BUIDING AN UNDERGRADUATE COURSE IN DATA-DRIVEN METHODOLOGIES
BUIDING AN UNDERGRADUATE COURSE IN DATA-DRIVEN METHODOLOGIES

Summary/Abstract: A data specialist is an emerging professional profile oriented towards collection, filtering, analysis, visualization, management and preservation of huge collections of information. Even some tasks of database managers, computer programmers, or statistical data analysers are common for a "data scientist", many other skills are required. A data scientist should be, as possible as, a quantitative analyst, a data management expert, and should have a solid foundation in math, statistics, probability, and computer science and strong social skills. This paper describes the architecture of an undergraduate course in data-driven methodologies, an advanced one, making use of fundamental knowledge and introducing new topics coming from cloud development and big data processing. The following tracks are considered: data management, data analytics and pattern discovery, modern data bases and distributed systems, mathematics and statistical software packages, econometry and quantitative finance, operational research, computational social science, social network and graph analysis, soft computing based decision making, data analysis fields, data science open source tools etc. Twelve chapters will address the planned content based on demonstrative projects using Hadoop, MapReduce, R, and Python packages. The allocated time is 56 hours, with 28 hours of lectures. Also, the data scientist profession is defined according to the market reaction. To fulfil the objective, various open resources are identified and linked to a digital front-end of the course. In conclusion, the aim of the project is to offer such a course in a blended manner, in order to create the need for a professional master program in data science.

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