Case-driven training

Data and AI workflow courses built around real scenarios

Practical courses that lead participants through full workflows with example datasets, reproducible notebooks and operational checklists. Learn by implementing cases relevant to your business context.

Active cases 12+

Hours of labs 40+

120

Participants trained

Corporate workshops

Practical scenarios delivered

Hands-on cases

Learn Data and AI workflows through scenarios and cases

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DataNowKurs focuses on converting theoretical methods into reproducible workflow patterns. Each lesson maps to a practical artifact — a pipeline, notebook or deployment manifest — that teams can reuse.

DataNowKurs courses prioritize scenario-based learning: participants work through end-to-end workflows that reflect real operational constraints. Training sessions include concrete case descriptions, sample data schemas, step-by-step lab guides and reference implementations. Labs cover data ingestion strategies, schema evolution handling, feature stores, model validation techniques and lightweight deployment options suitable for Swiss regulatory contexts. Throughout the course, learners document decisions, create reproducible scripts and run tests that emulate production monitoring. Trainers present multiple variants of each case to illustrate activity-offs: for example, batch inference versus online serving, versioned features for reproducibility, or different approaches to model explainability when auditability is required. After hands-on labs, teams receive a practical checklist and extension plan to adapt the workflow to their own systems and policies. This method ensures that participants leave with artifacts and actionable next steps rather than abstract concepts.

End-to-end pipelines

Build a minimally viable pipeline from data collection to serving with monitoring hooks and roll-back procedures.

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Feature engineering at scale

Hands-on labs demonstrate feature versioning, lineage tracking and performance testing using realistic datasets.

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Operational readiness

Operational checklists and CI patterns included so teams can evaluate readiness for production deployments.

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Schedule a briefing

Book a workshop or request a proposal

Provide a brief summary of your case and constraints. DataNowKurs will propose a case-driven agenda and timeline tailored to your team's needs.

Address: Blattacker 12, 4612 Wangen bei Olten, Switzerland

Phone: +41768142407

Business ID: CHE-554.154.011 — Date: 04-01-2026

Request a training proposal

Share a concise description of your challenge and the outcomes you seek

Hands-on Cases

Data & AI Workflow Courses — case-driven, practical, job-ready

DataNowKurs designs each module around real operational scenarios: from data acquisition and transformation to model deployment and monitoring. We focus on concrete workflows used in Swiss and international teams, with step-by-step case studies that demonstrate activity-offs, failure modes and recoverable patterns.

Real-world Case Studies

Each lesson centers on a reproducible case: data integration from manufacturing sensors, end-to-end ETL for management, model validation in regulated contexts. Learners reproduce results and adapt approaches to their own data.

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End-to-end Pipelines

Practical walkthroughs cover orchestration, versioned datasets, CI/CD for models and automations that keep pipelines reliable under change. Emphasis is on observable, debuggable workflows.

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Tool-agnostic Practices

We teach patterns that apply across tools: how to structure experiments, manage metadata, and design resilient data contracts so you can apply techniques with your preferred stack.

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Operational Readiness

Lessons include runbooks, monitoring scenarios and incident post-mortems so teams can transition prototypes into production with clear checks and actionable playbooks.

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Case-driven learning

Learn by doing with scenario-based modules

DataNowKurs courses are structured as progressive scenarios: you start by reproducing a compact working pipeline, then extend it to handle scale, noisy data and business constraints. Each module includes a documented problem statement, sample datasets, stepwise labs and a short scenario-based assessment. Example cases include building a demand forecasting pipeline from point-of-sale logs, constructing an anonymized customer analytics dataset while preserving privacy constraints, and deploying a model inference service with A/B experiment logging. Instructors highlight common pitfalls and provide alternative designs so learners can evaluate activity-offs and choose approaches suitable for their context.

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Scenario Labs

Playbooks

Assessments

Instructors & Practitioners

Experts with production experience in data engineering, MLOps and analytics — teaching via scenarios and documented case studies. Company registered: DataNowKurs, Blattacker 12, 4612 Wangen bei Olten, Switzerland. Business ID CHE-554.154.011. Date reference: 16-03-2026.

Liam Carter

Lead Data Engineer

Liam brings experience building resilient data platforms for logistics and manufacturing. He leads hands-on labs on ingestion, schema evolution and observability using real dataset scenarios.

Amelia Rossi

MLOps Architect

Amelia focuses on reliable model delivery and monitoring. Her sessions walk learners through deployment scenarios, experiment tracking and post-deployment validation.

Noah Müller

Analytics Engineering

Noah teaches data modeling and transformation patterns using concrete business cases: revenue analytics, cohort analysis and reproducible BI pipelines.

Sofia Dubois

Product & Data Strategy

Sofia frames technical work with product scenarios and compliance considerations. Her case studies show how to align data workflows with stakeholder needs and regulatory constraints.

Case-based

Turn data projects into operational workflows through hands-on scenarios

Join a focused course at DataNowKurs that walks you through a complete workflow: data ingestion, transformation, model training, validation, deployment and monitoring. Each session centers on a practical case with artifacts you can reuse.

120+Practical cases completed by learners

45Hours of guided labs per specialization

32Companies whose workflows were modeled in course cases