Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: The Application of Data Science Concepts in Engineering Software (17.E2S07)

Native organizations units: No data

Type of studies	Title
Master Academic Studies	Software Engineering and Information Technologies (Godina: 1, Winter)
Master Academic Studies	Computing and Control Engineering (Godina: 1, Winter)

General information:

Category	Scientific-professional
Scientific or art field	Applied Computer Science and Informatics
Interdisciplinary	No
ECTS	6

Educational goal:

Enable students to apply modern methods, tools and best practices in the process of transforming heterogeneous data sets into usable knowledge. Raise awareness about the role of formal presentation of knowledge and its use in intelligent information systems. Enable students to apply methods, techniques, technologies and tools in the process of data transformation into knowledge.

Educational outcome:

After successfully completing the course, students are able to: use modern techniques and tools in the development of systems based on data transformation into knowledge (integrated environments, domain specific languages, etc.) and successfully collaborate on developing components of software systems that provide support for the integration of heterogeneous sources Data in the context of intelligent information systems. They are able to: use modeling and abstraction to manage the process of data transformation into knowledge at all stages of the life cycle of the knowledge warehouse. They are able to use the specification and model elements in the process of verifying and validating components for data transformation into knowledge.

Course content:

Advanced principles of the system based on data. Modern tools to support data transformation into knowledge, information templates. Methods of techniques and tools for collecting data, verifying the integrity and quality of the collected data and their sharing as resources within complex systems based on data / information / knowledge. Basic concepts and concepts of data engineering. Relationship between information and knowledge. Methods, techniques and tools for data analysis. Use of R language and RStudia. Mechanisms, methods and tools for displaying (reproduction) the collected data. Elements of statistical conclusion, regression models, machine learning elements. Data, information and knowledge as products. System modeling and formalisms related to the description of the structure and behavior of complex systems based on the transformation of data into usable knowledge. Practical part: installation, setup and use of an integrated development environment to support the transformation of data into usable knowledge; Implementation of information templates. Installing, setting up and using clients for the selected system for data transformation into knowledge. Installation, configuration and use of the system for handling heterogeneous data / information / knowledge warehouse. Installation, configuration and use of the service layer for accessing formatted knowledge to the multilayer architecture.

Teaching methods:

Lectures; Computer exercises; Consultations. Project. Continually monitor the use of the version control system, the project management system, the testing framework, and the documentation framework through the project task. As part of the course, students divided into teams realize components for supporting the data / information / knowledge layer within a complex business information system. The methodological approach is based on the creation of a document vision vision model and a functional model of developed components. The specification-guided development enables later verification and validation of the data / information / knowledge handling components in relation to their specification.

Literature:

Authors	Title	Year	Publisher	Language
Martin Kleppman	Designing Data-Intensive Applications The Big Ideas Behind Reliable, Scalable, and Maintainable Systems 1st Edition	2015	Martin Kleppman	English
Roger D. Peng	R Programming for Data Science	2015	elektronska verzija	English
Jeffrey Stanton	Introduction to data science	2013	Syracuse University’s School of Information Studies - elektronsko izdanje	English
MOHAMMED J. ZAKI, WAGNER MEIRA JR.	DATA MINING AND ANALYSIS Fundamental Concepts and Algorithms	2014	Cambridge University Press - elektronsko izdanje	English
Petra Kuhnert and Bill Venables	An Introduction to R:Software for StatisticalModelling & Computing	2005	CSIRO Australia - elektronsko izdanje	English
Peter Harrington	Machine Learning in Action	2012	Manning	English
George Casella, Roger L. Berger	Statistical Inference	2002	elektronsko izdanje	English
Stephen Marsland	Machine Learning An Algoritmic Perspective	2009	CRC Press	English
Reza Zafarani, Mohammad Ali Abbasi and Huan Liu	Social Media Mining	2014	Cambridge university Press - elektronsko izdanje	English
Jure Leskovec, Anand Rajaraman, Jeff Ullman	Mining of Massive Datasets	2014	elektronsko izdanje	English