KDD Process for Data Analytics Project Delivery Training Course

Course overview

Data analytics projects often fail before modelling begins: the business question is vague, source data is poorly understood, transformation choices are undocumented, and teams cannot explain how a dashboard, model or recommendation was produced. The Knowledge Discovery in Databases (KDD) process provides a disciplined delivery method for moving from raw organisational data to validated, usable knowledge. This course helps analysts and project teams apply KDD as a repeatable project framework, reducing rework and making analytical outputs easier for stakeholders to trust and act upon.

Participants work through the full KDD lifecycle: defining discovery objectives, selecting relevant data, profiling quality and fitness for purpose, cleaning and transforming datasets, applying data-mining methods, interpreting patterns, and presenting findings for operational use. They gain practical skills in SQL extraction, Python-based profiling and preparation, exploratory analysis, feature construction, classification and clustering, evaluation metrics, and traceable documentation. Particular attention is given to the hand-offs between KDD stages, where analytical projects commonly lose context, controls and stakeholder alignment.

Delivery combines instructor-led method briefings with a sustained business case based on customer, transaction and service data. Each day adds a controlled artefact to a project evidence pack, including a problem statement, data inventory, profiling report, transformation log, modelling record, evaluation plan and deployment recommendation. By the end of the course, participants leave with a completed KDD project portfolio and a practical delivery plan they can adapt to a live analytics initiative.

The course is designed for professionals who already work with data and need a stronger method for delivering analysis, not simply producing one-off queries or visualisations. It suits individual analysts seeking defensible project practice and managers who need consistent analytical governance across teams.

Course objectives

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

• Frame an analytics problem as a KDD discovery objective with measurable business success criteria
• Select and document source datasets using a data inventory, inclusion rationale and lineage record
• Profile dataset structure, completeness, validity and outliers using SQL and Python
• Clean and transform analytical data while maintaining a reproducible transformation log
• Engineer and justify features suitable for segmentation, classification and pattern discovery
• Apply clustering and classification methods to a defined KDD analytical question
• Evaluate analytical results using appropriate metrics, validation checks and business interpretation
• Produce a KDD project evidence pack containing findings, limitations, deployment actions and monitoring measures

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need a repeatable method for turning raw data into defensible business findings
• Business Intelligence Analysts — who must connect reporting requests to sound data selection and interpretation
• Analytics Project Managers — who need stage gates, artefacts and controls for data-led project delivery
• Data Scientists — who need to document discovery, preparation and evaluation work for business stakeholders
• Database Developers — who prepare analytical extracts and need to understand downstream KDD requirements
• Product and Operations Analysts — who use customer or process data to recommend measurable operational changes

Requirements and prerequisites

Participants should be comfortable working with tabular data and should understand basic spreadsheet concepts, including rows, columns, filters, joins and calculated fields. Prior exposure to SQL SELECT statements and basic descriptive statistics, such as averages, distributions and percentages, is expected. Participants should also be able to read simple Python code; experience writing pandas or scikit-learn code is helpful but not essential because guided notebooks are provided. A laptop capable of running a modern web browser and, where permitted, Python notebooks is required. Prior machine-learning expertise, advanced programming, calculus and database administration are not required.

Training methodology

The course uses short instructor-led KDD method sessions followed by guided work in SQL and Python notebooks. Participants progress through one connected case study, making decisions about source selection, quality issues, transformations, analytical techniques and business recommendations as a project team would. Facilitated reviews use sample stakeholder challenges, data-quality exceptions and model results to test the reasoning behind each decision. Daily workshops produce named KDD artefacts, and the final day includes peer review and an application-planning session for adapting the method to a participant’s own data analytics project.

Course outline

Day 1: KDD foundations and analytics project framing

• Knowledge Discovery in Databases lifecycle and stage dependencies
• Distinguishing business questions from data-mining tasks
• Discovery objectives and measurable success criteria
• Analytical hypotheses, assumptions and constraints
• Stakeholder roles in KDD project delivery
• KDD stage gates and decision records
• Project evidence pack structure

Workshop: Participants convert a business request into a KDD project charter containing objectives, scope, stakeholders, assumptions and success measures.

