www.datamarket.at

Data Market AustriaMihai Lupu, Allan HanburyTU Wien , Research Studios Austria FG

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Service Providers

Data Providers

Data Market Customers

End Users

BrokersInfrastructure Providers

Research and Development

(Basic and Applied)

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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

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DMA Partners

Technology Foundations

Connected Clouds

Data Innovation Environment

Pilots

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Mobility Pilot Example: Taxi Fleet Management

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Heatmap API

Taxi Management

Application

Taxi Companies

Taxi Management

Application

Taxi Management

Application

End Users

Data Market

Customers

Service Providers

Infrastructure Providers

Data Providers

www.datamarket.at

Service Providers

Data Providers

Data Market Customers

End Users

BrokersInfrastructure Providers

Research and Development

(Basic and Applied)

www.datamarket.at

Project Status

▪ Project started in October 2016

▪ Integration planning underway

▪ Community consultation is underway

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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

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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 16:0010

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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

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http://www.datamarket.at

@DataMarketAT | #DataMarketAT

Continuing Education Course

Data Science und Deep Learning

Planned Modules

1. Fundamentals of Data Science

2. Advanced Data

Science

2. Advanced Data

Science3. 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)

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

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

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

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

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

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

Planned Modules

1. Fundamentals of Data Science

2. Advanced Data

Science

2. Advanced Data

Science3. 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)

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

Quick plug

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

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 valueof this offering be over what isalready on the market?

▪ Other comments