This document summarizes a preliminary study of online data science courses and open educational resources. It finds that universities offer the majority of massive open online courses (MOOCs) on Coursera, edX, and Udacity platforms, covering topics like statistics, data analysis, and data mining. Many open educational resources are also available through repositories like MIT OpenCourseWare, OER Commons, and MERLOT, providing learning materials like course notes, textbooks, and tutorials. The document analyzes the providers, audiences, objectives, formats, and content of existing online data science offerings to identify opportunities for collaboration on open courses.
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http://www.mysliderule.com/
Search on “data” + free: 367
+ certificate: 139
MOOCs -catalog
https://learning.accredible.com/courses
Number?
As of 12/09/2014
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Open Education Europa:
http://www.openeducationeuropa. eu/en/find/moocs
Searching on ‘data’: 54 MOOCs
MOOCS -catalog
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MOOCs (a few weeks with a few hours/week):
Coursera: “Statistics and Data Analysis”, 56 courses: https://www.coursera.org/courses?orderby=upcoming&cats=stats
edX: “Statistics & Data Analysis”, 26 courses: https://www.edx.org/course-list/allschools/statistics-data- analysis/allcourses
Udacity: “Data science”, 11 courses: https://www.udacity.com/courses#!/data-science
E-learning (a few hours):
Big Data University: free courses, ~50 courses: http://bigdatauniversity.com/wpcourses/?cat=124
MOOCs –main platforms
As of 12/09/2014
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More MOOcs
Source: http://www.technoduet.com/a-comprehensive-list-of-mooc-massive-open-online-courses-providers/
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MIT’s OCW (http://ocw.mit.edu/)
OER Commons (https://www.oercommons.org/)
MERLOT (http://www.merlot.org/merlot/materials.htm)
OpenStaxCNX (http://cnx.org/)
OpenStaxCollege (http://openstaxcollege.org/)
Open Education Europa (http://www.openeducationeuropa.eu/en/find/resources)
Main OERs repositories
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MIT’s OCW: http://ocw.mit.edu/
Search on ‘data’: 259 Courses, 21 900 ‘all formats’
OER: MIT’s OCW
As of 13/09/2014
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OER: MIT’s OCW
http://ocw.mit.edu/resources/res-9-0002-statistics- and-visualization-for-data-analysis-and-inference-january-iap-2009/lecture-notes/
As of 13/09/2014
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Searching on ‘data’: 3968 results
OER: OER Commons
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http://dataintheclassroom.noaa.gov/ DataInTheClassRoom/
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OER: MERLOT
Link to
http://ocw.mit.edu/courses/sloan-school-of-management/15- 062-data-mining-spring-2003/
2076 results searching materials on ‘data’, different types of materials; for ex:
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Subject “Mathematics and Statistics”, keyword ‘data’:
102 results, 18 books
OER: OpenstaxCNX
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Open textbook on Introductory Statistics: http://openstaxcollege.org/textbooks/introductory-statistics
OER: Openstaxcollege
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Open Education Europa:
http://www.openeducationeuropa. eu/en/find/resources
Searching on ‘data’: 26 resources
OER: Open Education Europa
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More OERs
Source: http://www.technoduet.com/25-awesome-open-online-educational-resources-videos-ideas-materials-books-and-more/
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by whom?
for whom?
which objectives? certifying?
which success?
which form?
which platforms?
which content?
Analysis
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Universities:
Coursera: Caltech, Columbia U., Duke U., Eindhoven U. of Technology, FudanU., Higher School of Economics, Princeton U., Stanford U., Technical U. of Denmark (DTU), U. of Edinburgh, U. of Toronto…
edX: Caltech, Delft, Harvard, MIT, Peking, UC Berkeley, UT Arlington, UtAustin…
TU Delft / Eindhoven / Twente(as Research Data NL)
Schools of journalism:
European Journalism Center
Knight Centerfor Journalism (University of Texas)
Companies or research lab developing tools:
Google: Making sense of data -> Google Fusion Tables
SAP: open SAP platform (ex: “BI 4 Platform Innovation and Implementation”)
IBM: in Big Data University
Microsoft: Microsoft Academy with courses on big data, data insights… -> SQL Server, Azure
Amazon: Big data on AWS
Machine Learning Group at the University of Waikato: (More) Data Mining with Weka
Non-profit network of passionate people: School of data (Open Knowledge Foundation)
Group of enthusiasts: Big Data University
By whom?
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Higher Education, Vocational Education and Training, Adult Learning, Secondary, Primary?
All except Primary in Open Education Europa
NODE project targeting grade 6-8
“Preparing for Uni” MOOC (next slide) for secondary
Citizens at large?
When no pre-requisite needed
To be developed to have citizen take advantage of the Open (Government) Data movement?
Professionals:
Researchers
Support to researchers
Librarians
Journalists -> data journalists a new profession?
Computer scientists
For whom?
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Example 1: Preparing for Uni
https://www.futurelearn.com/courses/preparing-for-uni-3
University of East Anglia
Secondary
“Higher education is about learning at a higher level: developing skills relating to critical thinking; holding a supported, substantive argument; analysing and using data or sources critically.”
Example 2: Essentials 4 Data Support
http://datasupport.researchdata.nl/en
Research Data Netherlands, coalition of 3TU.Datacentrum and DANS
“for those who provide support to researchers in storing, managing, archiving and sharing their research data(data support staff)”
For Whom?
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Coursera: Data Science specialization
9 courses + 1 capstone project => get a Specialization Certificate
[courses free + 35€/course to get a verified certificate]
Research Data NL: Essentials 4 Data Support
Free online course
Full course: online + face2face (2 days) with certificate (€250, exclusive of VAT)
which objectives? certifying?
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MOOCs in general -see reports:
Bill and Melinda Gates foundation: http://www.moocresearch.com/reports
Open Education Europa: http://www.openeducationeuropa.eu/en/european_scoreboard_moocs
Conference on “MOOCs: What have we learned, emerging themes and what next” (28/01/2014, University of London): http://www.lfhe.ac.uk/en/research-resources/post-event- resources/conferences-and-events/moocs2-resources.cfm
And many more…
which success?
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Some data on completion rates (2013): http://www.katyjordan.com/MOOCproject.html
Which success?
Zoom on courses with ‘data’ in title:
4.5% -21.7%
91 –6500 students
R, computing, DB, statistics,
Data Analysis, Data Visualization
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An example: “A Brief Introduction to Data Science with R”, iSchool(the School of Information Studies, Syracuse University)
http://ischool.syr.edu/newsroom/news.aspx?recid=1439
“The class used the free open-source e-book authored by Stanton (“An Introduction to Data Science”) as a guide, and also examined the R statistical analysis and visualization system.”
“While 500 slots were planned, 1,731 requests to participate were received.”
“Of the 839 participating (…) 91 students are receiving certificates for completing all course requirements. (That is 21.2% of the 429 actively engaged students and 10.8% of the 839 who signed up).”
“Course objective: professional development (61%)
Age ranges (of those responding):
Which success?
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MOOC: to animate one, it requires planning -teaching team (to prepare and animate) -community tools
Self-paced e-learning: to provide some, it requires a LMS such as Moodle, or a blog/CMS; time to set it up including automatic quizzes, activities, re-use of OERs
which form?
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MOOC platforms: Coursera, edX/ Open edX, Canvas,…
eLearning / LMS: Moodle
Blogs/CMS: Wordpress, Typo3,…
which platforms?
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Mix of theory + hands-on
Theory = statistics, data mining, machine learning...
Hands-on = tools to
collect data: scraping (import.io,…),...
curatedata:Google Spreadsheets, Datawrapper, CartoDB, OpenRefine...
analyse data: R,...
visualize data: infographics,…
Engagement via
“Real" projects: Coursolve.org
Competition: Kaggle.com
(MOOCs in general -Gamification: Angry Birds, Minecraft, SimCity…)
which content?
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Observed content of online “data science courses”, that is to teach “data scientist” skills, matches the description made in http://www.dataists.com/2010/09/a-taxonomy-of-data-science/:
“We’ve variously heard it said that data science requires some command- line fufor data procurement and preprocessing, or that one needs to know some machine learning or stats, or that one should know how to `look at data’. All of these are partially true, so we thought it would be useful to propose one possible taxonomy —we call it the Snice* taxonomy —of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret(or, if you like, OSEMN, which rhymes with possum).”
Which content?
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Which content?
The Data Scientist skills, objectives of courses, can also be summarised by this Venn diagram.
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
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Use resources from MOOCs, Big Data University, OERs?
Check licences
Collaboratively create one/several online course(s)
On dedicated Moodle platform?
On Big Data University? [Moodle]
Follow the “Creating a course in Big Data University” course
See http://bigdatauniversity.com/contact/to get course added
One/several generic courses, and one per domain (social sciences, environment,…); English and/or other languages
Create Engaging Learning Environments?
Minecraft?
SimCity?
Suggestions
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