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Zürcher Fachhochschule
Colloquium of University of Mannheim’s
School of Business Informatics & Mathematics
CHOCOLATE FLAVOURED DATA SCIENCE
Thilo Stadelmann
28.04.2014
Zürcher Fachhochschule
2
ZHAW Zurich University of Applied Sciences –
School of Engineering
Switzerland’s biggest fully-featured university of applied sciences
• >10’000 students
• >2’600 employees
• >1’000 (associate) professors
School of Engineering emanates from «Technikum Winterthur» (est. 1874)
Zürcher Fachhochschule
3
InIT Institute of Applied Information
Technology
Smart Information Systems since 2005
• 5 Focus areas, one vision: Smart IS
• Undergraduate, graduate & continuing eductation in
computer science
• Associated labs: Cloud Computing Lab, Data Science
Lab, Visual Computing Lab, Services Lab, …
Facts & Figures
• >70 employees, >35 (associated) professors
• >6 MCHF business volume in 2013
Zürcher Fachhochschule
4
Information Engineering Research Group
Combining structured and unstructured data analysis
• Information Retrieval and Data Warehousing
• Integration of heterogeneous and unstructured data
• Topic- and Trend-Detection (finding w/o search)
• Data- and Text-Mining
• Machine Learning and Artificial Intelligence
• Benchmarking of search engines
• Data Science within ZHAW Datalab
Information Engineering
Information
Retrieval
Data Mining
Data Warehousing
Machine Learning
Zürcher Fachhochschule
5
A Personal Story
• Fascinated by AI
• Studied computer science
• Applied ML & IR during Ph.D.
• Used DWH & DM professionally
• Difficult to briefly explain professional interests
 Excited about term «Data Scientist»
Agenda: 1. Definition  2. Projects  3. State of the Union
Zürcher Fachhochschule
6
What is a Data Scientist, what is Data Science?
Definition & Classification
Zürcher Fachhochschule
7
Data Science? …
…
Zürcher Fachhochschule
8
Data scientist?
 Unique blend of skills from AI, engineering & communication aiming at generating value from the data
strongly
interdisciplinary
 new discipline?
…
Competence clusters
 ca. 80% per Person
…
Character
 important for success
…
Zürcher Fachhochschule
9
Project Examples
Zürcher Fachhochschule
10
The ZHAW Data Science Laboratory
• One of the first European centers for Data
Science R&D and teaching
• Interdisciplinary virtual organization spanning
several instituts
• Core competency: Data Product design
with structured and unstructured data
Projects
• Many years of successful collaborations between academia and business
• Focused on Swiss SMEs as well as European programmes
Teaching
• Undergraduate and Graduate Courses
• One of the first European professional education programmes for Data Scientists
• Seminars and Workshops for idea exchange with industry
The ZHAW Data Science Laboratory
Zürcher Fachhochschule
11
Foundation Register 2.0
Situation: Ca. 7’500 foundations in Switzerland
Goal: Simple visual search by project proposal should yield
most probable sponsor
Challenges: Quantify and visualize content-based similarity of foundation’s missions
Solution:
• Develop multilingual retrieval system
• Search on very small document collection
(7.5k foundation’s mission statements)
 Extremely recall-oriented search
• High amount of data for intuitive visualizations
 “Forced Directed Layout” of similarities from
term-document matrix and topic modeling
Zürcher Fachhochschule
12
Foundation Register 2.0
Technical Aspect: Multilingual IR System
Solution:
• Different index fields for search (DE, FR, IT) and network similarity calculation (CROSS)
• Dual iterative search process:
• search in own language  relevance feedback on visual neighborhood  foreign language docs appear in search results
• CROSS field construction:
• Aggressive stop word elimination (outlier removal according to Zipf distribution)
• No stemming (to retain precision)
• Machine translation to main language  drop words that couldn’t be translated
• Visualization generation:
• Build similarity matrix of docs in translated CROSS field for network generation
• Use QuadTree algorithm for large collections
Zürcher Fachhochschule
13
Talkalyzer
Goal: Speaker Recognition in meetings on mobile devices
Challenge: Build reliable speaker models
Approach:
• Loosen i.i.d. assumption on feature vectors
• Use Viola&Jones’ face detection approach on audio features
 find typical sounds of a speaker in a spectrogram
Zürcher Fachhochschule
14
Enterprise Knowledge Curation
Goal: Contextual News for Structured Data
Challenge: Bridging the sematic gap
Solution:
• IR Pipeline with implicit relevance
feedback
• Learning user profiles from structured
and unstructured data
Zürcher Fachhochschule
15
Enterprise Knowledge Curation (contd.)
