Information on this page, including unit offerings, is from the 2019 academic year.
Foundations of Data Science (ICT515)
|School||School of Engineering and Information Technology|
|Teaching Timetables||Murdoch S1
|Description||The objectives of this unit are to introduce students to an understanding of data, uncovering patterns within data and developing models whose prediction accuracy on future, unknown data can be quantified. Major topics include: concepts of statistical learning; machine learning algorithms; over-fitting and model tuning; regression models; classification models; recommendation engines and social networks; assessing model performance; extracting meaning from data. The R programming language and software environment will be introduced to students and will be used to demonstrate implementations.|
|Unit Learning Outcomes||Upon completion of this unit, students should:
ULO 01. Be able to demonstrate a practical and theoretical understanding of the terminology, principles, fundamental tools and techniques of data analysis.
ULO 02. Be able to apply statistical learning methods in real life applications.
ULO 03.Demonstrate awareness of the applicability of modern, powerful predictive models to different domains.
ULO 04. Be able to demonstrate knowledge of using the R programming language and software environment for data analysis.
|Timetabled Learning Activities||Lectures: 1 x 2 hours per week: workshops: 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 workshops). This combination will allow students to demonstrate understanding of the fundamental concepts/methods of data science and then to put them in use in their weekly workshop assignments. In the workshops, depending on the nature of each weekly task, students may work individually or in pairs. The first assignment will be individually completed by students, while the second assignment will be completed in pairs, and students will need to present in class the findings of their data analysis project.
The use of blended learning in this unit will be prominent. Students will be able to access, through Moodle, a variety of additional material (papers, videos, 10-minute optional assignments for self-assessment) which will help them grasp the fundamentals of data science at their own pace.
|Assessment||The assessment consists of participation in the workshops, a project proposal, an interim report, and final report and a number of presentations.
Students demonstrate their learning through workshop work, problem-based assignments (involving solution design, implementation, testing and documentation) and a final examination. The final grade for the unit will be reported as a letter grade and a mark. In order to pass the unit students must have an aggregate score for the combined assessment of 50% or better.
|Prerequisites||Enrolment in an IT graduate course or permission of the Academic Chair.|
|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 two assignments 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.|