Incubator Summer Academy - Next Level Data ScienceOnline Event

18 Sept 2023, 09:00 → 29 Sept 2023, 17:00 Europe/Berlin

 

Once more, the five Helmholtz incubator platforms Helmholtz AI, Helmholtz Imaging, HIFIS, HIDA and HMC have teamed up to create another edition of the Incubator Summer Academy - Next Level Data Science!

We have designed a joint program with a variety of course packages covering state of the art data science methods and skills, as well as networking opportunities in our Summer Academy Gathertown space!

Ranging from fundamental course packages such as “Python” or “Introduction to Scienctific Metadata” to advanced topics like “Machine Learning Based Image analysis”, the program offers participants the opportunity to select course packages that best suit their experience level and interest.

The Incubator Summer Academy is open to all doctoral and postdoctoral researchers in the Helmholtz Association. Additionally, a small number of seats are reserved for Master students, doctoral and postdoctoral researchers from other research institutions and universities.

As a kick off, Dr.Dirk Werth from the August-Wilhelm Scheer Institut will give a keynote on "Could AI become the better scientist? Understanding how digital science impacts our life in science, education and business" (Abstract) during our opening event on September 18th (9-10am). 

 

Registration is now closed. 

For those who have registered for one or multiple course packages: 

1. After registration, you will have received a registration notice via e-mail. Please note that this is NOT yet a confirmation.
2. If you are selected as a participant for the course package, you will be notified about ten days to one week prior to the Incubator Summer Academy.
3. Please make your own calendar entries so that you do not miss the course package you registered for.
4. The event will take place in Gathertown. The link to our Incubator Summer Academy Gathertown space will be shared with you shortly before the start of the event.

Questions?

Write us at hida-courses@helmholtz.de

CP12recording_GMT20230918-080945_Recording_1920x1440.mp4

CP12StatisticalLearning_20230919T072810Z-001.zip

timetable_all.pdf

timetable_auditorium.pdf

timetable_room_0.pdf

timetable_room_1.pdf

timetable_room_2.pdf

Monday 18 September

General

1 Opening event

In the opening event, Christian Beilmann (HIDA coordinator) and Otmar D. Wiestler (President Helmholtz Association) will first welcome everyone to the 2nd Incubator Summer Academy (9 - 9:15am).

Then, Dirk Werth from the August-Wilhelm Scheer Institut will give a keynote on "Could AI become the better scientist? Understanding how digital science impacts our life in science, education and business." (9:15 - 10am).

Keynote abstract:
Some people say: AI is the new big data and digitalization is just another kind of automation technologies. But they neglect the fact that in recent years, society has encountered the beginning of a massive transformational process that is mainly induced by digitalization and especially AI and that also affects the science domain. In this talk, Dirk Werth will provide interdisciplinary insights on working mechanisms of digitalization and how AI demands new problem solving approaches. Based on real examples, the presentation will indicate the upcoming and partially already ongoing shift of behavior in various domains, such as science, education and business, and how to prepare and benefit from the new possibilities.

Speakers: Christian Beilmann (HIDA coordinator), Dirk Werth (AWS Institut), Otmar D. Wiestler (President Helmholtz Association)

Course Package 1 (HIDA/ HIFIS): Reproducibility in Science

2 Introduction to Open Science and Reproducibility

In this talk the Helmholtz Open Science Office will focus on digital reproducibility and its importance for open and robust science.
Research is reproducible when it is possible to (independently) recreate the same results from the same data and same code/analysis as used by the original researcher or team of researchers. Reproducibility enhances collaboration and transparency in science and supports reusability of scientific products. This closely links with the open science endeavor towards the cultural change in science and science communication.
To truly enable reuse underlying data sets of research results are published in a FAIR
manner for instance. The FAIR principles can be transferred to research software with some adaptations and also define important prerequisites in the context of reproducibility in order to be able to reproduce results. On the other hand, doing actual reproduction attempts (meaning success / failure) need to be an integral part of scholarly communication and should be incentivized accordingly.

Speaker: Antonia Schrader (Helmholtz Open Science Office)

2023-09-18_Incubator-Summer-Academy_Schrader.pdf

3 Lecture: Reproducibility in Science

The lecture will introduce the topic of reproducibility in science: what is
reproducibility, why does it matter and why is to hard to achieve? I will
discuss abstract requirements for reproducibility and try connect these to
concrete measures we can take in day-to-day research to make our (computational)
results more reproducible. The potential of Jupyter Notebooks and the Jupyter
ecosystem is explored.