Day 2: Data selection and source understanding

• Source-system mapping for analytical projects
• Data inventory and metadata capture
• Entity, event and transaction grain
• SQL extraction design and join logic
• Population definition and sampling decisions
• Data lineage from source to analytical dataset
• Selection bias and coverage risk

Workshop: Participants build a data inventory and SQL-based source selection plan for the course case study.

Day 3: Data profiling and quality assessment

• Structural profiling with pandas DataFrame inspection
• Completeness, validity, uniqueness and consistency checks
• Frequency distributions and descriptive statistics
• Outlier detection using range and distribution checks
• Duplicate record identification
• Missing-data mechanisms and treatment options
• Data quality issue register

Workshop: Participants profile a supplied dataset in Jupyter Notebook and produce a prioritised data quality issue register.

Day 4: Data cleaning and transformation control

• Data type standardisation and parsing rules
• Missing-value imputation and exclusion criteria
• Deduplication and record survivorship rules
• Categorical value normalisation
• Date, time and numerical transformations
• Transformation logs and reproducible code
• Pre- and post-transformation validation

Workshop: Participants clean and transform the case dataset, producing versioned code and a transformation log with validation results.

Day 5: Feature construction and exploratory discovery

• Feature engineering linked to discovery objectives
• Aggregation at customer, product and time-period levels
• Ratio, recency, frequency and monetary features
• Encoding categorical variables
• Scaling and normalisation choices
• Exploratory data analysis with visual diagnostics
• Feature dictionary and business definitions

Workshop: Participants create an analytical feature set and feature dictionary for segmentation and service-risk analysis.

Day 6: Pattern discovery through segmentation

• Unsupervised learning in the KDD process
• K-means clustering assumptions and workflow
• Selecting candidate clustering variables
• Choosing cluster numbers with inertia and silhouette scores
• Cluster profiling and descriptive labels
• Detecting unstable or non-actionable segments
• Translating segments into business actions

Workshop: Participants build, compare and profile clustering solutions, then recommend an actionable segmentation approach.

Day 7: Predictive discovery and classification

• Classification tasks and target-variable definition
• Training, validation and test data partitions
• Decision trees for interpretable prediction
• Logistic regression for binary outcomes
• Class imbalance and resampling considerations
• Confusion matrices and classification metrics
• Feature importance and explanatory caution

Workshop: Participants develop and compare two classification models, recording assumptions, metrics and interpretation notes.

Day 8: Evaluation, interpretation and risk checks

• Technical versus business evaluation criteria
• Accuracy, precision, recall and F1 score selection
• Overfitting and cross-validation checks
• Sensitivity analysis for key assumptions
• Bias, fairness and proxy-variable review
• Data leakage and temporal validation
• Findings limitations and confidence statements

Workshop: Participants complete an evaluation report that selects a preferred approach and documents limitations, risks and validation evidence.

Day 9: Knowledge presentation and deployment design

• Converting analytical output into decision-ready insight
• Evidence-led storytelling for non-technical stakeholders
• Recommendation prioritisation and expected value
• Dashboard and report specification principles
• Operational deployment options for analytical outputs
• Monitoring measures and model performance drift
• Ownership, controls and review cadence

Workshop: Participants prepare a stakeholder briefing and deployment recommendation with actions, owners, monitoring metrics and decision thresholds.

Day 10: KDD project integration and workplace application

• End-to-end KDD artefact review
• Traceability from objective to recommendation
• Peer challenge of data and modelling decisions
• KDD delivery risks and mitigation planning
• Adapting KDD to agile analytics delivery
• Workplace project selection criteria
• Ninety-day KDD application roadmap

Workshop: Participants present their completed KDD evidence pack and produce a ninety-day plan for applying the method to a workplace analytics project.

Tools & standards covered

Python, Jupyter Notebook, PostgreSQL, scikit-learn

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