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: know where business data comes from, how reliable it is and what it is worth. Identify the information your organization collects but never uses, and learn how to bring data from different teams together into one trusted view.
→Read and interpret data: spot trends, patterns and unusual results in business figures. Understand what the numbers really say before making decisions.
→Explore and predict: use simple tools to explore your data and anticipate trends and outcomes in your area of the business.
→Solve problems structurally: use proven thinking frameworks to find the real cause of a business problem and design solutions based on evidence.
→Lead transformation: build a digital-first strategy and track the KPIs that show whether change is working in your organization. A solid foundation to build on.
01
Decision Architecture
How reliable data leads to better decisions: telling real patterns from coincidence, finding the company data nobody uses and what it costs, one trusted source of truth versus data owned by each team. Case study and interactive online tasks.
02
Digital Transformation
Putting digital at the center of company strategy, developing and retraining people, and measuring progress with clear targets. Case study and interactive tasks.
03
Business Data Analysis
Hands-on workshops with real business data: using analytical tools, AI assistants and company databases to answer practical business questions
04
Transformation Simulation
Team-based online game progressing through multiple stages of digital transformation
05
Root Cause Analysis
Practical methods to find the real reason behind a business problem, from the visible symptom to its underlying source
06
Problem Solving
Simple frameworks for breaking a complex challenge into clear, practical steps based on facts and context
07
Critical Thinking
Questioning assumptions, weighing evidence and spotting the mental traps that lead to poor business 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
Short diagnostic exercises to calibrate group knowledge, energy and learning style at the start of the programme.
10
Change Management
Interactive self-diagnostic of how you react to change and how much influence you have, followed by a practical communication strategy for bringing people along.
11
Failure Management
Interactive exercise built on a real goal you missed: separate facts from interpretation, work through the emotions, then find the root causes. Includes peer review by a second person.
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 →