Data Analysis for Managers Training Course

Participants can assess whether data is suitable, permitted and reliable for a stated decision.

• Data lifecycle controls from collection and storage through retention and disposal
• Data dictionaries defining fields, formats, permitted values, owners and meanings
• Data lineage tracing source systems, transformations, extracts and reporting outputs
• GDPR principles: purpose limitation, minimisation, accuracy, retention and accountability
• Role-based access controls for confidential customer, employee and financial datasets
• Quality dimensions: completeness, validity, accuracy, consistency, timeliness and uniqueness

Participants can specify, extract and combine data needed for a management analysis.

• Source-system assessment covering transaction tables, spreadsheets, APIs and third-party files
• SQL SELECT, WHERE, ORDER BY and LIMIT for targeted extracts
• INNER and LEFT JOIN logic checked against key cardinality
• GROUP BY aggregations with COUNT, SUM, AVG and HAVING filters
• Sampling plans balancing representativeness, cost, timeliness and operational disruption
• Extraction specifications documenting population, fields, dates, filters and refresh frequency

Participants can clean, structure and document datasets for repeatable analysis.

• Structured tables, named ranges and data types for reliable analysis
• Text-to-columns, TRIM and CLEAN functions for inconsistent source fields
• Missing-value treatments using deletion, imputation, flags and business-rule review
• Duplicate detection through composite keys, conditional formatting and record counts
• Outlier screening with sorted distributions, IQR rules and domain thresholds
• Power Query steps for repeatable imports, merges, pivots and refreshes

Participants can identify meaningful patterns, exceptions and questions requiring further investigation.

• Frequency tables and cross-tabulations revealing category patterns and exceptions
• Distribution plots selecting histograms, box plots and density displays appropriately
• Segment comparisons by region, channel, product, customer and time period
• Pareto analysis ranking contributors to defects, costs, complaints or delays
• Time-series plots exposing trends, seasonality, shifts and unusual observations
• Analytical notebooks recording questions, transformations, findings and unresolved issues

Participants can select and interpret summary statistics appropriate to business data.

• Mean, median and mode selected for appropriate business distributions
• Range, variance, standard deviation and IQR interpreted for variability
• Percentiles and quartiles used to set service, risk and performance thresholds
• Weighted averages avoiding distortion from unequal volumes, values or exposures
• Rates, ratios and index numbers standardising comparisons across changing populations
• Correlation coefficients interpreted without claiming causation or managerial certainty

Participants can design clear visual outputs that direct attention to material performance issues.

• Chart selection matched to comparison, composition, distribution, relationship and trend questions
• Axis scales, baselines and labels checked for misleading visual claims
• Small multiples exposing performance differences across comparable segments and periods
• Power BI data models linking fact tables, dimensions and measures
• DAX measures calculating ratios, variances, year-to-date and rolling metrics
• Dashboard wireframes prioritising decisions, alerts, drill-through paths and action owners

Participants can define performance measures that link strategic aims to operational action.

• Metric trees connecting strategic outcomes, drivers, measures and operational actions
• Leading and lagging indicators distinguished for controllable performance management
• KPI definitions specifying formula, grain, source, owner and refresh schedule
• Targets set using baselines, benchmarks, capacity constraints and risk appetite
• Balanced scorecard perspectives linking financial, customer, process and capability measures
• Control limits separating normal variation from signals requiring intervention

Participants can judge whether samples support credible conclusions about a wider population.

• Population, sample frame and unit definitions preventing invalid survey inferences
• Random, stratified and systematic sampling methods compared for business studies
• Sample-size drivers: confidence level, margin of error, variability and population
• Confidence intervals communicating estimated ranges rather than false point precision
• Sampling bias, nonresponse bias and survivorship bias diagnosed before reporting
• Margin-of-error statements tailored to survey, audit and customer research audiences

Participants can test whether observed differences are credible and managerially meaningful.

• Null and alternative hypotheses written for measurable operational claims
• Type I and Type II errors assessed against decision consequences
• t-tests comparing group means with assumptions, effect sizes and confidence intervals
• Chi-square tests assessing associations between categorical variables and outcomes
• A/B test designs defining randomisation, guardrails, exposure windows and success metrics
• p-values interpreted alongside practical significance, power and multiple-testing risks

Participants can use regression and time-series methods to estimate drivers and future outcomes.

• Simple linear regression relating a continuous outcome to one predictor
• Multiple regression models incorporating drivers while checking multicollinearity and residuals
• Logistic regression estimating event probabilities for binary operational outcomes
• Regression coefficients translated into conditional business implications and limitations
• Moving averages and exponential smoothing forecasting stable demand series
• Forecast accuracy measured with MAE, RMSE, MAPE and bias

Participants can group cases and assess classification methods for targeted management action.

• RFM analysis grouping customers by recency, frequency and monetary value
• Rule-based segmentation combining eligibility criteria, needs and service priorities
• Cluster analysis concepts: distance, scaling, cluster profiles and validation
• Decision trees identifying interpretable splits for classification and intervention
• Confusion matrices reporting accuracy, precision, recall, specificity and false alarms
• Model monitoring plans tracking drift, performance, fairness and retraining triggers

Participants can diagnose performance variation and identify evidence-based process improvement priorities.

• Process maps linking workflow steps, handoffs, queues, systems and measures
• Cycle-time analysis separating touch time, wait time and rework
• Control charts detecting special-cause variation in operational performance
• Root-cause analysis using fishbone diagrams, five whys and stratified evidence
• Funnel analysis locating conversion losses across customer or case stages
• Before-and-after measures controlling for seasonality, mix changes and concurrent interventions

Participants can communicate analytical findings in a form that supports clear management decisions.

• Claim-evidence-action structure connecting findings to accountable management decisions
• Executive summaries stating decision, evidence strength, recommendation, risks and next steps
• Data annotations explaining material changes, drivers, exceptions and uncertainty
• Scenario analysis comparing base, upside and downside assumptions transparently
• Recommendation registers assigning owner, deadline, measure and expected benefit
• Challenge sessions testing conclusions against alternative explanations and stakeholder knowledge

Participants can plan and govern an analytical project from business question through implementation.

• CRISP-DM phases organising business understanding, preparation, modelling, evaluation and deployment
• Analytical project charters defining scope, milestones, roles, dependencies and acceptance criteria
• RACI matrices clarifying accountability for data, analysis, decisions and actions
• Risk registers covering privacy, quality, bias, security and adoption failures
• Reproducibility checklists preserving source versions, transformation logic and calculation audit trails
• Benefits-realisation plans measuring adoption, savings, service and risk outcomes

Participants can build, review and present a complete management analysis with actionable recommendations.

• Capstone brief translating a live business issue into analytical requirements
• Data preparation log documenting sources, cleaning decisions, assumptions and limitations
• Exploratory analysis selecting segments, comparisons, trends and exception tests
• Statistical or predictive technique justified against the stated decision question
• Decision dashboard and executive narrative presenting evidence, recommendations and ownership
• Peer review challenging validity, interpretation, ethics and implementation feasibility
• Presentation defence responding to management questions, trade-offs and requested follow-up