Big Data. Refining Information

General data

Course ID:	2700-M-BIGD-FAK-ANG
Erasmus code / ISCED:	15.1 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0321) Journalism and reporting The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Big Data. Refining Information
Name in Polish:	Big Data. Refining Information
Organizational unit:	Faculty of Journalism, Information and Book Studies
Course groups:	(in Polish) Zajęcia fakultatywne w j. angielskim dla studiów studiów stacjonarnych II stopnia
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Main fields of studies for MISMaP:	computer science
Type of course:	elective courses
Prerequisites (description):	(in Polish) The base: Nearly all information generated over the world is recorded in digital form. Resources of this scale are referred to as Big Data. Analysis – refining (including adopting sentimental analyses) of this information - provides a new source of valuable information for business, education, journalism and other fields.
Mode:	Classroom
Short description:	The aim: discussion of a set of tools (procedures, software and hardware) for refining Big Data (Information Resources Network). These tools can provide a true picture of the past, the image in real time and predictions. During the course will be shown the basic steps of refining: 1. Collecting information (robot); 2. Recognition of text entries available in the non-text format; 3. subsystem self-learning identification sentiments; 4. Subsystem analysis of the content of messages; 5. Creating a model of the examined process; 6. Subsystem of the presentation of the results; Examples: case studies.
Full description:	1. Forms of creating, recording, processing information in digital form (multimedia). 2. The current conditions, tools and methodology of IT use in processing multimedia. 3. Assessment of readability of texts. 4. Potential of Information of the indexed Net, Deep Web and Dark Web. 5. Search engines bias: the results of global research. Manipulation of information in the Network. Personalize of the Net Information 6. Big Data - The value of the hidden information. 7. Refining of information. The new source of information for science (business, politics, media and others fields). 8. Robots. Collecting text and no-text (audio, pictures of text,) information from the Web. 9. Identification of personalized and current sentiments for sentiment analyze. Live time of the sentiments. 10. Models of predictions. 11. The Cases. Methodology, results. 12. Workshops. Development and monitoring of practical projects for students. The idea of the projects: to familiarize students with the information richness of Big Data and proper identification of sentiments to sentiment analyze .

This course is not currently offered.

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