KNIME Data Science Workflow Automation Training Course

Course overview

Data science teams often lose time rebuilding the same preparation, modelling and reporting steps for each new data extract. Spreadsheet hand-offs, unversioned scripts and manually run notebooks make results difficult to reproduce, audit or operationalise. This five-day KNIME Data Science Workflow Automation Training Course equips practitioners to replace fragmented analysis processes with visual, reusable KNIME workflows that can be scheduled, parameterised, monitored and shared across a team.

Participants build end-to-end workflows in KNIME Analytics Platform, from connecting to files, databases and APIs through data profiling, transformation, modelling, validation and output generation. They learn to use nodes, metanodes, components, flow variables and configuration dialogs to create maintainable workflow assets. The course also covers model evaluation, Python integration, PMML model exchange, error handling, workflow logging, data-quality checks and deployment patterns using KNIME Business Hub.

Teaching combines instructor demonstrations with guided builds based on realistic operational data problems, including customer churn, sales forecasting and data-quality remediation. Each participant progressively develops an automated analytical workflow with documented inputs, reusable components, validation rules, model outputs and automated reporting steps. They leave with a portfolio-ready KNIME workflow package and a practical deployment plan showing how it can be moved from desktop development into a controlled team or production environment.

The course is designed for analysts, data scientists, BI professionals and automation-minded technical staff who already work with structured data and need a reliable way to turn repeatable analysis into governed workflows. Managers benefit from a clear route to reducing manual data preparation, standardising analytical methods and improving confidence in recurring data-driven decisions.

Course objectives

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

• Build end-to-end KNIME workflows that ingest, transform, analyse and publish structured data
• Configure database, file and REST API connections using KNIME connector and reader nodes
• Create reusable metanodes and components with configuration dialogs for repeatable analytical tasks
• Apply flow variables, table manipulation and rule-based logic to parameterise workflow execution
• Develop classification and regression pipelines using KNIME preprocessing, learner and predictor nodes
• Evaluate model performance with partitioning, cross-validation, ROC curves and scoring metrics
• Integrate Python scripts and exchange deployable models using PMML where appropriate
• Deploy and schedule governed workflows through KNIME Business Hub with logging and error controls

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to automate recurring preparation, analysis and reporting workflows
• Data Scientists — who want to operationalise repeatable model-building pipelines without rebuilding code
• Business Intelligence Developers — who combine data from multiple sources before producing trusted outputs
• Analytics Engineers — who need visual, parameterised workflows that can be shared and governed
• Data Operations Specialists — who maintain scheduled data processes and need stronger validation controls
• Technical Business Analysts — who translate business rules into transparent, reusable data workflows

Requirements and prerequisites

Participants should be comfortable working with tabular data and understand common concepts such as rows, columns, data types, joins, filters, missing values and basic descriptive statistics. Experience using Excel, SQL, Python, R, a BI tool or another analytics environment is useful, because the course moves quickly into workflow design and model evaluation. No prior KNIME experience is required, and participants do not need advanced programming, machine learning theory or DevOps experience. Familiarity with their organisation’s data sources and a typical recurring analytical task will help them apply the course directly.

Training methodology

The course is delivered through short instructor-led demonstrations followed by sustained hands-on work in KNIME Analytics Platform. Participants build workflows node by node, inspect intermediate tables, diagnose failures and compare alternative transformation and modelling approaches. Case exercises use realistic customer, sales and operational datasets rather than isolated feature demonstrations. Small-group reviews focus on component design, data-quality rules and workflow readability. On day five, participants complete an application-planning workshop that maps their workflow to a live business process, including inputs, owners, schedule, controls and deployment considerations.

Course outline

Day 1: KNIME workflow foundations and data access

• KNIME Analytics Platform interface, workflow editor and node repository
• Workflow execution states, node configuration and intermediate table inspection
• CSV, Excel and database reader nodes for structured data ingestion
• Database Connector and Database Query nodes for SQL-based access
• Column filtering, type conversion and missing-value handling
• Joiner, Concatenate and GroupBy nodes for dataset assembly
• Workflow annotations, node naming and layout conventions for maintainability

Workshop: Build a documented customer-data preparation workflow that combines CRM, transaction and reference-data extracts into a validated analysis table.

Day 2: Reusable transformation and workflow control

• Rule Engine and Column Expressions nodes for business-rule implementation
• String Manipulation, Date&Time Shift and Math Formula transformations
• Pivoting, unpivoting and aggregation patterns for analytical datasets
• Metanodes for encapsulating repeated transformation logic
• Components, configuration nodes and reusable workflow interfaces
• Flow variables and variable-driven node settings
• Try-Catch, empty-table handling and workflow error-management patterns

Workshop: Create a parameterised data-quality component that applies configurable validation rules and produces an exceptions report.

Day 3: Predictive analytics workflows in KNIME

• Analytical problem framing and target-variable selection
• Partitioning and cross-validation strategies for model assessment
• Numeric Binner, One to Many and Normalizer preprocessing nodes
• Decision Tree Learner, Random Forest Learner and Logistic Regression Learner
• Regression Predictor and classification prediction workflows
• Scorer, ROC Curve and Lift Chart evaluation nodes
• Feature selection, leakage checks and reproducible model comparison

Workshop: Develop and evaluate a customer churn classification workflow, then select and document the preferred model using defined metrics.

Day 4: Integration, outputs and operational automation

• Python Script node integration for specialised analytical logic
• Python environment configuration and input-output table exchange
• PMML Writer and PMML Predictor for portable model exchange
• REST Client nodes for consuming external data services
• Excel Writer, CSV Writer and database writer output patterns
• Report generation using KNIME views and output tables
• Workflow logging, execution monitoring and data-lineage documentation

Workshop: Extend a model-scoring workflow with a REST data input, Python enrichment step and automated Excel and database outputs.

Day 5: Deployment, governance and workflow application

• KNIME Business Hub concepts, spaces and team workflow sharing
• Deployment packaging and workflow dependency management
• Scheduling recurring workflow executions and parameterised runs
• Credentials configuration and secure connection handling
• Workflow versioning, review practices and release controls
• Production monitoring, failure notifications and recovery procedures
• Automation opportunity assessment and workflow operating-model design

Workshop: Package the completed workflow for deployment and produce a one-page automation plan covering schedule, owners, controls, outputs and success measures.

Tools & standards covered

KNIME Analytics Platform, KNIME Business Hub, Python, PMML

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