Speaker: Hans Fangohr (MPSD)

Lecture: Reproducibility in Science (Hans Fangohr)

Course Package 3 (HIFIS): Python

4 Introduction to Python (Part 1)

We get to know our programming tool and develop a fundamental understanding how writing Python code works.
Supervised exercises allow to consolidate the new knowledge.

Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)

Course Package 12 (Helmholtz AI): Introduction to Statistical Learning

5 Lecture: Introduction to Statistical Learning (Part 1)

The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear discriminant analysis (generative model)
● Nonlinear classifiers with Ensemble learning: Decision trees, random forests, boosting
● Unsupervised learning: Gaussian mixture models, k-means
Our course aims to provide participants with not only a theoretical
foundation, but also the practical skills needed to use and develop
effective machine learning solutions to a wide variety of problems. We
illustrate the use of the models in the tutorial throughout the course
with methods implemented in Python.

Speaker: Tingying Peng (Helmholtz Munich)

6 Tutorial: Statistical Learning (Part 1)

The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear discriminant analysis (generative model)
● Nonlinear classifiers with Ensemble learning: Decision trees, random forests, boosting
● Unsupervised learning: Gaussian mixture models, k-means
Our course aims to provide participants with not only a theoretical
foundation, but also the practical skills needed to use and develop
effective machine learning solutions to a wide variety of problems. We
illustrate the use of the models in the tutorial throughout the course
with methods implemented in Python.

Speaker: Alaa Bassadok (Helmholtz Munich)

7 Lecture: Introduction to Statistical Learning (Part 2)

The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear discriminant analysis (generative model)
● Nonlinear classifiers with Ensemble learning: Decision trees, random forests, boosting
● Unsupervised learning: Gaussian mixture models, k-means
Our course aims to provide participants with not only a theoretical
foundation, but also the practical skills needed to use and develop
effective machine learning solutions to a wide variety of problems. We
illustrate the use of the models in the tutorial throughout the course
with methods implemented in Python.

Speaker: Tingying Peng (Helmholtz Munich)

8 Tutorial: Statistical Learning (Part 2)

The course package covers foundations and recent advances of machine learning techniques, including:
● Basic concepts: Linear regression, nearest neighbour, parametric vs.
non-parametric methods, Bayesian classifiers, the curse of
dimensionality, model accuracy, bias-variance trade-off
● Linear classifiers: linear regression for classification (discriminative
model), linear discriminant analysis (generative model)
● Nonlinear classifiers with Ensemble learning: Decision trees, random forests, boosting
● Unsupervised learning: Gaussian mixture models, k-means
Our course aims to provide participants with not only a theoretical
foundation, but also the practical skills needed to use and develop
effective machine learning solutions to a wide variety of problems. We
illustrate the use of the models in the tutorial throughout the course
with methods implemented in Python.

Speaker: Alaa Bessadok (Helmholtz AI)

12:30 Lunch break

Tuesday 19 September

Course Package 3 (HIFIS): Python

9 Introduction to Python (Part 2)

We get to know additional code structures and language features and learn how to apply them for solving various problems.
Supervised exercises offer additional problems to gather experience with the learned skills.

Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)

Course Package 8 (Helmholtz AI): Introduction to Machine Learning

10 Introduction to Machine Learning (Part 1)

This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Jiangtao Wang (FZ Jülich), Peter Steinbach (HZDR), Sebastian Starke (HZDR), Steve Schmerler (HZDR)

collaborative notes

learner pack (.zip)

General: Meet the Helmholtz Incubator Platforms

11 Meet the Helmholtz Incubator Platforms

Get to know the Helmholtz Incubator Platforms and see what they can do and mean for you! Next to posters, presentations, and discussions by and with the platforms, you can also meet fellow participants of the Summer Academy. Do not miss out on the opportunity to expand your network!

2023-09_HMC_Poster_ISA.pdf

Helmholtz-Imaging_Info-Poster_A1_General_07092023.pdf

HIDA_Online_Flyer.pdf

HIFIS_overview_en.pdf

hifis-overview-software.pdf

OnePager_Helmholtz AI.pdf

12:30 Lunch break

Wednesday 20 September

Course Package 3 (HIFIS): Python

12 Introduction to Pandas

An introduction to the popular data processing framework.
Hands-on-exercises allow to solidify the gained knowledge.

Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)

Course Package 8 (Helmholtz AI)

13 Introduction to Machine Learning (Part 2)

This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Jiangtao Wang (FZ Jülich), Peter Steinbach (HZDR), Sebastian Starke (HZDR), Steve Schmerler (HZDR)

collaborative notes

learner pack (.zip)

12:30 Lunch break

Thursday 21 September

Course Package 3 (HIFIS): Python

14 Introduction to Matplotlib

Learn how to use the popular plotting framework to generate graphs from data sets.
Additional exercises allow to gather experience with different kinds of visualizations..

Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)

Course Package 8 (Helmholtz AI): Introduction to Machine Learning

15 Introduction to Machine Learning (Part 3)

This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Jiangtao Wang (FZ Jülich), Peter Steinbach (HZDR), Sebastian Starke (HZDR), Steve Schmerler (HZDR)

collaborative notes

learner pack (.zip)

Course Package 1 (HIDA/ HIFIS): Reproducibility in Science

16 Repro hack

The ReproHack reproducibility hackathon is a hands-on event where you can practice reproducible research with real-world data and research software (R or Python). We will suggest two scientific publicatios from the ReproHack repository (www.reprohack.org) with publicly available code and data you can choose from. During the event, you aim to reproduce the scientific results (figures and statistical results) detailed in the published paper. Feel free to either join the one of the three teams: Beginners (re-run the original R code), Advanced (re-implemented the R code in a Python Jupyter notebook), or Experts (implement the analysis in a Docker container). The ReproHack will give you the opportunity to understand the importance of careful documentation of the entire analysis workflow. You will learn from other researchers from different domains and levels of experience.

Speakers: Anja Eggert (FBN Dummerstdorf), Frank Krüger (HS Wismar), Max Schroeder (University of Rostock)

12:30 Lunch break

Friday 22 September

Course Package 8 (Helmholtz AI): Introduction to Machine Learning

17 Introduction to Machine Learning (Part 4)

This course will introduce participants to the concepts of AI and Machine Learning, covering clustering and clasifications fundamentals as well as practical experience with standard methods for both techniques. Lastly, participants will gain an insight on best practises for evaluating a machine learning model's performance (ROC curve, FPR etc.)

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Jiangtao Wang (FZ Jülich), Peter Steinbach (HZDR), Sebastian Starke (HZDR), Steve Schmerler (HZDR)

collaborative notes

learner pack (.zip)

Course Package 9 (Helmholtz AI): Introduction to Interpretable Machine Learning

18 Lecture: Fairness in machine learning

// Level: BEGINNER ///
In this talk we survey the role of machine learning methods in questions of social justice and discrimination. First, we take a bird’s eye view on which domains may be particularly affected, how machine learning can sustain or even promote inequalities, and whether there are also opportunities for ML to help reduce or prevent discrimination in practice. Via a deep dive into automated data-driven decision-making in consequential scenarios, we learn about the interactions of technical aspects with societal questions and introduce a broader perspective of the life-cycle of ML methods. Throughout, we try to give concrete examples of ML models arguably acting “unfair” and try to distill potential mindsets and techniques to avoid such failure modes in the future.

Speaker: Niki Kilbertus

Course Package 7 (Helmholtz Imaging): Machine Learning- based Image Analysis

19 Lecture: Introduction to Machine Learning- based Image Analysis

The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We will end the package with a hands on tutorial in which we go step by step through using nnU-Net from training to visualization of the resulting 3D-segementation maps.

Speaker: Paul Jäger (Helmholtz Imaging, DKFZ)

20 Lecture: Common Pitfalls in ML-based Image Analysis

The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We will end the package with a hands on tutorial in which we go step by step through using nnU-Net from training to visualization of the resulting 3D-segementation maps.

Speaker: Paul Jäger (Helmholtz Imaging, DKFZ)

21 Lecture: Applications of AI in Medical Imaging

The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We will end the package with a hands on tutorial in which we go step by step through using nnU-Net from training to visualization of the resulting 3D-segementation maps.

Speaker: Lukas Klein (Helmholtz Imaging, DKFZ)

22 Tutorial: nnU-Nnet: A self-configuring image segmentation method

The success of Machine Learning has revolutionized the field of medical image analysis in the past 5 years. This talk will give an introduction to relevant concepts in machine learning with a focus on computer vision. Subsequently, several example applications in the biomedical domain will be discussed to study the current state of research and the associated challenges and opportunities. We will end the package with a hands on tutorial in which we go step by step through using nnU-Net from training to visualization of the resulting 3D-segementation maps.

