Data Science for Business Professionals Training Course

Participants can convert a business problem into a testable analytical requirements statement.

• SMART decision statements specifying owner, action, timing and constraints
• Target variables, prediction horizons and unit-of-analysis choices
• Classification, regression, clustering and forecasting problem distinctions
• Success criteria combining technical metrics with operational measures
• Assumptions register documenting causal beliefs, exclusions and dependencies
• Acceptance criteria for analytical outputs, users and business actions

Participants can identify required data sources, owners, access routes and handling constraints.

• Source-system inventories covering transactions, CRM, sensors and external datasets
• Data lineage traced from operational capture through analytical consumption
• Primary keys, foreign keys and joins across relational tables
• Data ownership roles using RACI responsibility assignments
• Access controls based on least privilege and approved purposes
• Data requirements specifications listing fields, grain, history and refresh frequency

Participants can assess dataset fitness and specify defensible preparation steps for analysis.

• Data quality dimensions: accuracy, completeness, consistency, timeliness and validity
• Profiling distributions, distinct values, null rates and duplicate records
• Missing-data mechanisms: MCAR, MAR and MNAR distinctions
• Outlier detection using IQR rules, z-scores and domain thresholds
• Standardising dates, currencies, categories and measurement units
• Data preparation logs recording transformations, rationale and data-loss effects

Participants can use SQL queries to extract, join, aggregate and validate analytical datasets.

• SELECT, WHERE and ORDER BY for targeted record retrieval
• INNER JOIN and LEFT JOIN applied to customer and transaction tables
• GROUP BY aggregations using COUNT, SUM, AVG and conditional logic
• CASE expressions for business rules and derived categories
• Window functions calculating rankings, running totals and cohort measures
• SQL validation checks reconciling extracted totals with source reports

Participants can use a Python notebook to load, inspect, transform and summarise data.

• Jupyter Notebook cells, kernels, markdown and reproducible execution order
• Pandas DataFrames loaded from CSV files and database extracts
• Filtering, sorting and selecting columns using Pandas syntax
• Merge and groupby operations for combined business datasets
• Feature creation from dates, categories, ratios and historical behaviour
• Notebook annotations explaining assumptions, decisions and analytical limitations

Participants can summarise data distributions and identify patterns relevant to business decisions.

• Mean, median and mode chosen for appropriate distribution shapes
• Range, variance, standard deviation and interquartile range interpretation
• Skewness and long tails in claims, balances and transaction values
• Percentiles and quantiles for service levels and customer thresholds
• Cross-tabulations revealing patterns across segments, channels and regions
• Sampling bias identified through population coverage and response patterns

Participants can interpret uncertainty, confidence intervals and common statistical test results.

• Probability concepts expressed through events, conditional probability and base rates
• Normal, binomial and Poisson distributions matched to business questions
• Confidence intervals for estimated means, proportions and differences
• Null and alternative hypotheses stated before analysis begins
• p-values interpreted alongside effect size and practical significance
• Type I and Type II errors linked to decision consequences

Participants can conduct structured exploratory analysis to generate and test useful hypotheses.

• Univariate analysis using histograms, box plots and frequency tables
• Bivariate relationships examined with scatter plots and grouped summaries
• Correlation matrices interpreted without claiming causation
• Segment comparisons using rates, averages and distribution differences
• Time-series plots revealing seasonality, trends and structural breaks
• Exploration checklist separating observations, hypotheses and confirmed findings

Participants can design decision-focused visualisations and narratives for non-technical audiences.

• Chart selection matched to comparison, composition, distribution and relationship questions
• Bar, line, scatter and box plots used appropriately
• Axes, scales and labels designed to prevent misleading interpretation
• Reference lines showing targets, benchmarks, control limits and uncertainty
• Annotation techniques highlighting exceptions, drivers and recommended actions
• Data narratives structured around question, evidence, implication and decision

Participants can select an appropriate predictive modelling approach and establish a baseline.

