Data Science Master 4.0 on Belgrade University - Drazen Draskovic

Editor's Notes

• #2: Education for Digital Transformation and a new wave of Data Science - the challenge for Serbia
• #5: Division for data processing was formed within our school in 1967. We have been engaged in this field,for over 50 years. The Computer Engineering Chair later emerged from that division
• #6: in accordance with ACM/IEEE All courses have the mandatory practical project, which has all requirements of a real industrial project. Also, students can work in the latest programming languages and software tools for Data Scientist.
• #7: In February 2019, MINISTRY published the call for new multidisciplinary master program, based on the collaboration of academia and IT companies. The cooperation between 2 higher education institutions was the minimum requirements, and we prepared new program in synergy with the Faculty of Organizational Sciences.
• #8: The Master thesis will be with a research character and the work on the thesis was followed by 2 mentors, from academia and from industry.
• #10: Like any responsible institution, we have set several objectives when we developed this program.
• #13: As the saying goes: The world remains for the younger.
• #16: Team: Different level od education and experience, but mostly from quantitive fields Me: Engineering background, mostly with research experience, SC first contact with clients and creating DS producti from scratch to production  Etihad: working in DS team, devoloping models but closely cooperating with Data Engineers and business stakeholders 
• #18: How does it look like?  PS: Understand existing methods, detect it's weaknesses or space for improvement,  define the problem  with clearity and conciseness -> ask questions (1-2 days) Data: Once you understand the problem, describe kind of data you need -> communicate data prerequasities to stakeholder of domain expert             It is good in this phase to understand if you can obtain data in real time, to detect the latency of data extraction -> these can affect your solution design later (1 week) EDA: This is the place where questions should be asked -> set of features which pottentialy can be beneficial for model (1 week) Model Development: Trying out different models, understanding the best way to train and validate, estimate the quality of model ( are errors uniformly distributed, is model robust enough/adaptive enough) - > model and training/testing  method (1 week) Benefit Estimation: transfer the predictions to $$ - > numbers (1 week) Communication: presenting to stakeholders solution design, communicating the benefits and risk, estimating the cost of solution ( additional costs for vendors) -> stakeholders feedback  Refining Output: modyfing the solution design according to the feedback (1-2 days up to week) Production: Creating production code at Dataiku (with automated scenarios), generating instructions for data enginners, testing (2 weeks)
• #19: Problem Statement : More local joiners require more rostered staff and/or more staff allocated to check-in counters, in order to ensure a good customer experience and that queue lengths are not overly long. Ideally, one could know the expected volume of passengers arriving at the airport in advance. Appropriate staffing volumes would also reduce our reliance on staff to perform overtime to cover staffing shortages. Less overtime means less manpower costs. Data: Daily updated EDA: arrival curves, route region based (ppl arriving earlier for USA flights than GCC flights) Solution Design: Models for economy, business, bagdrop counters for 1 day and 1 month ahead.
• #21: Jesam li ovo stavila sto je lepo ili sto cu stvarno nesto pametno da kazem ne znam, razmisli jos o ovome 
• #23: Ovo je lepo