Author: Allan Hanbury, senior researcher at the Vienna University of Technology, leader of the Data Science Research Studio Austria, co-founder of ContextFlow. Part 1: Data Market Austria, a recently-started project building a Data-Services Ecosystem in Austria. • What is the vision for Data Market Austria? • How is DMA planned to develop? • What are some of the technologies behind DMA? • How can one participate in DMA? • What are the requests from practitioners for DMA? Part 2: an outline of a Data Science Continuing Education course being developed at the Vienna University of Technology.

1. www.datamarket.at
Data Market Austria
Mihai Lupu, Allan Hanbury
TU Wien , Research Studios Austria FG

2. www.datamarket.at
Service
Providers
Data
Providers
Data Market
Customers
End Users
BrokersInfrastructure
Providers
Research and
Development
(Basic and
Applied)

3. www.datamarket.at
Advance Technology
Foundations
Interconnect Clouds Create a Data
Innovation
Environment
Mobility Pilot
Earth Observation Pilot
First Steps to Further Domains
Data Market Austria Project Structure

4. www.datamarket.at
DMA Partners
Technology
Foundations
Connected
Clouds
Data Innovation
Environment
Pilots

5. www.datamarket.at
Mobility Pilot Example: Taxi Fleet Management

6. www.datamarket.at 6
Heatmap API
Taxi Management
Application
Taxi Companies
Taxi Management
Application
Taxi Management
Application
End Users
Data
Market
Customers
Service
Providers
Infrastructure
Providers
Data
Providers

7. www.datamarket.at
Service
Providers
Data
Providers
Data Market
Customers
End Users
BrokersInfrastructure
Providers
Research and
Development
(Basic and
Applied)

8. www.datamarket.at
Project Status
▪ Project started in October 2016
▪ Integration planning underway
▪ Community consultation is underway

9. www.datamarket.at
Plans to Facilitate Data Science Practice
▪ Sandboxes available with straightforward access to demo
datasets and necessary software installed
▪ Transparent pricing and usage regulations for data, services
and infrastructure
▪ Search engine for data and services
▪ Easier publication of data and services
▪ Smart Contracts

10. www.datamarket.at
Participating in DMA
▪ Participation models are being developed
▪ Register for the newsletter on the DMA website
▪ Start-up call for funding of small start-up projects on DMA
planned for the end of 2017
▪ DMA Public Meet-up in Salzburg on 6. April at 16:00
10

11. www.datamarket.at
Feedback
▪ What are your requirements for a Data Market?
▪ What would you like to be able to do in a Data Market?
▪ What are the main advantages you see in the Data Market?
▪ What would discourage you from participating in a Data Market?
▪ Other comments

12. www.datamarket.at
http://www.datamarket.at
@DataMarketAT | #DataMarketAT

13. Continuing Education Course
Data Science und Deep Learning

14. Planned Modules
1. Fundamentals of Data Science
2. Advanced Data
Science
2. Advanced Data
Science
3. Deep Learning3. Deep Learning
4. Text Analysis and
Word Embedding
4. Text Analysis and
Word Embedding
5. From Data to Stories5. From Data to Stories
6. Fundamentals of Data Market Participation
Each module is 60 hours (30 hours theory, 30 hours exercises)

15. 1. Fundamentals of Data Science
▪ Computational Thinking (the formulation of problems and
their solution spaces so that a computer can solve them)
▪ Data-centred programming paradigms (Python and R)
▪ Basic statistical and machine learning methods
▪ Data lifecycle and stewardship
▪ Experiment design for data science
▪ Reproducibility of data science experiments
▪ Practical examples of data science in practice

16. 2. Advanced Data Science
▪ Scaling data science algorithms
▪ Advanced scalable data science programming paradigms
▪ Common data science tools (Apache ecosystem and
beyond)
▪ Evaluation for selecting the optimal tools for solving a
problem
▪ Stream analysis
▪ Practical examples of scalable data science in practice

17. 3. Deep Learning
▪ Introduction to Neural Networks
▪ Convolutional Neural Networks (CNN)
▪ Recurrent Neural Networks (RNN, LSTM, GRU)
▪ Recent developments and extensions of Deep Neural
Networks
▪ Deep Architectures
▪ Software tools and frameworks
▪ Deep Learning in Practice

18. 4. Text Analysis and Word Embedding
▪ Basic Natural Language Processing
▪ Search
▪ Explicit/Implicit Semantics
▪ Word Embedding – deep learning for text
▪ Scalability aspects of NLP and search
▪ Software tools and frameworks
▪ Text Analysis and Word Embedding in Practice

19. 5. From Data to Stories
▪ The data science workflow
▪ Gathering information and ground truth from domain experts
▪ Communicating results for decision makers
▪ Collaborative data science (Jupyter Notebooks, R-Studio,
…)
▪ Visualisation and Visual Analytics
▪ Psychology of visualisations
▪ Examples of the data science workflow in practice

20. 6. Fundamentals of Data Market Participation
▪ How a data market is structured
▪ Technology behind a data market
▪ Business models in a data market
▪ Legal and ethical aspects of data sharing/selling
▪ Smart-contracts
▪ Data markets in practice

21. Planned Modules
1. Fundamentals of Data Science
2. Advanced Data
Science
2. Advanced Data
Science
3. Deep Learning3. Deep Learning
4. Text Analysis and
Word Embedding
4. Text Analysis and
Word Embedding
5. From Data to Stories5. From Data to Stories
6. Fundamentals of Data Market Participation
Each module is 60 hours (30 hours theory, 30 hours exercises)

22. Feedback
▪ What is missing?
▪ What is not needed?
▪ What should be done in another way?
▪ What could the added value of this offering be over what is
already on the market?
▪ Other comments

23. Quick plug

24. Contact
▪ We are examining getting FFG funding for the first round of
the course
▪ Contact us if your company would be interested in
participating:
– Allan.Hanbury@tuwien.ac.at
– Mihai.Lupu@tuwien.ac.at

25. feedback
▪ What are your requirements for
a Data Market?
▪ What would you like to be able
to do in a Data Market?
▪ What are the main advantages
you see in the Data Market?
▪ What would discourage you
from participating in a Data
Market?
▪ Other comments
▪ What is missing?
▪ What is not needed?
▪ What should be done in
another way?
▪ What could the added value
of this offering be over what is
already on the market?
▪ Other comments