• Supervised learning distinguished from unsupervised learning and rule-based automation
• Training, validation and test sets separated to prevent leakage
• Baseline models established using averages, majority classes and simple rules
• Feature engineering linked to available decision-time information
• Overfitting and underfitting diagnosed through training and validation performance
• Model selection balanced across accuracy, explainability, cost and latency

Participants can interpret classification model outputs and set decision thresholds using business costs.

• Logistic regression probabilities interpreted for churn, fraud and default decisions
• Decision trees explained through splits, paths and feature importance
• Confusion matrices showing true positives, false positives and missed cases
• Precision, recall, specificity and F1 score compared by use case
• ROC curves and AUC used for ranking model discrimination
• Threshold selection based on intervention capacity and error costs

Participants can assess regression and forecasting outputs for planning and resource decisions.

• Linear regression coefficients interpreted with units and practical meaning
• Residual plots used to identify systematic prediction errors
• MAE, RMSE and MAPE compared for forecast accuracy
• Moving averages and exponential smoothing for baseline demand forecasts
• Seasonality, holidays and promotions treated as forecasting features
• Forecast intervals communicated for inventory, staffing and budget decisions

Participants can evaluate segmentation approaches and translate groups into differentiated actions.

• Customer segmentation objectives linked to service, risk and retention actions
• K-means clustering based on scaled numerical features
• Cluster count assessed using inertia, silhouette scores and usability
• Segment profiles described through behaviours, value and needs
• Dimensionality reduction explained using principal component analysis
• Segmentation governance preventing prohibited or unjustified customer treatment

Participants can design and interpret controlled tests for interventions and product changes.

• Randomised controlled trials separating treatment effects from selection bias
• Control and treatment groups defined with eligibility rules
• Primary metrics, guardrail metrics and stopping rules specified
• Sample size considerations linked to detectable effect and decision risk
• A/B test results interpreted using confidence intervals and practical impact
• Quasi-experimental methods introduced through difference-in-differences logic

Participants can identify ethical, legal and operational risks and propose proportionate controls.

• Fairness concerns assessed across protected groups and outcome disparities
• Bias sources traced to sampling, labels, features and historical decisions
• Privacy principles applied to collection, retention and data minimisation
• Explainability requirements matched to consequential automated decisions
• Model risk registers covering assumptions, limitations, owners and controls
• Human oversight points defined for exceptions, appeals and escalation

Participants can define deployment requirements and monitoring measures for operational models.

• Batch, real-time and decision-support deployment patterns compared
• Model input checks validating schema, ranges and missing values
• Performance monitoring tracking accuracy, calibration and business outcomes
• Data drift and concept drift detected through distribution comparisons
• Champion-challenger approaches supporting controlled model replacement
• Incident response procedures covering rollback, communication and remediation

Participants can assess suitable generative AI uses while applying validation and confidentiality controls.

• Generative AI tasks separated from predictive modelling and search
• Prompt structures specifying role, context, constraints and output format
• Retrieval-augmented generation explained for grounded organisational knowledge
• Hallucination risks controlled through source checking and human review
• Confidential data handling assessed before external tool use
• Evaluation criteria covering accuracy, relevance, safety and consistency

Participants can present analytical recommendations with evidence, limitations, ownership and next actions.

• Executive briefings structured around decision, evidence, value and requested action
• Model cards documenting purpose, data, performance and known limitations
• Decision logs recording approvals, exceptions and accountable owners
• Uncertainty communicated through ranges, scenarios and confidence statements
• Technical detail tiered for executives, users, analysts and reviewers
• Implementation roadmaps sequencing pilots, controls, adoption and measurement

Participants can build, review and present a complete data science business case, analysis and implementation recommendation.

• Business problem statement linked to a measurable decision and value case
• Data inventory, quality assessment and preparation rationale documented
• Exploratory analysis used to support prioritised business hypotheses
• Model or analytical method selected against stated success criteria
• Performance, fairness and implementation risks reviewed with stakeholders
• Executive presentation delivering recommendation, roadmap, controls and measures