CRISP-DM Data Science Project Lifecycle Training Course

Course overview

Data science projects often stall after an impressive prototype because the team has not agreed the business question, defined a usable target variable, documented data limitations, or set acceptance criteria for deployment. CRISP-DM provides a repeatable lifecycle for moving from a decision problem to a governed analytical solution. This course helps professionals use the method to prevent scope drift, connect modelling work to measurable business value, and make project decisions traceable to stakeholders, data owners, and delivery teams.

Participants work through all six CRISP-DM phases: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. They learn to frame analytical objectives, create success criteria, profile data quality, select and justify features, establish baselines, compare models, interpret evaluation metrics, identify operational risks, and plan monitoring. The course also addresses the iteration points that distinguish real projects from linear textbook workflows, including revisiting business objectives when data evidence changes.

Instructor-led explanations are paired with a running case study, structured templates, data-analysis exercises in JupyterLab, and facilitated project reviews. Teams develop a CRISP-DM project pack containing a business objective statement, data inventory, preparation plan, experiment log, model evaluation record, deployment plan, and monitoring measures. Participants leave with a practical set of artefacts they can adapt for an active data science initiative and a completion certificate.

The course is designed for data professionals and project stakeholders who already understand the basic language of data, analytics, and machine learning but need a disciplined method for delivering data science work in a business setting.

Course objectives

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

• Frame a business problem as a measurable CRISP-DM analytical objective with success criteria and constraints
• Produce a data understanding report covering source lineage, data quality, distributions, gaps, and risks
• Design a data preparation plan that documents cleaning, joining, feature engineering, and leakage controls
• Build and record a reproducible modelling experiment using baselines, train-test splits, and versioned assumptions
• Select evaluation metrics that connect model performance to business cost, benefit, and decision thresholds
• Conduct a CRISP-DM evaluation review that tests technical validity, business fitness, fairness, and operational readiness
• Create a deployment and monitoring plan covering ownership, refresh cycles, drift indicators, and retraining triggers
• Compile a CRISP-DM project pack that supports stakeholder approval, handover, and future auditability

Benefits of attending

Target competencies

Who should attend

• Data Scientists — who need a disciplined framework for converting exploratory work into deployable, business-approved solutions
• Data Analysts — who support analytical projects and need to define data, metrics, and decision requirements rigorously
• Machine Learning Engineers — who must translate model outputs into reliable deployment, monitoring, and retraining plans
• Analytics Managers — who govern portfolios of data science initiatives and need consistent stage gates and evidence
• Data Product Managers — who must align user needs, business value, data constraints, and release decisions
• Project Managers — who coordinate data science delivery and need realistic lifecycle artefacts, risks, and acceptance criteria

Requirements and prerequisites

Participants should be comfortable working with tabular data and understand basic analytical concepts such as variables, data types, descriptive statistics, training data, test data, and model performance metrics. Experience using spreadsheets, SQL, Python, or a business intelligence tool is helpful; the practical exercises use JupyterLab and Python notebooks, so attendees should be able to follow simple code cells and inspect outputs. Prior exposure to a machine learning project is beneficial but not essential. Advanced programming, calculus, cloud engineering, MLOps platforms, and prior knowledge of CRISP-DM are not required.

Training methodology

The five-day programme uses short instructor-led sessions to introduce each CRISP-DM phase, followed by guided work on a realistic predictive analytics case. Participants profile data in JupyterLab, complete lifecycle templates, review model evidence, and make documented go/no-go decisions in small groups. Facilitated discussions examine trade-offs such as accuracy versus business cost, incomplete data versus scope revision, and prototype performance versus deployment readiness. On the final day, each participant adapts the method to a current or proposed workplace initiative and receives feedback on their project pack.

Course outline

Day 1: Business Understanding and CRISP-DM initiation

• The six CRISP-DM phases and iterative project loops
• Business problem statements versus analytical problem statements
• Stakeholder mapping and decision-owner identification
• Analytical objectives, hypotheses, and target-variable definition
• Business success criteria and technical success criteria
• Project constraints, assumptions, dependencies, and risks
• CRISP-DM project charter and stage-gate structure

Workshop: Participants convert a sponsor brief into a CRISP-DM project charter with objectives, stakeholders, constraints, and measurable success criteria.

Day 2: Data Understanding and preparation design

• Data source inventory, ownership, and lineage mapping
• Data profiling with JupyterLab and pandas
• Distribution analysis, missingness, duplicates, and outliers
• Data quality dimensions and fitness-for-purpose assessment
• Exploratory analysis for target leakage and sampling bias
• Data dictionaries and feature-level documentation
• Preparation plans for cleaning, joins, transformations, and splits

Workshop: Participants profile a case-study dataset and produce a data understanding report with quality findings, risks, and a prioritised preparation plan.

Day 3: Modelling and experiment management

• Baseline models and the value of simple benchmarks
• Feature engineering hypotheses and transformation choices
• Training, validation, and test-set design
• Cross-validation and reproducible random-state controls
• Model selection criteria and hyperparameter search boundaries
• Experiment logs, assumptions, and result traceability
• Git-based version control for notebooks and project artefacts

Workshop: Participants build a baseline and candidate model in JupyterLab, then document the comparison in a structured experiment log.

Day 4: Evaluation, approval, and responsible use

• Classification and regression metric selection
• Confusion matrices, threshold setting, and error trade-offs
• Linking model performance to cost, benefit, and intervention capacity
• Business evaluation against original success criteria
• Bias, fairness, explainability, and data protection considerations
• Model limitations, failure modes, and decision boundaries
• Evaluation review meetings and go/no-go recommendations

Workshop: Teams conduct an evaluation review, calculate threshold trade-offs, and present a justified go/no-go recommendation to a sponsor panel.

Day 5: Deployment, monitoring, and workplace application

• Deployment patterns for batch scoring, APIs, dashboards, and decision support
• Production data contracts and input validation controls
• Model monitoring for performance, drift, and data-quality change
• Retraining triggers, review cycles, and model retirement criteria
• Operational ownership, support procedures, and escalation routes
• MLflow tracking concepts for model and experiment traceability
• CRISP-DM closure, lessons learned, and next-project planning

Workshop: Participants complete a deployment and monitoring plan, then assemble and peer-review their full CRISP-DM project pack for a workplace use case.

Tools & standards covered

CRISP-DM, JupyterLab, Git, MLflow

A typical training day

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