Data Science for Business Analysts Training Course

Participants can select an appropriate analytical approach and define team responsibilities.

• Descriptive, diagnostic, predictive and prescriptive analytics compared against decision needs
• Supervised and unsupervised learning distinguished through labelled outcome availability
• Analyst, data scientist, engineer and product owner accountabilities clarified
• Analytical backlog structured using user stories, acceptance criteria and dependencies
• Build-versus-buy decisions assessed for models, platforms and external data
• Model lifecycle stages defined from discovery through retirement and audit

Participants can specify the data needed to answer an analytical question.

• Source systems classified as transactional, operational, survey, web and external
• Data requirements matrix linking fields, grain, refresh rate and owners
• Entity relationships mapped between customers, accounts, events and transactions
• Event timestamps assessed for ordering, latency, timezone and reporting cutoffs
• Data lineage documented from source extraction through analytical dataset creation
• Sampling frames defined to prevent unsupported population-level conclusions

Participants can assess dataset fitness using documented data quality tests.

• Completeness, validity, uniqueness, consistency, timeliness and accuracy quality dimensions
• Column profiling calculates null rates, distinct counts, ranges and distributions
• Business rules translated into executable data validation checks
• Duplicate records investigated using natural keys, surrogate keys and matching rules
• Outliers separated into errors, exceptions and valid rare observations
• Data quality scorecards prioritise defects by decision impact and remediation effort

Participants can construct SQL queries that produce analysis-ready datasets.

• SELECT, WHERE and ORDER BY queries isolate relevant analytical records
• INNER, LEFT and FULL joins combine related business entities correctly
• GROUP BY aggregations calculate customer, product and period-level measures
• CASE expressions derive business categories and indicator variables
• Window functions calculate rankings, rolling values and customer histories
• Common table expressions organise multi-step transformations for review

Participants can use Python notebooks to document and reproduce analytical work.

• Jupyter notebooks combine code, narrative, outputs and review comments
• Pandas DataFrames load, inspect and transform tabular datasets
• Reusable functions reduce repeated cleaning and calculation logic
• Markdown cells document assumptions, definitions and analytical decisions
• Versioned files distinguish raw data, prepared data and outputs
• Exception handling identifies failed imports, invalid types and missing columns

Participants can prepare consistent model inputs from raw business data.

• Missing values treated through deletion, imputation and explicit missingness flags
• Categorical variables encoded using one-hot, ordinal and target encoding
• Numerical features scaled using standardisation and min-max normalisation
• Date fields transformed into recency, frequency, duration and seasonality measures
• Leakage checks prevent future information entering training features
• Feature definitions recorded in a data dictionary and transformation log

Participants can identify meaningful patterns and anomalies before modelling.

• Distribution plots reveal skewness, spread, multimodality and improbable values
• Cross-tabulations compare outcomes by customer, channel, product and region
• Correlation analysis identifies linear relationships and multicollinearity warnings
• Scatterplots examine relationships, clusters and influential observations
• Cohort analysis tracks behaviour by acquisition month and customer tenure
• Segment comparisons distinguish statistically notable differences from operationally material findings

Participants can interpret statistical evidence without overstating conclusions.

• Population, sample, parameter and statistic distinguished using business examples
• Confidence intervals communicate plausible ranges around estimated measures
• Hypothesis tests compare groups using null and alternative statements
• P-values interpreted alongside effect size, sample size and decision risk
• Type I and Type II errors linked to operational consequences
• Correlation and causation separated through confounders, controls and study design

Participants can present analytical evidence clearly for business decisions.

• Chart selection matches comparison, composition, distribution, relationship and trend questions
• Power BI visuals apply titles, annotations, reference lines and accessible labels
• Small multiples compare segments without misleading dual-axis charts
• Executive narratives state decision, evidence, uncertainty and recommended action
• Visual encodings checked for scale distortion, truncation and colour accessibility
• Dashboard requirements define audience, refresh cadence, filters and ownership

Participants can build and interpret regression models for numeric business outcomes.

