Applied Data Science and Advanced Analytics Training Course

Programme outline for analytical foundations

Module 1 Foundations of Applied Data Science and Business Analytics

Purpose: Establish a common understanding of analytics and formulate valuable customer and operational questions.

• Explaining data science, statistics, analytics and machine learning in accessible business language.
• Overview of descriptive, diagnostic, predictive and prescriptive analytics and the specific questions each resolves.
• The analytics lifecycle, spanning from initial business understanding through to evaluation and action.
• Converting service challenges into defined questions, outcomes, scope and success metrics.
• Data types, units of observation, time periods and fundamental data dictionaries.
• Responsible interpretation, proper data access protocols and human accountability.

Practical activity Frame a customer-service challenge, identify the underlying decision to support and draft an analysis plan.

Participant output A one-page problem definition detailing outcome measures, data requirements and assumptions.

Module 2 Preparing, Exploring and Interpreting Customer and Operational Data

Purpose: Develop a replicable approach for data preparation and deriving defensible descriptive insights.

• Comprehending dataset structure, identifiers, categories and date fields.
• Identifying missing values, duplicates, inconsistent labels and anomalies.
• Merging related records while verifying matching keys and record counts.
• Calculating counts, rates, proportions, averages, medians, percentiles and measures of dispersion.
• Comparing service categories, channels, periods and customer cohorts using correct denominators.
• Exploring distributions, trends, seasonality and exceptions through effective visualisation.
• Introductory concepts of sampling, uncertainty and the constraints of small or incomplete datasets.
• Distinguishing correlation from causation and recognising common interpretation errors.

Practical activity Prepare a service dataset, reconcile key totals and investigate variations in demand, resolution time and customer feedback.

Participant output A preparation log and an insight sheet containing findings, supporting evidence and limitations.

Programme outline for customer insight and prediction

Module 3 Understanding Customer Behaviour and Performance Drivers

Purpose Identify significant customer and service patterns and formulate explanations for investigation.

• Business-rule segmentation based on behaviour, contact frequency, service type or channel.
• Comparing customer journeys, repeat contacts, complaint categories and satisfaction trends.
• Disentangling volume effects from actual differences in service outcomes.
• Examining relationships between workload, wait times, resolution duration and repeat contacts.
• Formulating root-cause hypotheses and differentiating plausible explanations from proven causes.
• Interpreting segments cautiously to avoid unsupported generalisations.

Practical activity Compare service segments and identify one priority group or process requiring deeper investigation.

Participant output A segment profile, supporting comparisons and a prioritised list of hypotheses.

Module 4 Predictive Analytics and Forecasting for Business Outcomes

Purpose Apply selected predictive methods and evaluate their utility for business decision-making.

• Selecting between forecasting, regression and classification based on the specific question and target variable.
• Defining the prediction horizon and using only information available at that point in time.
• Preparing input variables and targets while avoiding data leakage and overfitting.
• Managing training and evaluation data, including chronological splits for time-dependent issues.
• Demand forecasting using simple baselines, moving averages and introductory smoothing techniques.
• Guided regression for numerical outcomes and basic classification for events such as repeat contacts.
• Comparing forecast or regression errors using mean absolute error and root mean squared error.
• Interpreting confusion matrices, precision and recall in the context of operational costs.
• Explaining uncertainty, model limitations, shifting patterns and the necessity for ongoing review.

Practical activity Create a demand forecast and complete a guided predictive-model exercise; compare results against a simple baseline.

Participant output A forecast or model summary documenting evaluation results, assumptions and appropriate business applications.

Programme outline for decisions and business narratives

Module 5 Data-Driven Decisions and Continuous Improvement

Purpose Convert findings into feasible improvements and define evaluation methods for their effects.

• Linking evidence to operational performance and customer-experience goals.
• Prioritising improvement opportunities based on expected value, feasibility, effort and risk.
• Utilising forecasts to inform workload and resource discussions without assuming the forecast dictates the best action.
• Comparing scenarios and testing sensitivity to key assumptions.
• Designing interventions with clear ownership, milestones and measurable success criteria.
• Defining baseline performance, outcome metrics and guardrails against unintended consequences.
• Implementing controlled pilots where feasible and interpreting before-and-after comparisons with caution.
• Applying a plan–do–check–act cycle and monitoring the sustainability of gains.

Practical activity Compare two improvement options and develop a pilot plan with measurable business and customer outcomes.

Participant output A prioritised recommendation, scenario comparison and an improvement measurement plan.

Module 6 Communicating Insights and Developing Business Recommendations

Purpose Present analysis as a clear business narrative that supports a specific decision.

• Identifying the target audience, the decision at hand and the required level of detail.
• Structuring a narrative around the business question, evidence, implications and action.
• Selecting charts, titles and annotations that accurately convey the key findings.
• Explaining uncertainty and distinguishing between observations, predictions and assumptions.
• Drafting recommendations that specify the action, rationale, owner and expected benefit.
• Preparing a concise executive briefing and responding to stakeholder scrutiny.
• Presenting the integrated case and refining recommendations based on feedback.

Practical activity Deliver a short decision briefing utilising the analysis developed throughout the programme.

Participant output A three-to-five-slide presentation and a one-page recommendation memo.