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Data Science Society
Presentation: Higher education in Bulgaria
1. Data Science Society brief
2. Education data analysis review
3. Next steps
Data Science Society brief
Sergi Sergiev
Business Science
Education
Why
Who we are
For the past 2 years our driving core of 10 people has been joined by 30 volunteers and over 1000
members of the community.
Events
• Meetup
• Data and Beer
• Conference
• Projects
• Data talks
• Datathon
Directions
• Online platform
• Support
Education
• Projects
Previous collaborations
Education data analysis review
Anton Nenov
Universities in Bulgaria DSS Interactive Visualisation Tableau Public
1. What we Did?
a. We have created one database with the data for all Universities in Bulgaria for the period
2005-2015
b. We have built an online interactive visualisations to see different kinds of structured info
2. Why we did it?
a. We have more than 50 universities in Bulgaria
b. They have more than 3900 different courses active for the last year
c. More than 100 000 rows analysed.
d. It is too difficult for everyone to see the big picture using the official data on the website
of Ministry of Education
3. Source of data - Ministry of Education:
https://www2.mon.bg/AdminUReg/html/reports.asp#
All Universities in Bulgaria
Forecast for the next Year • It is easy for everyone to select the university and see what will be
the situation next year.
• We used integrated Winters Additive as method with low level of
Standard Error (MAPE from 4.2% to 6.9%)
Some Interesting findings
• All Universities in Plovdiv Region are increasing number of students
compared with steady numbers for all other regions.
Some Interesting findings
• There is a big Increase of the foreign Student in all Medical
Universities in Bulgaria?
Some Interesting findings
• There is a big decrease for students studying Architecture and
Construction after a period of big interest (2008-2010).
Where is the VIZ?
You can find it on the following address:
https://public.tableau.com/profile/dss.bulgaria
Small problem - the VIZ is on Bulgarian language :)
Next steps
Sergi Sergiev
Big data implications
Datathon Cases:
• Volume - GBs of data • Velocity - real time portals of data, APIs • Variety - images, opendata, text, structured data,
APIs • Machine learning - predictive models, NLP,
semantics, OCR, data cleansing • Participants:
• 9 business • 4 startups • NSI and Council of Ministers -
opendata.government.bg
Collaboration
- Datathon support with statistical data
- Collaboration about EU Big Data Hackathon in March
- Discussion to open NSI data
Questions
http://www.DataScienceSociety.net/datathon/
@DataSciSociety