EDSA curriculum and courses
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The document outlines the curriculum and self-study courses developed by EDSA. It includes the following key points: - The curriculum covers foundational topics, storage/processing, analysis, and interpretation/use of data science. It is delivered through video lectures, MOOCs, eBooks, and other online learning formats. - Self-study courses are offered on topics like foundations of big data, distributed computing, and machine learning. MOOCs are also developed on the FutureLearn platform. - The curriculum is regularly updated, with new topics like statistical foundations and data management planned for future iterations. - Courses are tagged with skills and targeted towards different learner groups like statisticians, analysts
EDSA curriculum and courses
- 1. Curriculum and self-study coursesChris Phethean University of Southampton United Kingdom
- 2. EDSA activities • 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
- 3. 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
- 4. EDSA Courses The EDSA Courses Portal: http://courses.edsa-project.eu/
- 5. EDSA Courses: Self-study options • Foundations of Big Data • Big Data Architecture • Process Mining • Distributed Computing • Essentials of Data Analytics and Machine Learning • Big Data Analytics • Foundations of Data Science
- 6. Big Data Architecture course: http://courses.edsa-project.eu/course/view.php?id=27
- 7. EDSA Courses: FutureLearn MOOCs Started 11 April 3 weeks Starts 11 July 4 weeks
- 9. Curriculum – Next Iteration (M18) 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 • Machine Learning, Data Mining and Basic Analytics • Big Data Analytics • Process Mining Interpretation and Use • Data Visualisation • Visual Analytics • Finding Stories in Open Data • Data Exploitation M18 Curricula modules
- 10. Course Schedule the Data Science community. Table 3: EDSA Core Curriculum Schedule Topic Schedule Foundations of Data Science M6 Foundations of Big Data M6 Big Data Architecture M6 Distributed Computing M6 Machine Learning, Data Mining and Basic Analytics M6 Process Mining M6 Statistical / Mathematical Foundations M18 D2.1 Data Science Curricula 1 Page 11 of 37 Data Management and Curation M18 Big Data Analytics M18 Data Visualisation M18 Finding Stories in Open Data M18 Programming / Computational Thinking (R and Python) M30 Stream Processing M30 Visual Analytics M30 Data Exploitation including data markets and licensing M30 ✔ ✔ ✔ ✔ ✔ ✔
- 11. 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
Editor's Notes
- #4: 15 topics. Detailed curricula for
- #6: Domain-specific variants of Founds D.S taking on board earlier AB feedback.
- #8: FutureLearn: Over 3.5M registered users as of 26/04/16
- #10: M18: Next 5 modules to have curricula developed, with learning resources to follow in M24, for the next set of self-study courses. Revisions to existing curricula based on feedback from advisory board and learners – for example providing domain-specific variants of key topics such as foundations of data science.