Speaker: Kim-Celine Kahl (Helmholtz Imaging, DKFZ)

12:30 Lunch break

Course Package 9 (Helmholtz AI): Introduction to Interpretable Machine Learning

23 Course Package 9 (Helmholtz AI): Introduction to eXplainable AI

During this course participants will get an introduction to the topic of Explainable AI (XAI). The goal of the course is to help participants understand how XAI methods can help uncover biases in the data or provide interesting insights. After a general introduction to XAI, the course goes deeper into state-of-the-art model agnostic interpretation techniques as well as a practical session covering these techniques. Finally, we will focus on two model specific post-hoc interpretation methods, with hands-on training covering interpretation of random forests and neural networks with imaging data to learn about strengths and weaknesses of these standard methods used in the field.

Speakers: Donatella Cea, Elisabeth Georgii (Helmholtz Zentrum München), Florian Kofler (HAI), Francesco Campi, Harshavardhan Subramanian, Helena Pelin, Lisa Barros de Andrade e Sousa (Helmholtz AI), Mahyar Valizadeh (Helmholtz AI consultacy health unit), Sabrina Benassou, Theresa Willem

Course Pad

GitHub repo

XAI_Introduction_Summer_Academy_2023.pdf

Monday 25 September

Course Package 4 (HIFIS): Version Control & Project Management

24 Introduction to Git and GitLab (Part 1)

We get to know the basics of version control with Git. The live coding approach allows you to directly apply your new knowledge in practice.

Speaker: Tobias Schlauch (DLR / HIFIS)

Course Package 10 (Helmholtz AI): Introduction to Deep Learning

25 Introduction to Deep Learning (Part 1)

This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.

The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning can be somewhat intimidating.

We start with explaining the basic concepts of neural networks, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic Deep Learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Peter Steinbach (HZDR), Steve Schmerler (HZDR)

collaborative notes

12:30 Lunch break

Tuesday 26 September

Course Package 4 (HIFIS): Version Control and Project Management

26 Introduction to Git and GitLab (Part 2)

We get to know the basics of GitLab as well as how to combine Git, GitLab issues and GitLab merge requests to form an effective contribution workflow. The live coding approach and the team exercise allow you to directly apply your new knowledge in practice.

Speaker: Tobias Schlauch (DLR / HIFIS)

Course Package 6 (HMC): Introduction to Scientific Metadata

27 Fundamentals of Scientific Metadata (Part 1)

You will learn:

• about the differences between and the importance of data & metadata
• to annotate your research data with structured metadata
• to find and evaluate a suitable metadata framework and data repository
• to use basic Markdown / JSON / XML
• which tools are already available to level up your metadata annotation game
• why structured metadata is important and how it can increase your scientific visibility

organized by HMC Hub Information & HMC Office

Speakers: Silke Gerlich (HMC Hub Information), Mirl Trösch (HMC Office)

BreakoutRoomMaterial

• Handout - Group 1
• Handout - Group 2
• Handout - Group 3

Course Package 10 (Helmholtz AI): Introduction to Deep Learning

28 Introduction to Deep Learning (Part 2)

This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.

The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning can be somewhat intimidating.

We start with explaining the basic concepts of neural networks, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic Deep Learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Peter Steinbach (HZDR), Steve Schmerler (HZDR)

collaborative notes

12:30 Lunch break

Course Package 13 (Helmholtz Imaging): Machine Learning for Instance Segmentation

29 Lecture: Instance segmentation and tracking

Speaker: Dr Dagmar Kainmüller (MDC Berlin)

30 Tutorial: hands-on instance segmentation challenge in colab

Speaker: Dr Dagmar Kainmüller (MDC Berlin)

Wednesday 27 September

Course Package 5 (HIFIS): Continuous integration

31 Using containers in science

Containerized solutions can be helpful in the testing stage of continuous integration. This day will focus on how to use containerized solutions for scientific projects using Docker as an example.

Speakers: Mr Christian Hueser (Helmholtz-Zentrum Dresden-Rossendorf (HZDR)), Norman Ziegner (UFZ), Tobias Huste (Helmholtz-Zentrum Dresden-Rossendorf)

Course Package 6 (HMC): Introduction to Scientific Metadata

32 Fundamentals of Scientific Metadata (Part 2)

You will learn:

• about the differences between and the importance of data & metadata
• to annotate your research data with structured metadata
• to find and evaluate a suitable metadata framework and data repository
• to use basic Markdown / JSON / XML
• which tools are already available to level up your metadata annotation game
• why structured metadata is important and how it can increase your scientific visibility

organized by HMC Hub Information & HMC Office

Speakers: Silke Gerlich (HMC Hub Information), Mirl Trösch (HMC Office)

Course Package 10 (Helmholtz AI): Introduction to Deep Learning

33 Introduction to Deep Learning (Part 3)

This is an hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.

The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning can be somewhat intimidating.

We start with explaining the basic concepts of neural networks, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic Deep Learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.

Speakers: Alina Bazarova, Donatella Cea, Francesco Campi, Peter Steinbach (HZDR), Steve Schmerler (HZDR)

collaborative notes

12:30 Lunch break

Course Package 6 (HMC): Introduction to Scientific Metadata

34 Metadata consulting

This is a hands-on session where you can bring your own (meta)data to discuss consult about your questions and challenges.

Speakers: Mirl Trösch (HMC Office), Silke Gerlich (HMC Hub Information)

Thursday 28 September

Course Package 5 (HIFIS): Continuous integration

35 Continuous Integration in GitLab (Part 1)

With continuous integration in GitLab, you can automate the building, testing, and deploying of your code. This day will focus on creating an initial GitLab CI pipeline.

Speakers: Mr Christian Hueser (Helmholtz-Zentrum Dresden-Rossendorf (HZDR)), Norman Ziegner (UFZ), Tobias Huste (Helmholtz-Zentrum Dresden-Rossendorf)

Course Package 14 (Helmholtz Imaging): Rendering 3D datasets

In the course, we will discuss visualization of spatial datasets in general and then transform 3D segmentations into effectful Blender renderings.

36 Rendering 3D datasets

In the course, we will discuss visualization of spatial datasets in general and then transform 3D segmentations into effectful Blender renderings.

Speaker: Deborah Schmidt

12:30 Lunch break

Course Package 11 (Helmholtz Imaging): Regularization in Image Reconstruction: From Model to Data Driven Methods

37 Lecture: Regularization in Image Reconstruction: From Model to Data Driven Methods

In this course, we are going to provide the participants with knowledge about the typical mathematical tasks and caveats of image reconstruction problems. This covers advanced forward models and uncertainty, regularizing the reconstruction in order to prevent artifacts caused by noisy data and model errors, and eventually computational tasks. The participants will get the chance to test different image reconstruction and regularization schemes in the hands-on tutorial session.

Speakers: Lorenz Kuger, Martin Burger, Samira Kabri, Tim Roith

38 Tutorial: Regularization in Image Reconstruction: From Model to Data Driven Methods

Speakers: Lorenz Kuger, Martin Burger, Samira Kabri, Tim Roith

HIDA-Regularization-Burger.pdf

Package11-Instructions.pdf

Package11-Setup-Test.ipynb

Tutorial

• DESY.png
• models.py
• operators.py
• optimizers.py
• Tutorial_01.ipynb
• Tutorial_02.ipynb
• Tutorial_03.ipynb
• Tutorial_04.ipynb
• utils.py

Friday 29 September

Course Package 2 (HIDA/ Helmholtz AI): Entrepreneurship - from idea to I did

39 Lecture: Introduction to Lean Methodology for Startups

We’ll introduce the principles and methodologies of the lean startup approach covering the crucial topics including conducting customer research, creating a minimum viable product (MVP), and reiterative development cycles. We’ll emphasise the importance of user feedback, rapid experimentation, and agile continuous improvement to build successful innovations.

Speaker: Klaus Kammermeier

40 Workshop - Playing Lean

Join us for a session of Playing Lean, an engaging hands-on "flight simulator" for Lean Startup and innovation. The creators of the game have partnered up with Alexander Osterwalder, inventor of the Business Model Canvas and one of the great minds of Lean Startup. The Playing Lean board game teaches highly valuable lessons on Lean Startup, creates interest in Business Model Canvas, goes deep on the Value Proposition Canvas or running lean with the Lean Canvas. Playing Lean is turn based and players in competing teams advance a fictional strategy, competing in the same industry. Playing Lean deploys a number of gamification practices: storytelling, social learning, motivation and reward structures, competition - all supported by a trained facilitator.

Speaker: Klaus Kammermeier

Course Package 5 (HIFIS): Continuous Integration

41 Continuous Integration in GitLab (Part 2)

Building on day 2, you will learn advanced concepts of GitLab CI useful for optimizing the pipeline.

Speakers: Mr Christian Hueser (Helmholtz-Zentrum Dresden-Rossendorf (HZDR)), Norman Ziegner (UFZ), Tobias Huste (Helmholtz-Zentrum Dresden-Rossendorf)