SDEB022 Дисруптивни иновации в ИКТ (Disruptive Innovations in ICT)

Анотация:

The course is designed for students studying service engineering in telecommunications and computer systems and technologies. The educational content is targeting a wider audience of professionals wishing to acquire the basics of next-generation distributed systems for storing information (multimedia, images, text, graphics data etc.) used in corporate systems and social networks for business communication. The course is focused on the theoretical essentials within a practically oriented ambiance for a better understanding of actual telecommunication service functions based on Big Data. Students will be involved to make solve close-to-real cases with real prototypes of open source systems. They have to use large amounts of data with open access as a demo version to integrate them into smart social networking tools.

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Телекомуникации

Преподавател(и):

гл. ас. Полина Михова  д-р
доц. Георги Петров  д-р

Описание на курса:

Компетенции:

Successfully graduated students:

1) know:

? Disruptive innovation concepts enshrined within Big Data Analytics and background technology systems for Data Storage

? What fields of application Big Data insights have as disruptive innovation.

? Innovations and development trends in the field of insights.

2) can:

? Install and maintain Apache ™ Hadoop®

? Use analysis tools for graphics, video, audio and text data.


Предварителни изисквания:
? The architecture of telecommunications networks, working with the Internet, basic software, work with different operating systems, basic telecommunications services, installation and configuration of computer systems.

Форми на провеждане:
Редовен

Учебни форми:
Лекция

Език, на който се води курса:
Английски

Теми, които се разглеждат в курса:

1 Business based on Data design; User apps and business solutions with Big Data.

2 The crucial role of Big Data as a disruptive innovation in your company; Social networking, business analyzes and forecasts, streaming video and security systems, medical systems, legal information, meteorology and others; Cloud essentials, solutions and applications.

3 Systems Storage: architecture, evolution, data networks, methods and techniques of protection and data backup, distributed systems for data storage, business systems, open systems.

4 Basic concepts in databases: species evolution, applications, languages retrieval records and more. data (SQL similar languages).

5 Corporate solutions and platforms to ensure Big Data.

6 Problems of large databases, distributed storage of information along the lines of: Youtube, Facebook and others.

7 Distributing systems for collecting data (consumer, insurance, medical, technological, ecological and meteorological and others. Internet of Things)

8 Practical work: Methods to retrieve data via the command line and by distributing sources.

9 Practical work: Installing and configuring Apache ™ and Hadoop®.

10 Practical work: Transaction processing with Hadoop.

11 Statistical methods and data analysis.

12 Software applications for analyzing big data.

13 Visualization of Big Data.

14 Practical work: OS Big Data Databases, Business Intelligence Tools, Data Mining Tools

15 Practical work: OS Big Data File Systems and Programming Languages, Big Data Tools: Transfer and Aggregate, OS Tools for Big Data

Литература по темите:

1. CHRISTENSEN: The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail (Management of Innovation and Change)

2. Data Science and Big Data Analytics by EMC Education Services

3. DEAN, JARED: Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners.

4. GEMIGNANI, ZACH et al.: Data Fluency: Empowering Your Organization with Effective Data Communication.

5. GLASS, RUSSELL / CALLAHAN, SEAN: The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors and Boost Profits.

6. HERBEN, DIO: Big Data, Big Analytics: Emerging Business Intelligence.

7. IMMON, W.H. / LINSTEDT, DAN: Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

8. JANSSENS, JEROEN: Data Science at the Command Line: Facing the Future with Time-Tested Tools.

9. KERNS, CHRIS: Trendology: Building an Advantage through Data-Driven Real-Time Marketing.

10. MAYER-SCHONBERGER / CUKIER, KENETH: Big Data: A Revolution That Will Transform How We Live, Work, and Think, 2010.

11. MORRISON, RAYMOND: Big Data Now. 2014.

12. OJEDA, TONY et al.: Practical Data Science Cookbook.

13. PAGANONI, ANNA MARIA / SECCHI, PIERCESARE: Advances in Complex Data Modeling and Computational Methods in Statistics.

14. SILVER, NATE / COOK, GARETH: The Best American Infographics, 2014.

15. TOWNSEND, ANTHONY: Smart Cities: Big Data, Civic Hackers and the Quest for the New Utopia.

Средства за оценяване:

АНАЛИЗ (РЕАЛЕН КАЗУС) 30%

РАБОТА В ГРУПИ 35%

ДИСКУСИОННО УЧАСТИЕ 35%