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Course Outline 2016 INFOSYS 722: Data Mining and Big Data (15 POINTS) Semester 2 (1165) Course Prescription Data mining and big data involves storing, processing, analysing and making sense of huge volumes of data extracted in many formats and from many sources. Using information systems frameworks and knowledge discovery concepts, this project-based and research oriented course uses latest published research and cutting-edge business intelligence tools for data analytics. Programme and Course Advice None Goals of the Course The primary objectives of the course are to: 1. Learn how to critically review literature in the area of business intelligence, big data, business data analytics, visualisation and relevant information systems architectures; 2. investigate links between big data, business data analytics and emerging technologies; 3. develop an understanding of the process of decision making; and 4. apply knowledge gained to design, implement and evaluate an information system – with reference to big data, data analytics and visualisation for an identified business case study; Learning Outcomes By the end of this course it is expected that the student will be able to: 1. critically read, discuss and write about published research; 2. develop a holistic research-based roadmap of the big data and business analytics fields; and 3. analyse, design and build an information system using emerging tools and technologies (including big data and business data analytics) for an identified business problem. Content Outline Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 6 &6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Introduction to big data and business intelligence Big data and its impacts Exploration of tools used for harnessing big data Capabilities of Business Intelligence and business data analytics Technologies enabling insights and decisions regarding BI Practical uses of Big Data and BI Exploring various data mining techniques and BI tools Exploring various BI Tools using case studies Exploring various Big Data tools in use Visualisation Emerging technologies: links with data analytics Presentations of Final Projects; handing in Research Essays Learning and Teaching Participants will be required to learn to use the library and other resources for their literature searches, carefully read research papers, write summaries, locate additional research papers, and discuss key research issues as part of the course. The learning in this course is student-centred. After the first two weeks during which time some of the important introductory papers in the field will be discussed, class meetings will have a seminar and workshop format, the success of which is heavily dependent on the active participation of the students. Teaching Staff: Dr. Ami Peiris Room 468, Owen G Glenn Building Email:[email protected] Phone: +64 (0)9 9235988 Learning Resources There are no text-books for this course. Photocopied journal articles and book chapters form an integral (and examinable) part of this course, and will be made available on Canvas. You are strongly advised to prepare brief summaries as you "digest" each reading. Especially useful in writing the research essay. Students are also advised to take advantage of a n y software and library resources available. Assessment Assessment No Course Component Weight 1 Class presentations (paper Summaries) 20% 2 Project 25% 3 Research essay 40% 4 Labs 15% Learning outcome/ Assessment Assessment Assessment Assessment Assessment 1 2 3 4 1 X 2 X 3 X X X X X X X Inclusive Learning Students are urged to discuss privately any impairment-related requirements face-toface and/or in written form with the course lecturer and/or tutor.