Teaching

Interactive learning
programmes

Hands-on courses combining theory with simulations, instructor-led exercises, quizes, interactive tests, demos and real-world case studies.

01 / MBA

01.

Digital Transformation

Digital Transformation is a revenue-generating game. The course teaches how to read data, build strategy, and implement solutions at scale, for managers, directors and MBA students who are non-technical domain experts looking to transform their domains. A series of lectures mixed with interactive tests, quizzes and a simulation game, engaging all participants throughout.

Main rule of the course: Slides are only to inspire discussions.

MBA: Digital Transformation
~20h instructor-led workshops & presentations
~20h self-study
3 days core course
+3 days simulation game · 4 weeks
Language
Polish · English
Format
In-person (preferred) · Online
Audience
University · Corporate
Group size
20–25 participants

Used at

After this course participants will

→Understand data: sources, quality, value and integration context. Know the difference between dark data, data mesh and single sources of truth.
→Read and interpret data: find correlations, patterns and anomalies in business data. Understand what the numbers actually say before making decisions.
→Explore and predict: use data exploration tools and basic predictive approaches to anticipate trends and outcomes in their domain.
→Solve problems structurally: apply root cause analysis and critical thinking frameworks to diagnose issues and design data-driven solutions.
→Lead transformation: build a digital-first strategy and drive transformation KPIs in their organization. This is a foundation, not a deep dive.
01
Decision Architecture
Data Mesh vs SSoT, correlations, dark data, case study, interactive online tasks
02
Digital Transformation
Digital First, upskilling/reskilling, transformation KPIs, case study, interactive tasks
03
Business Data Analysis
Applications, models, agents with hands-on business data engineering workshops and databases
04
Transformation Simulation
Team-based online game progressing through multiple stages of digital transformation
05
Root Cause Analysis
Structured methods to diagnose business and data problems, from symptom to source
06
Problem Solving
Frameworks for breaking complex challenges into actionable steps using data and context
07
Critical Thinking
Questioning assumptions, evaluating evidence, avoiding cognitive traps in data-driven decisions
08
Transformation Game (coming soon)
4-week online simulation: 625 unique scenario paths. Each student navigates a different story through Diagnosis, Decision, Implementation and Settlement.
Game brief PDF ↗
09
Ice-breaker Aptitude Tests (coming soon)
Short diagnostic exercises to calibrate group knowledge, energy and learning style at the start of the programme.
Enquire about this course →

02 / Data Engineering

02.

Observability Foundations MAP

Interactive visual lectures on data engineering and observability, designed for university-level students and conference audiences. Covers observability as a mindset, Log-Driven Development, data correlation, anomaly detection and alert fatigue.

Main rule of the course: You don't need a monitoring tool. You need to understand what you're looking at.

~20h instructor-led workshops & presentations
~20h self-study
Language
Polish · English
Format
In-person (preferred) · Online
Audience
University
Group size
20–25 participants

Used at

After this course participants will

→Understand observability as a mindset: know how to design software systems that are observable from the ground up, not just monitored after the fact.
→Implement Log-Driven Development (LDD): design log outputs before writing code, ensuring every significant event is traceable and meaningful in production.
→Apply data engineering to observability: use clustering, binning, histograms and regression to analyse system logs, find correlations and detect anomalies.
→Detect and interpret spikes and failures: identify peaks, outliers and failure patterns in time-series telemetry data using mathematical approaches.
→Address alert fatigue: design alerting thresholds that surface real signals and reduce noise, keeping on-call teams effective and focused.
→Build a foundation for SRE practice: use observability data for structured root cause analysis and faster incident resolution. This is a foundation, not a deep dive.
01
Observability as Mindset
What observability really means: designing systems to be understood, not just monitored
02
Log-Driven Development
Design log outputs before writing code, making every event traceable and meaningful in production
03
Data Correlation
Connecting log entries across systems to build a baseline for data-driven decisions
04
Clustering & K-Means
Grouping log patterns to surface hidden failure clusters and recurring anomalies
05
Bins & Histograms
Visualising time-based data distributions to understand system behaviour over time
06
Regression & Trends
Linear regression on production metrics: reading what your p99 is telling you before it becomes an incident
07
Peaks, Spikes & Anomalies
Mathematical approach to detecting outliers in telemetry data: traffic surges, memory leaks, I/O bursts
08
Alert Fatigue
Designing alert thresholds that surface real signals and keep on-call teams effective
Enquire about this course →