CRISP-DM Data Analytics Lifecycle and Project Delivery Training Course

Course overview

Data analytics projects often stall after an attractive dashboard, a promising model, or an initial data extract because the work was not tied to a defined business decision, measurable success criteria, data-quality evidence, or an adoption plan. CRISP-DM provides a disciplined way to connect business objectives to data preparation, analysis, evaluation, deployment and ongoing monitoring. This course helps practitioners turn fragmented analytics activity into governed, decision-ready project delivery that sponsors can understand, challenge and approve.

Participants work through the six CRISP-DM phases: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. They learn to frame analytical questions, define hypotheses and measurable acceptance criteria, create a data inventory, profile and clean data, select and validate analytical techniques, communicate limitations, and plan deployment. Practical work uses SQL, Python notebooks, Power BI and structured CRISP-DM documentation to build traceable links between a business problem, source data, analytical outputs and recommended action.

The course is delivered through instructor-led demonstrations, team workshops and a running business case involving customer retention and service performance. Participants produce a complete CRISP-DM project pack: business and analytics objectives, stakeholder map, data-quality assessment, preparation plan, analysis or model evaluation record, dashboard specification, deployment plan and monitoring measures. This gives both the participant and their manager a usable template for launching or recovering an analytics initiative.

It is best suited to analysts, data professionals, product and operational teams who already work with business data and need a repeatable delivery method rather than another tool-specific analytics course.

Course objectives

By the end of this course, participants will be able to:

• Frame a business problem as a CRISP-DM business objective, analytical objective and measurable success criteria
• Produce a stakeholder map, decision inventory and project charter for an analytics initiative
• Profile source data using SQL and Python to document completeness, validity, consistency and bias risks
• Create a data preparation plan covering joins, transformations, missing values, outliers and feature definitions
• Select and justify descriptive, diagnostic, predictive or segmentation techniques against the analytical objective
• Evaluate analytical outputs using validation measures, business acceptance criteria and model limitation statements
• Design a deployment plan that specifies dashboard ownership, refresh processes, user actions and monitoring controls
• Assemble a traceable CRISP-DM project pack for sponsor review and project handover

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to structure analysis work from business question through to operational use
• Business Intelligence Analysts — who must connect reporting requirements to source-data evidence and decision outcomes
• Data Scientists — who need a repeatable framework for scoping, validating and deploying analytical models
• Analytics Managers — who govern project portfolios, stakeholder expectations and delivery quality
• Product Managers — who use customer and product data to prioritise measurable improvements
• Operations Managers — who need to sponsor analytics projects that improve service, cost or process performance

Requirements and prerequisites

Participants should have practical experience working with business data in spreadsheets, SQL, a BI tool or a programming environment, and should understand basic concepts such as tables, fields, joins, descriptive statistics, KPIs and data visualisation. Familiarity with Python or Jupyter notebooks is useful because exercises include guided code, but participants do not need to write advanced programs or build machine-learning models from scratch. No prior CRISP-DM experience is required. This is not a beginner course in data literacy; complete beginners should first gain confidence reading datasets, interpreting charts and using basic spreadsheet formulas.

Training methodology

The instructor introduces each CRISP-DM phase using a worked customer-retention case, then demonstrates the related techniques in SQL, Python notebooks and Power BI. Participants apply the method in small delivery teams, reviewing business briefs, profiling supplied datasets, documenting preparation decisions and presenting evidence to a simulated project sponsor. Facilitated critique focuses on traceability between objectives, data, analysis and action. Each day adds a section to the project pack, and the final day includes a deployment review and an individual application plan for a live workplace initiative.

Course outline

Day 1: CRISP-DM foundations and business understanding

• The six CRISP-DM phases and iterative delivery loops
• Business objectives versus analytical objectives
• Decision statements and measurable business success criteria
• Problem framing with issue trees and hypothesis statements
• Stakeholder mapping and decision-rights analysis
• Project scope boundaries, assumptions and constraints
• CRISP-DM project charter structure

Workshop: Participants convert a customer-retention brief into a project charter with stakeholders, decisions, objectives, assumptions and measurable success criteria.

