Data Market Austria and Data Science Continuing Education Course

131 views

Published on

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.

Published in: Data & Analytics
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
131
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
6
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Data Market Austria and Data Science Continuing Education Course

  1. 1. www.datamarket.at Data Market Austria Mihai Lupu, Allan Hanbury TU Wien , Research Studios Austria FG
  2. 2. www.datamarket.at Service Providers Data Providers Data Market Customers End Users BrokersInfrastructure Providers Research and Development (Basic and Applied)
  3. 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. 4. www.datamarket.at DMA Partners Technology Foundations Connected Clouds Data Innovation Environment Pilots
  5. 5. www.datamarket.at Mobility Pilot Example: Taxi Fleet Management
  6. 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. 7. www.datamarket.at Service Providers Data Providers Data Market Customers End Users BrokersInfrastructure Providers Research and Development (Basic and Applied)
  8. 8. www.datamarket.at Project Status ▪ Project started in October 2016 ▪ Integration planning underway ▪ Community consultation is underway
  9. 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. 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. 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. 12. www.datamarket.at http://www.datamarket.at @DataMarketAT | #DataMarketAT
  13. 13. Continuing Education Course Data Science und Deep Learning
  14. 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. 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. 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. 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. 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. 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. 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. 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. 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. 23. Quick plug
  24. 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. 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

×