Information on this page, including unit offerings, is from the 2020 academic year.
Advanced Data Analysis (ICT602)
|Organisational Unit||Information Technology, Mathematics and Statistics|
|Teaching Timetables||Murdoch S1
|Description||This unit introduces students to advanced computational intelligence and statistical concepts and tools used for harvesting, processing, analysing and visualising real-world data. Students will develop an in-depth understanding of the nature of data specific to various application domains, and learn how to design and implement solutions using a combination of machine learning and statistical analysis methods and tools. Students will look at a number of characteristic problems/data sets and use the Python programming language to implement prototype solutions.|
|Unit Learning Outcomes||Upon completion of this unit, students should:
ULO 01. Be able to demonstrate an in-depth theoretical and practical understanding of advanced computational and statistical tools and methods for advanced data analysis.
ULO 02. Be able to evaluate and apply machine learning and statistical analysis methods in real-life applications.
ULO 03. Demonstrate and articulate a critical understanding of the latest approaches, theories, and research activities in data science.
ULO 04. Be able to demonstrate knowledge of using the Python programming language and software environment for advanced data analysis.
|Timetabled Learning Activities||Lectures/Workshops: 3 hours per week (incorporating lecture:1 x 2 hours per week; practicals: 1 hour per week).|
|Unit Learning Experiences||The unit will be taught as a combination of structured timetabled learning (in the form of lectures) and semi-structured learning (in the form of workshop/ seminars). The seminar content which will lead the participants in performing in-depth, critical, and rational evaluations of cutting-edge tools, technologies, techniques, approaches, theories and research activities in data science.
The combination of lectures and workshop/seminars will allow students to initially understand the fundamental concepts/methods of advanced data analysis and then to put them in use in their weekly workshop tasks.
The use of blended learning in this unit will be prominent. Students will be able to access, through the unit site on LMS, a variety of additional material, such as papers and videos, which will help them grasp the concepts of the unit at their own pace.
|Assessment||Students are assessed on the basis of two assignments (total 60%) and a final examination (40%). The first assignment will be composed of a set of short problems. The second assignment will be a project. In this, the students will be required to design and implement, using Python, the solution to a real problem. Then students will have to write a report in the form of a research paper summarising the academic criticism of the respective active area of research in data science, consisting of a short literature review, the experimentation and results, which culminates in a critical summary.
In order to pass the unit student must have an aggregate score for the combined assessment of 50% or better.
|Prerequisites||ICT515 Foundations of Data Science or ICT513 Data Analytics.|
|Notes||Each student is expected to spend on average three hours per teaching week reading the lecture notes, books chapters and other recommended materials relevant to the topic covered in that week and spend a similar amount of time working on the workshop exercises for that week. In addition, each student is required to complete an assignment and sit the final examination.|
|Appears in these Courses/Majors:
see individual structures for context
|Internet Access Requirements||Murdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.|