Data Analytics for Business Analysts Training Course

Participants can describe business data structures and their analytical implications.

• Entities, attributes and relationships translate business concepts into data structures
• Structured, semi-structured and unstructured data require different analysis approaches
• Grain identifies the lowest meaningful level of each dataset
• Identifiers distinguish natural keys, surrogate keys and duplicate records
• Fact and dimension tables support consistent reporting and trend analysis
• Data dictionaries document field meaning, format, ownership and permitted values

Participants can identify suitable data sources and document how data reaches an analysis.

• Source mapping links business questions to systems, files and external datasets
• Data lineage traces fields from capture point through transformation to report
• Primary data collection uses interviews, observation, surveys and sampling plans
• Secondary sources require checks for provenance, recency, bias and licensing
• Extraction requirements specify fields, date ranges, filters and refresh frequency
• Sampling frames define the population available for valid selection

Participants can assess data fitness for purpose and specify corrective actions.

• Accuracy, completeness, consistency, timeliness, validity and uniqueness define quality
• Profiling reveals null values, duplicates, outliers and invalid formats
• Reconciliation compares totals between source systems and management reports
• Business rules identify impossible dates, values and record combinations
• Root-cause analysis separates capture errors from integration and transformation defects
• Data-quality issue logs assign severity, owner, impact and remediation action

Participants can define measures that answer business questions consistently.

• Outcome, output, efficiency and quality measures serve different management decisions
• KPI trees connect strategic objectives with operational drivers and measures
• Metric definitions specify numerator, denominator, population, period and exclusions
• Baselines establish current performance before targets or benefits claims
• Leading and lagging indicators reveal causes, results and response timing
• Balanced scorecard perspectives prevent single-metric performance distortions

Participants can structure and validate spreadsheet data for repeatable analysis.

• Tidy data separates variables, observations and values into analysable tables
• Excel Tables create structured references, filters and controlled data ranges
• XLOOKUP and INDEX-MATCH reconcile records between related business datasets
• SUMIFS and COUNTIFS calculate segmented measures using explicit criteria
• PivotTables summarise volumes, values and rates by selected dimensions
• Validation checks compare row counts, totals and exception records

Participants can retrieve and summarise relational data using core SQL statements.

• SELECT statements retrieve specified columns from correctly identified tables
• WHERE clauses filter records using business rules and date conditions
• INNER and LEFT JOINs combine related records without losing required cases
• GROUP BY aggregates calculate volumes, values and rates by dimension
• CASE expressions classify records using transparent business logic
• ORDER BY and LIMIT support targeted review of exceptions

Participants can construct SQL queries for comparison, ranking and time-based analysis.

• Common table expressions separate complex query logic into reviewable steps
• Window functions calculate rankings, running totals and period comparisons
• COALESCE handles missing values without masking material data-quality problems
• Date functions group activity into weeks, months, quarters and cohorts
• Subqueries compare records against segment averages and threshold values
• Query validation reconciles extracts against independently calculated control totals

Participants can summarise business data using appropriate descriptive statistics.

• Mean, median and mode describe central tendency for different distributions
• Range, variance and standard deviation quantify spread and operational variation
• Percentiles reveal service levels, response times and customer experience patterns
• Frequency distributions expose concentration, skewness and unusual value clusters
• Box plots identify potential outliers requiring business investigation
• Rates and ratios enable fair comparisons between differently sized groups

Participants can design visual outputs that communicate decisions and performance clearly.

• Chart selection matches comparison, composition, distribution and relationship questions
• Bar, line, scatter and control charts communicate distinct analytical messages
• Pre-attentive attributes direct attention through position, colour, size and contrast
• Dashboard wireframes organise context, KPIs, trends and diagnostic detail
• Reference lines show targets, thresholds, averages and control limits
• Accessibility checks address labels, colour contrast and readable annotations

Participants can prepare a Power BI model that supports consistent business reporting.