Day 2: Data understanding and discovery

• Data inventory and source-system mapping
• Entity, field and grain identification
• Data dictionaries and business definitions
• Exploratory data analysis in Jupyter Notebook
• SQL queries for record counts and distribution checks
• Data-quality dimensions and profiling measures
• Initial data-risk and bias assessment

Workshop: Participants profile a multi-table customer dataset and produce a data inventory, quality scorecard and initial findings log.

Day 3: Data preparation planning and execution

• Analytical dataset design and unit-of-analysis selection
• SQL joins, filters and aggregation patterns
• Missing-value treatment options
• Outlier detection and treatment rules
• Duplicate records and entity-resolution controls
• Derived fields, feature definitions and transformation lineage
• Reproducible preparation workflows in Python

Workshop: Participants build and document an analysis-ready customer dataset, including joins, cleansing rules, derived measures and lineage notes.

Day 4: Descriptive and diagnostic analytics

• KPI design linked to business objectives
• Segmentation methods and cohort analysis
• Trend, seasonality and variance analysis
• Funnel analysis and customer journey measures
• Root-cause investigation using drill-down logic
• Correlation interpretation and confounding factors
• Power BI measures and visual diagnostic design

Workshop: Participants create a diagnostic Power BI report and present evidence-based explanations for changes in customer retention.

Day 5: Analytical technique selection and modelling

• Matching analytical methods to question types
• Baseline methods and benchmark selection
• Classification, regression and clustering use cases
• Train, validation and test dataset design
• Feature selection and leakage prevention
• Interpreting model outputs for business users
• Model documentation and reproducibility requirements

Workshop: Participants select an appropriate retention-analysis approach and produce a modelling plan with baselines, features, validation design and risks.

Day 6: Evaluation and decision readiness

• Technical performance measures and threshold setting
• Precision, recall, lift and confusion matrices
• Business acceptance criteria and cost-of-error analysis
• Sensitivity testing and scenario analysis
• Fairness, bias and representativeness checks
• Limitations, uncertainty and caveat statements
• Evaluation reports and sponsor decision gates

Workshop: Participants evaluate sample analytical results against agreed criteria and prepare a sponsor-facing evaluation report with a go, revise or stop recommendation.

Day 7: Insight communication and dashboard deployment

• Decision-focused data storytelling
• Executive narrative structures and recommendation framing
• Power BI dashboard requirements and wireframing
• Visual encoding, accessibility and chart selection
• KPI thresholds, alerts and exception reporting
• Data refresh schedules and ownership models
• User acceptance criteria for analytical products

Workshop: Participants create a dashboard wireframe and executive briefing that specifies user actions, KPI definitions, alert thresholds and acceptance criteria.

Day 8: Operational deployment and governance

• Deployment options for reports, models and analytical datasets
• Operational process mapping and decision integration
• Data governance roles and stewardship responsibilities
• Privacy, access control and retention considerations
• Model and dashboard monitoring measures
• Change management and user adoption planning
• Incident management and rollback procedures

Workshop: Participants produce a deployment and governance plan identifying owners, controls, refresh cycles, user training needs and escalation routes.

Day 9: Project management across the CRISP-DM lifecycle

• Work breakdown structures for analytics delivery
• Iterative planning and phase-gate reviews
• Effort estimation for data acquisition and preparation
• Risk registers for data, modelling and adoption risks
• Dependency management with business and technology teams
• Requirements traceability matrices
• Status reporting and sponsor governance packs

Workshop: Participants build a delivery plan for the case study, including milestones, risks, dependencies, review gates and a requirements traceability matrix.

Day 10: End-to-end CRISP-DM project delivery

• Integrating artefacts across all six CRISP-DM phases
• Quality review of project evidence and traceability
• Sponsor challenge questions and decision defence
• Lessons-learned capture and lifecycle iteration
• Workplace project selection criteria
• Ninety-day analytics implementation planning
• Personal CRISP-DM practice roadmap

Workshop: Participants present their complete CRISP-DM project pack to a simulated sponsor panel and produce a 90-day application plan for a workplace analytics project.

Tools & standards covered

CRISP-DM, Microsoft Power BI, Jupyter Notebook, PostgreSQL

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions