In Drazen talk, you will get a chance to listen to how Data Science Master 4.0 on Belgrade University was created, and what are the benefits of the program.

1. Dražen Drašković, PhD, Ass. Prof.
Belgrade, November 2019
University of Belgrade – School of Electrical Engineering
Education for Digital Transformation
and a new wave of Data Science -
the challenge for Serbia

2. Introduction
• School of Electrical Engineering and DataSci courses
• New master program in Serbia (2019)
• R&D projects and cooperation with other
universities/schools
• Young professionals and a way to become
a pioneer of data science in Serbia
12/26/2019 Data Science Conference 5.0 2

3. Basic facts about our School - ETF
• In 1935 mechanical and electrical engineering
established the common department
at the Technical School of the University of Belgrade
• Founding of the School of Electrical Engineering
in 1948
• In 2019: 22 000 graduated engineers
12/26/2019 Data Science Conference 5.0 3

4. Bachelor and Master program @ETF
 BSc academic studies
 4 years / 8 semesters
 2 study programs:
 1) Electrical Eng. and Computing
 Power engineering
 Telecommunications and information
technologies
 Electronics and digital systems
 Micro and physical electronics
 Signals and systems
 Computer engineering
• 2) Software Engineering
• Titles:
– BSc in Electrical Eng. and Computing
– BSc in Software Engineering
 MSc academic studies
 1 year / 2 semesters
 12 modules:
 Audio and Video Technologies
 Applied Mathematics
 Biomedical Engineering
 Electronics and digital systems
 Energy Efficiency
 Power Systems
 Nanoelectronics and Photonics
 System Engineering and Com.
 Microwave Engineering
 Signals and systems
 Computer Engineering and Informatics
 Software Engineering
12/26/2019 Data Science Conference 5.0 4

5. Data Science - new courses
 6 years ago: ETF group for Data Science promotion
 2 years ago, new curriculum reform
 6 new courses (Data Mining, Natural Language
Processing, Introduction to Machine Learning, Data
Analysis in Social Networks, Big Data in SE, Data Analysis
and Visualization)
 5 reformed courses (Introduction to AI / Intelligent
Systems / Image Processing / Speech Processing /
Advanced Neural Networks)
12/26/2019 Data Science Conference 5.0 5

6. Master for Digital Transformation 4.0
• Multidisciplinary master program - public call by
the Ministry of Education, Science and Tech. Dev.
• Synergy of two schools = ETF + Faculty Org. Sciences
• Master program "Advanced information technology
in digital transformation"
– 3 semesters
– 90 ECTS (30 per semester)
– from 2019/2020
– 35 students (15+5+15)
– Title: Master engineer in information technologies
• Support of the Digital Serbia Initiative
12/26/2019 Data Science Conference 5.0 6

7. Master 4.0 - Structure
• Four study modules:
– Data analysis
– Distributed computing
– Interactive computing
– Organization and management
• One-semester courses (1 course = 6 ECTS)
• Student has 10 courses in first 2 semesters
• Third semester = professional practice +
team startup project + MSc thesis
12/26/2019 Data Science Conference 5.0 7

8. Teaching staff and infrastructure
• More then 100 professors, TA and researchers
• Guest lecturers from the best world universities
• Industry lecturers
• Providers of practice - the most important domestic
companies
• Providers of support programs for the development of
technological entrepreneurial ventures:
Science Technology Park Belgrade, Innovation Center
ETF, R&D center FON, ICT hub, Impact hub, 30hills, etc.
12/26/2019 Data Science Conference 5.0 8

9. Where are we now?
• September 2019: the program accredited
• October 2019: Entrance exam/23 enrolled students
• November 2019: first courses started
• 70% students choose the "Data analysis" module
• Objectives - short / long term
– new disciplines in our higher education
– increase the number and quality of IT staff
– engineers competitive globally
– improving the entrepreneurial ecosystem
in Serbia
12/26/2019 Data Science Conference 5.0 9

10. R&D projects in DataSci field
• The use of Artificial Intelligence to automate
the Rapid Integrated Assessment mechanism and to
nationalize Sustainable Development Goals in Serbia (UNDP)
• Software tool for sectorization planning with traffic
prediction (SMATSA)
• Image analysis in the manufacture of chemical preparations
• Churn prediction in retail banking
• Recognize movie categories based on movie posters
and photos
• Recognizing perspective in car photos
• Application of convolutional neural networks to improve
photo quality
• Application of reinforcement learning in gaming industry
12/26/2019 Data Science Conference 5.0 10

11. Cooperation with other universities
• Temple University, USA
– Data Science lab. (Prof. Zoran Obradovic, PhD)
• Stanford University, USA
– Machine Learning lab. (Prof. Jure Leskovac, PhD)
• University of Ljubljana, Slovenia
– Lab for Data Technologies, Faculty of Computer and
Information Science (Prof. Marko Bajec, PhD)
• Universitat Politècnica de València, Spain
– Department of Computing Engineering, Data Science lab
(Prof. Ana Pont Sanjuan, PhD)
12/26/2019 Data Science Conference 5.0 11

12. Young professionals
• The best of our school - our students
• A way to become a pioneer of data science
in Serbia - Ms Ljubica Vujovic, MSc EE
12/26/2019 Data Science Conference 5.0 12

13. Pioneering Data Science at Etihad Airways
Ljubica Vujovic
Data Science Conference 2019, Belgrade, Serbia

14. Content
• Data Science Services Team
• Projects Overview
• Building Blocks of Data Science Project
• Local Joiners Estimation
• Let's Get Technical
• Ready To Depart

15. Belgrade Team
Etihad Data Science Team in Belgrade is a part of Enterprise Analytics Transformation.
Our mission is to develop automated machine learning solutions and data driven business rules which will
help in cost savings, provide new revenue streams and increase customer satisfaction.
Who are we?
• Five members
•BS, BEng, MEng, MSc, PhD
• Who am I?

16. Projects Overview
Our work is mainly divided between Operational and Commercial projects.
Commercial Projects
• Customer Lifecycle Value
• Timing and Cadence of
Email Campagne
• Types of Offers
• Content of Offers
Operational Projects
• Statistical Taxi-out Fuel Estimation
• Holding Fuel and Holdings
Estimation
• Local Joiners Passengers
Volume Estimation
• Catering Estimation

17. Building
Blocks of
Data
Science
Project
PROBLEM
STATEMENT
DATA
AVAILABILITY
EXPLORATORY
DATA ANALYSIS
MODEL
DEVELOPMENT
BENEFITS
ESTIMATION
STAKEHOLDERS C
OMMUNICATION
REFINING
OUTPUT
PRODUCTION

18. Local Joiners Estimation
• Forecast of locally joining passengers are based on business
logic and prone to inaccuracy. Use of predictive analytics
and automation to forecast is opportunity for improved stuff
productivity, reduced overtime hours and better guest's
experience.
• Check-in Data, Reservation Data, Flight Schedule Data.
• EDA insights.
• 6 machine learning models.

19. Let's Get
Technical
Number of expected passengers checking
in for a given 30-minute interval for
a future calendar month
XGBoost was used for training
Dataiku Platform is used
for automation and
scheduling
Two scenarios
Data set checks
Model perfomance
checks

20. Dataiku Flow Design

21. Dataiku Web App Design

22. Monitoring Model Output and Perfomance

23. Ready To Depart
• Airline industry can seize opportunities
both in commercial and operational sector
with growing data science talent in-house
• Entering in DS field from quantitive field
• Data Science project is not only about
training models. It is complex process
which
includes understanding business requirem
ents, generating actionable insights
and interpretable machine learning
models, clearly communicating results and
benefits and working closely with data
engineers with purpose of putting project
in production

24. Z
Thank you for attention!
Questions ?