European Data Science Academy
Transcript of European Data Science Academy
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European Data Science Academy
Chris Phethean edsa-project.eu
University of Southampton [email protected]
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What is EDSA about
ESTABLISH A EUROPEAN COMMUNITY OF EDUCATORS, LEARNERS, PRACTITIONERS, AND POLICY MAKERS ON DATA SCIENCE
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Who we are
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How do we deliver a syllabus to allow anyone to master Data Science?
Image: http://mappr.it/2016/02/24/data-science-for-beginners/
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What is EDSA?
• Surveys • Interviews • Dashboards
Landscaping
• Modular, media-rich, multiple languages
• Core, domain-specific, and technology-specific topics
Curriculum and
courseware • Video lectures • Professional training • MOOCs • eBooks
Courses and learning analytics
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Initial Version of Module
Final Module: eBook & Course
Webinar – Final Recording
Webinar – First Recording
Collection of Module Materials Slides – First Version
Slides – Final Version
Stakeholder Communities Industrial Advisory Board
Demand Analysis Reflection
Module Dissemination Online & F2F
Learning Analytics
Curricula EDSA Analytics
Dashboard
Reconfiguring & Repurposing
Learners Learning Delivery
End to End approach
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1. What is the current demand for data skills in Europe?
2. What training should be offered in order to accommodate this demand?
• Surveys • Interviews • Dashboards
Landscaping data science
demand
Demand Analysis
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The European Data Science Landscape
28 EU member states
19 Eurostat defined industry sectors
Sole traders to large
companies
584 survey responses
108 interviews
What is the current demand for data skills in Europe?
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Organisation Eastern Europe
Northern Europe
Southern Europe
Western Europe
NA Total
Large 69 112 100 105 1 387
SME 36 75 81 45 4 241
Micro 5 14 7 9 1 36
Self-employed
2 4 5 3 0 14
NA 0 2 2 10 0 14
Survey & Interview Responses
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Current Demand for Data Skills • Importance of skills: How would you rate the
following skills for a data scientist?
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Managers
Practitioners
• How proficient are you (practitioners) or your teams (managers) in the following skills areas? (1=very poor; 5=very good)
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What tools, technologies or languages should be covered on data science courses?
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What other skills are needed?
0
5
10
15
20
25
Communication and presentation skills
Industry and business domain
knowledge
Teamwork Data management Social skills
% of interviewees
“The data scientist must be willing to adapt quickly in order to keep up.”
Data scientists must have the “soft skills required of interacting with businesses and guiding the people responsible for making decisions“
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What training methods would you prefer for data science training?
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Preliminary Conclusions • Communication and persuasion skills
• Social skills
• Basic data literacy training for organisations
• Skills analytics framework
• Sector-specific courses
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Challenges to finding training • Majority of interviewees faced challenges with:
– finding courses: “It is difficult to find courses also because there is no single platform to search within.”
– gathering sufficiently detailed information on contents and course quality
– finding tailored solutions aligned with team’s needs
• Understanding one’s own status and demand (both as an individual and organisation) is an additional challenge: – “The difficulty is finding or understanding where the
individual is in terms of his own skill-set and where he needs to develop, and then finding the resources to plug into those gaps.“
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dashboard.edsa-project.eu
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Dashboard Data: Job Demand in the
EU • ~300K job posts from
– Adzuna (UK, DE, FR, N) via API
– Trovit (Additional 18 countries) via scraping
• Extracting – Time – Geolocation – Skills required – Company
Country No. of Postings Extracted Austria 4,694 Belgium 10,764 Bulgaria 44 Czech Republic 6,232 Denmark 7,026 Estonia 2 France 34,881 Germany 20,938 Hungary 7,225 Ireland 18,919 Italy 11,543 Malta 38 The Netherlands 9,571 Norway 33 Poland 18,159 Portugal 21,784 Romania 17,946 Slovakia 3 Spain 9,210 Sweden 13,870 Switzerland 14,616 United Kingdom 89,343
TOTAL 316,841
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The EDSA Curriculum
Foundations • Foundations of
Data Science • Foundations of
Big Data • Statistical /
Mathematical Foundations
• Programming / Computational Thinking (R and Python)
Storage and Processing • Data
Management and Curation
• Big Data Architecture
• Distributed Computing
• Stream Processing
Analysis • Essentials of
Data Analytics and Machine Learning
• Big Data Analytics
• Process Mining
Interpretation and Use • Data Visualisation • Visual Analytics • Finding Stories in
Open Data • Data Exploitation
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Addressing Demand EDSA courses portal: edsa-project.eu/resources/courses/
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Curriculum Dimensions • Courses to be tagged with pre and post skills • Learning pathways aimed at:
– Statisticians – Analysts – Managers, product owners, CEOs – Programmers, developers and system engineers – Data managers (incl. security experts)
• Further dimensions: – Tools and programming languages – Type of data – Industry sector – Level
• Basic, advanced, expert
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Summary • Challenges
– Data Science is a broad, multidisciplinary subject that requires a mix of skillsets
– How do we equip practitioners and their managers with the ability to assess their own skill profiles and identify gaps?
– Tools and tech used in industry change rapidly, faster than academia typically updates courses
– How do data scientists locate appropriate courses to best enable them to learn new skills?
– How should courses be delivered in order to target the diverse range of backgrounds and skillsets interested in data science?
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Data Science @ Southampton