Technical Aspect: Data Fusion
Subgoal: Find relevant news documents per user (document = text & metadata)
Challenge: How to combine information on text and metadata
Solution:
• Score Fusion: Fusion of separate score using a weighted average
• Learning to Rank: Learn weights with logistic regression
• Training data: Click-based implicit user feedback
𝑠 𝑑, 𝑞 = 𝑤𝑖
𝑖
∙ 𝑠𝑖
 Next challenge: overcome cold-start by
avoiding learning on training data
𝑤1
𝑤2
𝑤8
Profile
.
.
.
.
.
.
Document
𝑠1
𝑠2
𝑠8
.
.
.
.
.
.
Zürcher Fachhochschule
16
SODES – Swiss Open Data Exploration System
Challenge: Open Data promises to be a gold mine – but accessing and combining data
from different data sources turns out to be non-trivial and very time consuming
Goal: A platform that enables easy and intuitive access, integration and exploration of
different data sources
Solution:
Zürcher Fachhochschule
19
The State of the Union
Zürcher Fachhochschule
20
Summary of challenges faced so far
Transition US  Switzerland
• We don’t have internet behemoths
• But we have banks/telcos, industry, retailers, smart*, societal challenges
Uphold quality standard
• It’s Data Science!
•  Develop curricula
Zürcher Fachhochschule
21
«Evaluation»: DAS in Data Science
Diploma of Advanced Studies (DAS) professional education programme
• Start: this fall
• Three modules (part time, one afternoon + evening per week)
 Strong demand from industry (CAS Data Analytics already overbooked)
CAS Data Science Applications
Machine Learning, Big Data Visualization, Design & Development
of Data Products, Data Protection & Security
CAS Information
Engineering
Scripting inPython,
Information Retrieval &
Text Analytics,
Databases & SQL,
Data Warehousing,
Big Data
CAS Data
Analytics
Data Description &
Visualization, Statistical
Foundations of Analytics,
Multiple Regression,
Time Series & Forecasting,
Clustering & Classification
Zürcher Fachhochschule
22
Evaluation: 1st Swiss Workshop on Data Science
Impressions
Zürcher Fachhochschule
23
Evaluation: 1st Swiss Workshop on Data Science
• Expected 60 participants  got 120 plus 7 sponsors
• Ca. ¾ opted in to build a community of Swiss Data Scientists
• Two groups of participants: Data Scientists / Managers needing Data Scientists
Zürcher Fachhochschule
24
Conclusions
• Big demand from industry regarding Data Science related topics
• Partly due to routing of requests for formerly diverse topics to central institution
• More inquiries than actual projects…
• …but typically more projects than researchers
• Data Science has different Application areas in Switzerland
• But marketing / customer analytics is still a big one
• Chance for academia
• Bring together diverse expertise from many disciplines
• Coin a field

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Chocolate Flavoured Data Science

  • 1. Zürcher Fachhochschule Colloquium of University of Mannheim’s School of Business Informatics & Mathematics CHOCOLATE FLAVOURED DATA SCIENCE Thilo Stadelmann 28.04.2014
  • 2. Zürcher Fachhochschule 2 ZHAW Zurich University of Applied Sciences – School of Engineering Switzerland’s biggest fully-featured university of applied sciences • >10’000 students • >2’600 employees • >1’000 (associate) professors School of Engineering emanates from «Technikum Winterthur» (est. 1874)
  • 3. Zürcher Fachhochschule 3 InIT Institute of Applied Information Technology Smart Information Systems since 2005 • 5 Focus areas, one vision: Smart IS • Undergraduate, graduate & continuing eductation in computer science • Associated labs: Cloud Computing Lab, Data Science Lab, Visual Computing Lab, Services Lab, … Facts & Figures • >70 employees, >35 (associated) professors • >6 MCHF business volume in 2013
  • 4. Zürcher Fachhochschule 4 Information Engineering Research Group Combining structured and unstructured data analysis • Information Retrieval and Data Warehousing • Integration of heterogeneous and unstructured data • Topic- and Trend-Detection (finding w/o search) • Data- and Text-Mining • Machine Learning and Artificial Intelligence • Benchmarking of search engines • Data Science within ZHAW Datalab Information Engineering Information Retrieval Data Mining Data Warehousing Machine Learning
  • 5. Zürcher Fachhochschule 5 A Personal Story • Fascinated by AI • Studied computer science • Applied ML & IR during Ph.D. • Used DWH & DM professionally • Difficult to briefly explain professional interests  Excited about term «Data Scientist» Agenda: 1. Definition  2. Projects  3. State of the Union
  • 6. Zürcher Fachhochschule 6 What is a Data Scientist, what is Data Science? Definition & Classification
  • 8. Zürcher Fachhochschule 8 Data scientist?  Unique blend of skills from AI, engineering & communication aiming at generating value from the data strongly interdisciplinary  new discipline? … Competence clusters  ca. 80% per Person … Character  important for success …
