COMP5048 Assessment Answers Australia

With the advancement of technology and artificial intelligence, the demand for computer science students has increased rapidly. Visual Analytics is regarded as one of the major parts of computer science. The use of sophisticated tools and procedures to evaluate datasets using visual representations of the data is known as visual analytics. Users can find patterns and produce actionable insights by visualising data in graphs, charts, and maps. These insights assist businesses in making better data-driven decisions.

While studying this course, if you ever feel like taking assignment help for completing your COMP5048 assessment answers then we are to assist you with that. Information Visualisation is the goal of Visual Analytics, which tries to make the data analytics process easier. The goal of information visualisation is to create attractive representations of abstract data such as market prices, family trees, and software design diagrams.

This information can be conveyed quickly and effectively through well-designed images. Visual Analytics' challenge is to design and implement effective Visualisation methods that produce pictorial representations of complex data so that data analysts from various fields (bioinformatics, social networks, software visualisation, and network) can visually inspect complex data and make critical decisions.

Basic HCI principles, visualisation approaches, and foundational algorithms will be covered in this unit in order to obtain good visualisation of abstract data. It will also open up possibilities for academic study and the development of new Visual Analytic methodologies.

What are the Learning Outcomes of this Course?

Our COMP5048 academic assistance experts say that the course will help you to understand the importance of computer science and visual analytics. This course will help you to comprehend how this is used in various fields. To get more understanding of this take a look at the learning outcomes jotted here -

• To show complicated data, use suitable visual variables, space utilisation methodologies, and degrees of visual component organisation.
• choose, implement, and change visualisation methods appropriate for a specific issue area in order to assist data analysis via visual inspection
• comprehend fundamental computing principles, methodologies, and algorithms in order to build effective visualisations of abstract data
• grasp the fundamental principles of human-computer interaction that drive the creation of good/effective visualisation.

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What are the Perks of Visual Analytics?

There are numerous perks of visual analytics, our assignment helpers say that understanding visual analytics can assist you in various ways. Here are some benefits of visual analytics jotted, take a quick look at it -

• Users may measure, organise, and share important performance indicators throughout a company using interactive reports and dashboards.
• Make quicker decisions: Seeing and dealing with data sets in a visual manner allows users to grasp data insights much more rapidly.
• More easily explore data: Self-service analytics solutions that allow users to engage with data in a visual environment allow them to identify hidden links and patterns in data without the need for IT assistance.
• Educate people about data: Making data easy to work with and comprehend democratises data analytics, allowing more individuals and departments to participate.

Study materials for completing the course on time

If you are stuck somewhere and need help with assignment solution on COMP5048 then take a look at the sources added below. This will assist you in understanding the topics thoroughly. Take a look at the images jotted here -

• Keim, Daniel, Gennady Andrienko, Jean-Daniel Fekete, Carsten Grg, Jrn Kohlhammer, and Guy Melanon. "Visual analytics: Definition, process, and challenges." In Information visualisation, pp. 154-175. Springer, Berlin, Heidelberg, 2008.
• Keim, Daniel A., Florian Mansmann, Jrn Schneidewind, Jim Thomas, and Hartmut Ziegler. "Visual analytics: Scope and challenges." In Visual data mining, pp. 76-90. Springer, Berlin, Heidelberg, 2008.
• Thomas, James J., and Kristin A. Cook. "A visual analytics agenda." IEEE computer graphics and applications 26, no. 1 (2006): 10-13.
• Keim, Daniel A., Florian Mansmann, Andreas Stoffel, and Hartmut Ziegler. "Visual analytics." (2008).
• Keim, Daniel A., Florian Mansmann, and Jim Thomas. "Visual analytics: how much visualisation and how much analytics?." ACM SIGKDD Explorations Newsletter 11, no. 2 (2010): 5-8.
• Cui, Wenqiang. "Visual Analytics: A comprehensive overview." IEEE Access 7 (2019): 81555-81573.
• Sun, G.D., Wu, Y.C., Liang, R.H. and Liu, S.X., 2013. A survey of visual analytics techniques and applications: State-of-the-art research and future challenges. Journal of Computer Science and Technology, 28(5), pp.852-867.
• Andrienko, Gennady, Natalia Andrienko, Peter Bak, Daniel Keim, and Stefan Wrobel. Visual analytics of movement. Springer Science Business Media, 2013.

What is the Purpose of Visual Analytics?

The goal of data visualisation is self-evident. It is to make sense of the data and put it to good use for the company. Data, on the other hand, is intricate, and it gains greater value as it is displayed. It's difficult to swiftly explain data discoveries and detect trends, much alone draw insights and engage with data, without visualisation.

Without visualisation, data scientists can identify patterns and flaws. It is, nevertheless, vital to convey data discoveries and extract key information from them. Interactive data visualisation tools make all the difference in this case.

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