Data Science Economy 2019: Dawn of AI
2+ keynotes / 30+ talks
delivered from experts & practitioners

16th & 17th of May 2019, @KRAŠ Auditorium, Zagreb, Croatia
Data Science Team Lead, Neos, Croata

Albert Ćosić


Albert Ćosić is currently positioned as Data Science Team Lead & Solution Architect at part of Data Analytics Department at Neos.

Graduated Financial and Business Mathematics at PMF (Zagreb), started his carrier as life insurance Actuary at Wiener Osiguranje. In year to follow started with postgraduate study of Actuarial Mathematics.

Since joining Neos in 2014 he has been member of multiple project teams relating to Data Warehousing/Business Intelligence (DW & BI) and Data Science/Big Data as basis for quality improvement of business decisions.

Area of main interest are related to financial industry (credit risk and BI implementations) and applications of mathematical statistics in Data Science and Big Data areas and technologies (data mining, predictive modelling, machine learning, etc.)


Key ingredients of successful data science project?

Although we fully support the predictions advocated by most consulting firms that companies that do not invest in data science-based projects do not have a bright future – we are witnessing a large number of unsuccessful projects or ones with questionable profitability and expected results (according to recent Gartner research – big data projects failure rate is high as 60 and some argue that it even goes up to 85 percent).

According to data scientist practitioners and their leads there are few obstacles for successfully finishing data science projects. Often project starts from assumption based on previous experience or out of necessity to improve business operations using non-traditional methods. From assumption to hypothesis which need to be confirmed (or disproved) is a lot of unknown variables or at least not clearly defined ones. Project scope as well as duration, data sets and necessary tools are all subject to change as project develops. Additional specificity of data science project are project results. Final conclusions in form of structured presentation needs to properly and clearly expose project result. As results are not in form of application or report, it is expected to presentation material explain and assure contractors how project conclusion and developed modules will be integrated in business processes and IT system.

Since we at Neos do not like to participate in failed projects – and would like to continue with such practice – we decided to develop data science projects specific methodology/framework. This presentation will shortly explain methodology combining different concepts (ranging from design thinking to system engineering) which includes all steps, tools and expected results in context of mentioned data science project specifics with aim of project transparency and successfulness.