  • 10. Zürcher Fachhochschule 10 The ZHAW Data Science Laboratory • One of the first European centers for Data Science R&D and teaching • Interdisciplinary virtual organization spanning several instituts • Core competency: Data Product design with structured and unstructured data Projects • Many years of successful collaborations between academia and business • Focused on Swiss SMEs as well as European programmes Teaching • Undergraduate and Graduate Courses • One of the first European professional education programmes for Data Scientists • Seminars and Workshops for idea exchange with industry The ZHAW Data Science Laboratory
  • 11. Zürcher Fachhochschule 11 Foundation Register 2.0 Situation: Ca. 7’500 foundations in Switzerland Goal: Simple visual search by project proposal should yield most probable sponsor Challenges: Quantify and visualize content-based similarity of foundation’s missions Solution: • Develop multilingual retrieval system • Search on very small document collection (7.5k foundation’s mission statements)  Extremely recall-oriented search • High amount of data for intuitive visualizations  “Forced Directed Layout” of similarities from term-document matrix and topic modeling
  • 12. Zürcher Fachhochschule 12 Foundation Register 2.0 Technical Aspect: Multilingual IR System Solution: • Different index fields for search (DE, FR, IT) and network similarity calculation (CROSS) • Dual iterative search process: • search in own language  relevance feedback on visual neighborhood  foreign language docs appear in search results • CROSS field construction: • Aggressive stop word elimination (outlier removal according to Zipf distribution) • No stemming (to retain precision) • Machine translation to main language  drop words that couldn’t be translated • Visualization generation: • Build similarity matrix of docs in translated CROSS field for network generation • Use QuadTree algorithm for large collections
  • 13. Zürcher Fachhochschule 13 Talkalyzer Goal: Speaker Recognition in meetings on mobile devices Challenge: Build reliable speaker models Approach: • Loosen i.i.d. assumption on feature vectors • Use Viola&Jones’ face detection approach on audio features  find typical sounds of a speaker in a spectrogram
  • 14. Zürcher Fachhochschule 14 Enterprise Knowledge Curation Goal: Contextual News for Structured Data Challenge: Bridging the sematic gap Solution: • IR Pipeline with implicit relevance feedback • Learning user profiles from structured and unstructured data
  • 15. Zürcher Fachhochschule 15 Enterprise Knowledge Curation (contd.) Technical Aspect: Data Fusion Subgoal: Find relevant news documents per user (document = text & metadata) Challenge: How to combine information on text and metadata Solution: • Score Fusion: Fusion of separate score using a weighted average • Learning to Rank: Learn weights with logistic regression • Training data: Click-based implicit user feedback 𝑠 𝑑, 𝑞 = 𝑤𝑖 𝑖 ∙ 𝑠𝑖  Next challenge: overcome cold-start by avoiding learning on training data 𝑤1 𝑤2 𝑤8 Profile . . . . . . Document 𝑠1 𝑠2 𝑠8 . . . . . .
  • 16. Zürcher Fachhochschule 16 SODES – Swiss Open Data Exploration System Challenge: Open Data promises to be a gold mine – but accessing and combining data from different data sources turns out to be non-trivial and very time consuming Goal: A platform that enables easy and intuitive access, integration and exploration of different data sources Solution:
  • 18. Zürcher Fachhochschule 20 Summary of challenges faced so far Transition US  Switzerland • We don’t have internet behemoths • But we have banks/telcos, industry, retailers, smart*, societal challenges Uphold quality standard • It’s Data Science! •  Develop curricula
  • 19. Zürcher Fachhochschule 21 «Evaluation»: DAS in Data Science Diploma of Advanced Studies (DAS) professional education programme • Start: this fall • Three modules (part time, one afternoon + evening per week)  Strong demand from industry (CAS Data Analytics already overbooked) CAS Data Science Applications Machine Learning, Big Data Visualization, Design & Development of Data Products, Data Protection & Security CAS Information Engineering Scripting inPython, Information Retrieval & Text Analytics, Databases & SQL, Data Warehousing, Big Data CAS Data Analytics Data Description & Visualization, Statistical Foundations of Analytics, Multiple Regression, Time Series & Forecasting, Clustering & Classification
  • 20. Zürcher Fachhochschule 22 Evaluation: 1st Swiss Workshop on Data Science Impressions
  • 21. Zürcher Fachhochschule 23 Evaluation: 1st Swiss Workshop on Data Science • Expected 60 participants  got 120 plus 7 sponsors • Ca. ¾ opted in to build a community of Swiss Data Scientists • Two groups of participants: Data Scientists / Managers needing Data Scientists
  • 22. Zürcher Fachhochschule 24 Conclusions • Big demand from industry regarding Data Science related topics • Partly due to routing of requests for formerly diverse topics to central institution • More inquiries than actual projects… • …but typically more projects than researchers • Data Science has different Application areas in Switzerland • But marketing / customer analytics is still a big one • Chance for academia • Bring together diverse expertise from many disciplines • Coin a field