Overview

To develop a familiarity with a broad range of skills that are required to tackle the “big data” challenges of modern science-related careers. The subject introduces the fundamentals of e-Science and the key role that information technology plays in scientific discovery. This subject has a practical component, where students will build their skills in data analysis, visualization and programming.

Requisites

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date

Learning outcomes

Students who successfully complete this unit will be able to:

  • Select and use appropriate data analysis and visualization strategies
  • Explain and apply the fundamentals of computer programming
  • Explain the opportunities for, and challenges facing, scientific progress in the era of Data-Driven Science
  • Apply e-Science strategies and approaches to science discipline-specific requirements

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Face to Face Contact (Phasing out)
Lecture
2.00 12 weeks 24
Face to Face Contact (Phasing out)
Laboratory
1.00 10 weeks 10
Online Contact (Phasing out)
Online Learning Activities
0.67 12 weeks 8
Unspecified Learning Activities (Phasing out)
Independent Learning
9.00 12 weeks 108
TOTAL150

Sarawak

Type Hours per week Number of weeks Total (number of hours)
Face to Face Contact (Phasing out)
Lecture
2.00 12 weeks 24
Face to Face Contact (Phasing out)
Laboratory
2.00 12 weeks 24
Online Contact (Phasing out)
Online Learning Activities
0.67 12 weeks 8
Unspecified Learning Activities (Phasing out)
Independent Learning
7.83 12 weeks 94
TOTAL150

Assessment

Type Task Weighting ULO's
AssessmentIndividual 15 - 25% 1,2,3,4 
ExaminationIndividual 30 - 40% 1,2,3,4 
Laboratory ReportIndividual 35 - 45% 1,2,3,4 
Online TestsIndividual 10 - 20% 1,2,3,4 

Hurdle

As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:

(i) an aggregate mark of 50% or more, and(ii) at least 40% in the final exam.Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 45% as the total mark for the unit.

Content

  • Data-Driven Science and the evolution of the scientific method
  • Characterising data (mean, standard deviation, quartiles, median, mode, histograms)
  • Visualisation techniques and strategies – two-dimensional and three-dimensional data
  • Data mining and knowledge discovery
  • Information and communication technologies and methods for scientists
  • Fundamentals of computer programming
  • Algorithms, problem solving, and tools for scientists
  • Emerging technologies for e-Science
  • Big Data challenges and opportunities
  • Current issues in e-Science (e.g. ethics, privacy, and security of data)

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.