• Linear regression estimates continuous outcomes such as demand, spend and duration
• Dependent and independent variables defined from the analytical question
• Regression coefficients interpreted with units, direction and practical significance
• Residual plots test linearity, variance patterns and unusual observations
• MAE, RMSE and R-squared compared for prediction accuracy
• Baseline mean predictions establish minimum acceptable model performance

Participants can assess classification models for binary decision support.

• Binary classification predicts outcomes such as churn, fraud and default
• Logistic regression produces probability scores for defined target events
• Confusion matrices separate true positives, false positives and missed cases
• Precision, recall, specificity and F1 score reflect decision costs
• ROC curves and AUC compare ranking performance across thresholds
• Threshold selection balances intervention capacity, losses and customer impact

Participants can compare interpretable tree models with higher-performing ensembles.

• Decision trees split records using features that reduce impurity
• Tree depth controls overfitting and supports understandable decision rules
• Random forests combine multiple trees to improve prediction stability
• Feature importance identifies influential inputs but does not prove causation
• Cross-validation estimates performance across repeated training and test partitions
• Model selection considers accuracy, interpretability, maintenance and approval requirements

Participants can create and assess useful groups from unlabelled customer data.

• K-means clustering groups records by similarity across selected variables
• Feature scaling prevents large-unit variables dominating cluster assignments
• Elbow and silhouette methods assess plausible cluster numbers
• Cluster profiles describe needs, value, behaviour and service requirements
• Segment labels avoid stereotypes and unsupported causal claims
• Operational actions connect segment definitions to channels, offers and service models

Participants can produce and evaluate short-term forecasts from historical data.

• Time series components separate level, trend, seasonality and irregular variation
• Naive and seasonal-naive forecasts establish performance baselines
• Moving averages smooth short-term variation for operational planning
• Exponential smoothing weights recent observations for changing demand patterns
• Train-test splits preserve time order and prevent future data contamination
• MAPE, MAE and bias assess forecast error and directional drift

Participants can assess whether an intervention caused an observed business outcome.

• A/B tests define treatment, control, primary metric and guardrail metrics
• Randomisation reduces selection bias between intervention and comparison groups
• Sample size planning considers effect size, power and decision urgency
• Experiment results report uplift, confidence intervals and practical impact
• Difference-in-differences estimates change when randomisation is not feasible
• Selection bias identified in observational comparisons and campaign response data

Participants can identify governance requirements for business analytics and models.

• UK GDPR principles applied to analytical purpose, minimisation and retention
• Personal data, special category data and pseudonymised data distinguished
• Fairness assessments compare outcomes across relevant protected groups
• Model cards record purpose, data, performance, limitations and approvals
• Human oversight requirements defined for high-impact recommendations and decisions
• Risk registers capture privacy, bias, security, operational and reputational concerns

Participants can specify operational requirements for deploying and monitoring a model.

• Batch and real-time scoring compared against decision timing requirements
• Deployment specifications define inputs, outputs, thresholds and exception handling
• Data drift monitoring detects changed input distributions after deployment
• Concept drift reviews identify declining relationships between features and outcomes
• Performance dashboards track accuracy, calibration, usage and business value
• Retraining triggers define evidence, approval steps and rollback procedures

Participants can convert model evidence into an actionable stakeholder recommendation.

• Recommendation memos distinguish evidence, assumptions, risks and requested decisions
• Decision trees map model outputs to operational actions and owners
• Cost-benefit analysis compares intervention costs with expected outcome value
• Sensitivity analysis tests recommendations under changed assumptions and volumes
• Implementation roadmaps sequence data, process, technology and governance activities
• Executive challenge sessions address uncertainty, exceptions and accountability

Participants can build, review and present a complete data science project for a business decision.

• CRISP-DM project brief defines decision, stakeholders, measures and constraints
• Prepared dataset documents source lineage, quality findings and feature definitions
• Exploratory analysis identifies patterns requiring business interpretation and validation
• Candidate models compared using baseline, performance, fairness and usability criteria
• Implementation proposal specifies actions, controls, monitoring and accountable owners
• Executive presentation defends findings, limitations, recommendations and next decisions