• Power Query cleans columns, types, nulls and source-specific formatting
• Star schemas connect fact tables with conformed business dimensions
• Relationships require correct cardinality, filter direction and active paths
• Calendar tables support comparable periods, fiscal reporting and time intelligence
• DAX measures calculate reusable metrics within governed semantic models
• Model documentation records sources, refresh rules, measures and ownership

Participants can build interactive reports with controlled measures and useful drill paths.

• Report pages separate executive summaries from diagnostic operational detail
• Slicers support controlled filtering by period, region, product and segment
• Drill-through pages investigate exceptions without crowding summary views
• Tooltips provide definitions, context and supporting metrics at point-of-use
• Conditional formatting highlights threshold breaches and unusual performance changes
• Report testing confirms visual totals match validated source calculations

Participants can segment populations and identify meaningful differences in behaviour.

• Segmentation variables group customers by value, behaviour, need and risk
• RFM analysis ranks customers using recency, frequency and monetary value
• Cohort analysis compares retention and activity patterns over acquisition periods
• Cross-tabulation exposes differences between segments, channels and service outcomes
• Pareto analysis identifies high-impact causes, products and customer groups
• Segment profiles combine quantitative patterns with stated business implications

Participants can analyse time-based patterns and produce defensible short-term forecasts.

• Time-series decomposition separates trend, seasonality, cycles and irregular variation
• Moving averages smooth volatile demand while retaining operational visibility
• Exponential smoothing weights recent observations for short-term forecasting
• Forecast accuracy uses MAE, MAPE and bias measures
• Capacity analysis compares forecast demand with staffing, throughput and constraints
• Scenario assumptions document expected changes, risks and trigger points

Participants can assess whether observed differences support a business conclusion.

• Null and alternative hypotheses define claims before analysis begins
• Confidence intervals express plausible ranges around estimated business measures
• Statistical significance differs from operational and commercial significance
• A/B test design specifies treatment, control, outcome and exposure period
• Sample-size considerations reduce false conclusions from insufficient observations
• Confounding variables threaten causal claims and require explicit controls

Participants can use process data to identify delay, rework and control failures.

• SIPOC defines suppliers, inputs, process steps, outputs and customers
• Process maps connect activity data with handoffs, waits and decisions
• Cycle time separates touch time, queue time and elapsed time
• Funnel analysis identifies abandonment and conversion loss between process stages
• Control charts distinguish common-cause variation from special-cause signals
• Root-cause trees link operational evidence to corrective action options

Participants can identify governance, privacy and ethical controls relevant to analysis.

• Data ownership assigns accountability for definitions, quality and access decisions
• Role-based access controls restrict data according to legitimate business need
• Personal data minimisation limits fields to stated analytical purposes
• Retention schedules define disposal points for extracts and working files
• Bias assessment examines representation, measurement and decision impacts
• Audit trails record transformations, approvals, assumptions and report revisions

Participants can present analytical findings as clear, actionable business recommendations.

• Pyramid Principle structures messages from recommendation to supporting evidence
• Executive summaries state decision, evidence, impact, risks and next actions
• Insight statements distinguish observations, interpretations and recommended responses
• Evidence chains link source data, calculations, findings and requirements
• Sensitivity analysis shows how conclusions change under different assumptions
• Stakeholder objections receive documented responses, caveats and validation actions

Participants can specify an analytics deliverable with testable requirements and delivery controls.

• User stories define report users, decisions, information and acceptance criteria
• Non-functional requirements cover refresh frequency, access, performance and auditability
• Data mapping specifies source fields, transformations and target measures
• Traceability matrices connect business needs, metrics, reports and tests
• Prioritisation uses MoSCoW to sequence analytical requirements and releases
• UAT scripts verify calculations, filters, permissions and decision usefulness

Participants can build, review and present a complete data-backed decision pack for a business problem.

• Business case defines decision context, objectives, stakeholders and measurable success
• Analysis plan specifies sources, methods, assumptions, risks and validation checks
• Data-quality assessment documents material limitations and proposed corrective actions
• SQL extract and analytical model produce validated business measures
• Power BI report communicates findings through focused KPIs and drill paths
• Recommendation presentation states actions, benefits, risks, owners and next steps