Management 3.0 Workshop

After trying a couple of times to get into a Management 3.0 training I finally ha the chance to participate in a two day course in Nuremberg.


We training was hosted at Ancud IT in downtown Nuremberg. The quiet working environment was awesome, the catering for the coffee breaks incredibly good. Kudo cards to the Ancud team, but more about Kudo Cards later.

The Training

Jürgen Mohr works as an independent agile coach / scrum master and Management 3.0 trainer. He used the M3.0 approach in a couple of projects and could tell a lot of stories from his work experience.

So what is Management 3.0?

The overall question is: “What does a manager still do in a complex system with self-organizing teams?”

The Mindset of Management 3.0 tries to answer this question with “Manage the system not the people”

An organization is a complex adaptive system

The inventor of M3.0 Jurgen Appello built his concept around six views of agile management

  • energize people
  • empower teams
  • align constraints
  • develop competence
  • grow structure
  • improve everything

The Team

Saskia, Carsten and I had to form a team and find a name which represents our commonalities. We came up with #YORO – You only retire once, because we all considered working less at the age of fifty 🙂

We had to choose the team values from the values list:

We came up with

  • Openess
  • Agility
  • Trust
  • Innovation

The Tools

We tried out a lot of different tools. Jürgen set up a Kudo wall onto which we could put the Kudo cards to praise the behavior of our colleagues.

The Books

There are two must reads: Management 3.0 and Managing for happiness. While the first one is the scientific the other is the “playbook”, it comprises all the tools you can directly use in practice.

Additionally You can read Daniel Pink “Drive”

My Experiments so far

  • First Kudo Card given to a colleague and our HR team
  • Personal Maps as part of a team building workshop
  • Moving Motivators in an hiring interview


Classification: Precision and Recall

In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean?


Living together with little kids You very often run into classification issues:

My daughter really likes dogs, so seeing a dog is something positive. When she sees a normal dog e.g. a Labrador and proclaims: “Look, there is a dog!”

That’s a True Positive (TP) Continue reading “Classification: Precision and Recall”

Lesson 4: Decision Trees

from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(min_samples_split=40), labels_train)

UD120 – Intro to Machine Learning

One part of my bucket list for 2018 was finishing the Udacity Course UD120: Intro to Machine Learning.

the host of this course are Sebastian Thrun, ex-google-X and founder of Udacity and Katie Malone, creator of the Linear digressions podcast.

The course consists of 17 lessons. Every lesson has a couple of hours of video and lots and lots of quizzes in it.

  • [x] Lesson 1: Only introduction 🙂
  • [x] Lesson 2: Naive Bayes
  • [x] Lesson 3: Support Vector Machines
  • [x] Lesson 4: Decision Trees
  • [x] Lesson 5: Choose your own algorithm
  • [ ] Lesson 6: Datasets and questions
  • [ ] Lesson 7: Regression
  • Lesson 8: Outliers
  • Lesson 9: Clustering
  • Lesson 10: Feature Scaling
  • Lesson 11: Text Learning
  • Lesson 12: Feature Selection
  • Lesson 13: PCA
  • Lesson 14: Validation
  • Lesson 15: Evaluation Metrics
  • Lesson 16: Tying it all together
  • Lesson 17: Final project