Usability of network data 2700-M-LM-D3UDSI
The course is designed to familiarize students with the basic concepts and ideas related to the usability of network data and to present current developments and trends in the field of network data analysis. Students will learn about new techniques and tools used in network data analysis, as well as how advanced algorithms and artificial intelligence techniques used in this field work. They will learn about the role of cryptography in securing network data and the history of the development of computers and digital techniques in this context.
As part of the course, we will try to analyze various methods of collecting, processing and interpreting network data, and new trends, such as the impact of the Internet of Things (IoT), on network data analysis will be discussed. In addition, new inventions and innovations in the field of network data will be presented, and ethical and legal aspects related to their usability will be discussed.
It's also a consideration of the challenges of real-time network data analysis and practical applications of network data analysis in various fields such as marketing, cybersecurity, medicine, etc. Journalists and media personnel also need to approach network data visualization techniques with interest, so that the viewer can better understand the results presented. Methods for evaluating and measuring the usefulness of network data will be presented, and the role of network data analysis in business decision-making will be analyzed.
As part of the discussion, the challenges of confidentiality and privacy of network data will be addressed, and examples of the application of network data analysis in real-world cases will be presented. The subject will also include a discussion of future trends and forecasts in network data analytics, and students will be encouraged to explore for themselves the latest developments and scientific publications in the field.
There are many tools that can be used to analyze network data. Some of them are Wireshark, Network Performance Monitor, PRTG Networking Monitor, Network Traffic Analyzer, NetSpot and LanGuard. Open source tools available on the market include NetworkX, iGraph in R, and Gephi. Of all the tools, Gephi is considered the most recommended tool that can help easily visualize more than 100,000 nodes. The aforementioned programs will also be discussed.
Students will be encouraged to experiment with different tools and choose the one that best meets their needs. They will also learn how to use these tools effectively and how to integrate them with each other to get the best results. The lecture will be delivered from a humanistic position - students must understand complex processes and use the data they receive in their work.
Main fields of studies for MISMaP
Prerequisites (description)
Course coordinators
Term 2024Z: | Term 2023Z: |
Learning outcomes
KNOWLEDGE
Students will learn basic concepts and terms related to network data usability.
They will be aware of current developments and trends in network data analysis.
They will gain knowledge of the role of cryptography in securing network data.
They will learn about the history of the development of computers and digital techniques in the context of network data.
They will gain knowledge of various methods of collecting, processing and interpreting network data.
They will be aware of the impact of new trends, such as the Internet of Things (IoT), on network data analysis.
They will learn about new inventions and innovative solutions in the field of network data.
They will gain knowledge of the ethical and legal aspects related to the usability of network data.
They will be aware of the challenges associated with real-time network data analysis.
They will gain knowledge of future trends and forecasts in network data analysis.
They will become familiar with forms of digital multimedia recording
They will be familiar with the current information and service potential available on the Internet, They will be familiar with the technical conditions of the Network,
They know the basic forms of media presence on the Internet - press, radio and television, as well as examples of social networking, citizen journalism.
They know the essence of the use of Big Data information resources
SKILLS
Students will be able to use new techniques and tools used in network data analysis.
They will gain the ability to work with advanced algorithms and artificial intelligence techniques used in network data analysis.
They will learn to analyze network data, including methods of collection, processing and interpretation.
They will know how to evaluate and measure the utility of network data.
They will gain the ability to identify challenges related to confidentiality and privacy of network data.
They will learn to use network data visualization tools to better understand and present results.
They will develop the ability to analyze network data in the context of business decision-making.
They will be able to recognize practical applications of network data analysis in various fields such as marketing, cybersecurity, medicine, etc.
Students will be able to take advantage in journalistic work of the information and service potential available on the Internet, and identify the technical requirements for efficient use of the Internet,
Students will be able to adapt the choice of the form of media presence on the Internet to their own journalistic work.
OTHER COMPETENCIES
Students will develop the ability to independently explore and investigate the latest developments and scientific publications related to the usability of network data.
They will learn to adapt tools and select those that best meet their needs.
They will gain competence in integrating various tools and using them effectively in network data analysis.
They will be aware of the dynamics of change in the convergence of old and new media and information technologies relevant to journalistic work.
They will develop the ability to process complex processes and received data.
They will be aware of the dynamics of change in the convergence of old and new media, disappearing and emerging information technologies relevant to the work of a journalist.
Assessment criteria
The final grade consists of:
Test - dozens (50-60) single-choice questions (scope: knowledge from lectures). Criterion: a minimum of 60% correct answers. /Test always has a presented range of the number of points for a specific grade/.
- Possibility to pass in addition to the colloquium work on the semester project carried out in teams (then 50% project / 50% test).
Individual elements make up the final grade.
Practical placement
lack-of
Bibliography
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Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: