COURSES

Course 1: Open-Source Software Data Engineering
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The course will focus on various aspects of the open source software ecosystem and especially those related to the information and data that can be found there: (a) introduction to open source software repositories and data (b) OSS communities (c) OSS code reuse (d) OSS Assessment.

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Course 2: Social Networks Analytics
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The module aims to highlight cutting edge methodologies for social networks analytics which will contribute in understanding crowd sensing and online opinions formulation. The course will cover aspects and methods of online social networks data analytics, focusing on the importance of such information in business and industry. The current rapid impact of social networks on industrial and business settings will be addressed by collective intelligence and “wisdom of the crowds” approaches which will educate and empower Gradana audiences in understanding phenomena and patterns which unfold in todays social media platforms. In specific, the module will cover topics for : (a) Social Networks principles and fundamentals, with emphasis on collecting and identifying proper content and its metadata; (b) Social networks data structures and mining with emphasis on graph models and the communities detection; (c) sentiment analysis methodologies for social networks textual resources. Relevant use cases and best practices will be emphasized and real data exploitation will be thoroughly addressed and demonstrated.

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Course 3: Statistical Data Analysis in Software Engineering
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The module aims to provide new insights of the usage of modern and advanced data analytics and statistical methodologies in the demanding industry of information systems. Towards this direction, the participants will acquire significant knowledge and skills concerning different aspects of data-driven tasks in the development life-cycle and maintenance of Information Systems such as i) software cost estimation, ii) requirement prioritization, iii) defect prediction and iv) human factor in software development (peopleware). Generally, the course provides a broad coverage of different software engineering issues from an empirical and data-driven perspective through illustrative case studies on real datasets of information systems ecosystem implemented in the R statistical language.

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Course 4: Open-Source Software Data Engineering
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The course will cover (a) server virtualization including Hypervisors; (b) network virtualization; (c) Cloud Oss (d) state of the art techniques for energy efficiency like consolidation; and (e) scalability management

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Course 5: Distributed Systems
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(a) SLA languages, concepts, standards; (b) SLA management e.g., autonomic management, enforcement and enactment; (c) SLA negotiation models: auctions, negotiation protocols, meta negotiation, negotiation (d) SLAs and markets: managing market liquidity and (e) SLAs and emerging Cloud business models (f) current state of the art methodologies to build used defined networks like Virtual Network Embedding (VNE) and Software Defined Networks (SDN)

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Course 6: Big Data
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This module will provide an overview of Software architecture design and technical components in Big Data architectures: (a) Base technology and solutions (NoSQL, MapReduce, Hadoop and Apache Bigtop), (b) Theoretical insights (c) State-of-the-art big data architectures and extensions through the use of Linked Data standards and semantics (d) Application possibilities.

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Course 7: Citizen Science and Smart Cities
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This module will provide an introduction and overview of recent initiatives bringing together citizen science and smart city projects, focusing on both opportunities and success stories as well as technical challenges that are in the way of realising their full potential. In particular, the course will look at the current state of data infrastructures supporting smart cities and the various sensors feeding this data, including the possibilities enabled by considering citizens as sensors. The course will introduce i) standards and best practices for collecting and integrating data from diverse nodes in a digitalised city, ii) examples of how citizen science can further enrich smart cities in turn for an improved standard of living, and issues arising from involving private users, and iii) examples of how existing open data can already be exploited by entrepreneurs to generate value for citizens in return for new services.

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Course 8: Conducting Open Science: Approaches, Tools and Practices
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This module targets transversal skills of participants and is an introduction to the approaches, tools and common practices of Open Science. Concomitant terms (Open Notebook, Open Data, Open Research Software, Open Access) are explained and further developed as they can directly enrich each step of the scholarly lifecycle. The overall objective of the course is to provide an introduction to why Open Science is essential to rigorous, reproducible and transparent research.

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Course 9: Upgrading Soft Skills for Computer Engineers – Preparation for the Job Market
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This module aims to improve the skills and knowledge needed to create and deliver extensive level presentations in area of academic writing and speaking. The overall objective of the course is to provide a grasp on writing and presenting rigorous research in English.

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Course 10: Big Data in Practice
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This modules, which is the practical follow up of the course 7, is a practical and experimental use of Hadoop and MapReduce. Participants in this module will gain the practical knowledge of Improving Innovation and Entrepreneurship Competences of Iranian Higher Education Graduates through Data Analytics (GraDAna) programmes in Hadoop and MapReduce. The course is recommended for master and PhD candidates. This course will present tools, technologies and best practices in this area.

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Course 11: Tools and Technologies in Data Science: Introduction to R
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This course is an introductory course for undergraduate/graduate students. The typical challenges of today’s students are dealing with data, analyze and interpret the data and get insight out of it. To this aim the “R Course” is designed to equip students with the most demanded numerical skills in the domain of data analysis.

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