Product mix analysis compares sales contribution across categories and channels
• Pareto analysis identifies customers or products driving cumulative value
• Segmentation rules classify accounts using explicit threshold-based business logic
• Cross-sell patterns identify products purchased by shared customer populations
• Contribution analysis separates volume, price and mix movement drivers

Participants can measure behavioural progression and retention from customer activity data.

• Cohort assignment groups customers by first purchase or registration period
• Retention matrices measure returning activity across subsequent reporting periods
• Conversion funnels calculate progression between defined customer journey stages
• Drop-off analysis identifies the stages with highest customer abandonment
• Activation metrics distinguish registered users from engaged customers
• Churn definitions align inactivity thresholds with the underlying business model

Participants can use SQL to investigate process flow, workload and service performance.

• Service-level measures compare completion timestamps against agreed target times
• Queue ageing identifies unresolved cases by age band and priority
• Process bottlenecks located through stage duration and hand-off analysis
• Workload distribution compares volumes across teams, locations and channels
• Exception populations isolate breached, stalled or repeatedly reopened cases
• Root-cause cuts segment operational failures by product, region and reason

Participants can construct reconciled SQL analyses for revenue, cost and profitability questions.

• Revenue recognition dates distinguished from order, invoice and payment dates
• Gross margin calculations combine sales values with attributable cost records
• Budget variance analysis compares actual, plan and prior-period values
• Currency conversion logic applies approved rates and effective dates
• Account hierarchies roll detailed transactions into management reporting lines
• Financial reconciliations trace reported totals to transaction-level supporting data

Participants can define fact tables and dimensions that support consistent analytical reporting.

• Kimball dimensional modelling separates measurable facts from descriptive dimensions
• Fact table grain declared before selecting measures and foreign keys
• Conformed dimensions support consistent reporting across multiple business processes
• Star schemas simplify analytical queries for common reporting requirements
• Slowly changing dimensions preserve historically accurate descriptive attributes
• Date dimensions provide standard fiscal, calendar and comparative reporting fields

Participants can diagnose common query-performance issues and improve maintainability.

• Execution plans reveal scans, joins, sorts and estimated row volumes
• Predicate pushdown reduces records processed before joins and aggregations
• Indexes assessed against filter, join and sort access patterns
• SELECT star avoided to reduce unnecessary data transfer and ambiguity
• Sargable predicates preserve index use during date and text filtering
• Query review checklists assess correctness, clarity, performance and controls

Participants can communicate SQL findings with clear definitions, evidence and appropriate controls.

• Metric definitions specify formula, grain, exclusions, owner and refresh cadence
• Analysis narratives distinguish observed facts, interpretations and recommended actions
• Assumptions registers record source limitations, transformations and unresolved questions
• Data lineage diagrams link business outputs to source tables and fields
• Access controls protect sensitive customer, employee and financial data
• Peer review criteria test reproducibility, logic, documentation and decision relevance

Participants can scope a SQL analysis project with defined decisions, data and validation steps.

• Problem statements identify decisions, stakeholders, deadlines and expected business value
• Data inventories map required entities, systems, owners and extraction constraints
• Metric contracts agree calculation rules before analysis begins
• Analysis plans sequence data preparation, validation, calculation and review tasks
• Acceptance criteria define reconciliations, tolerances and required output formats
• Risk logs capture privacy, access, quality and interpretation dependencies

Participants can build, review and present a complete SQL-based business analysis with validated findings and recommendations.

• Business case framed through decision, stakeholder, measure and analytical scope
• Source tables profiled for grain, keys, completeness and relationship risks
• SQL analysis combines joins, aggregations, CTEs and window functions
• KPI calculations reconciled against stated controls and source totals
• Findings segmented to identify drivers, exceptions and decision implications
• Peer review tests query logic, assumptions, documentation and reproducibility
• Presentation communicates evidence, recommendations, limitations and next analytical steps