Data analytics and visualization

1500 - 2000 words in total Introduction: Data visualisation, transforming data, information, and knowledge into visual representations, is very important to users because it could provide comprehensive insights for different analytics purpose. In particular, such insights help commercial firms, public sector non-governmental and others to make more informed decisions, backed with graphs and figures. The boom in big data analytics has triggered broad utilization of data visualisation in a variety of domains, such as finance, marketing, economy, politics, and many others. Visual analytics on the other hand, is an outgrowth of data visualisation that focuses on analytical reasoning facilitated by interactive visual interfaces. It has overlapping goals with data visualisation, but it is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making). You are required to individually develop a review and analysis report on the topic of ‘Contemporary data visualisation and visual analytics for big data’. The report presented should have the following contents: 1. The clear definition, relation and comparison of these two visualisation areas 2. How data visualisation and visual analytics help today’s decision-making with big data 3. The research and practical challenges brought by several different big data to these areas and the related state-of-art research problems in these areas. 4. Choose ONE: ‘The state-of-art tools used in these areas (R, Tableau and Excel etc.) and their comparison’ OR ‘The advanced visualisation and visual analytics methods/algorithms’ Preliminary references Data Visualisation: • Nikos Bikaks (2018) “Big Data Visualization Tools” Encyclopedia of Big Data Technologies, Springer 2018. • Dianne Cook, Eun-Kyung Lee, Mahbubul Majumder “Data Visualization and Statistical Graphics in Big Data Analysis” Annual Review of Statistics and Its Application 2016 3:1, 133-159 • W. Chen, F. Guo and F. Wang, “A Survey of Traffic Data Visualization” in IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 2970-2984, Dec. 2015. • J. Kehrer and H. Hauser, "Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey," in IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 3, pp. 495-

513, March 2013. • C.L. Philip Chen, Chun-Yang Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Information Sciences, Volume 275, 2014, Pages 314-347. • James D. Miller, “Big Data Visualization”, Packt Publishing Ltd, 2017 • Daniel G. Murray, “Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software”, 2nd Edition, Wiley, 2016 Visual analytics: • M. Behrisch et al., "Commercial Visual Analytics Systems-Advances in the Big Data Analytics Field," in IEEE Transactions on Visualization and Computer Graphics. • J. Liu, T. Tang, W. Wang, B. Xu, X. Kong and F. Xia, "A Survey of Scholarly Data Visualization," in IEEE Access, vol. 6, pp. 19205-19221, 2018. • Sun GD, Wu YC, Liang RH et al. “A survey of visual analytics techniques and applications: State-of-the-art research and future challenges”. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 28(5): 852–867 Sept. 2013. DOI 10.1007/s11390-013-1383-8 • Y. Wu, N. Cao, D. Gotz, Y. Tan and D. A. Keim, "A Survey on Visual Analytics of Social Media Data," in IEEE Transactions on Multimedia, vol. 18, no. 11, pp. 2135-2148, Nov. 2016. • L. Zhang et al., "Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems," 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), Seattle, WA, 2012, pp. 173-182.