Introduction to e-Science
Duration
- One semester or equivalent
Contact hours
- 34 Hours
On-campus unit delivery combines face-to-face and digital learning.
Prerequisites
Nil
Assumed Knowledge
VCE Mathematics (any) or Year 12 Mathematics
Aims and objectives
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.
Unit Learning Outcomes (ULO)
1. Select and use appropriate data analysis and visualization strategies
2. Explain and apply the fundamentals of computer programming
3. Explain the opportunities for, and challenges facing, scientific progress in the era of Data-Driven Science
4. Apply e-Science strategies and approaches to science discipline-specific requirements
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
2. Explain and apply the fundamentals of computer programming
3. Explain the opportunities for, and challenges facing, scientific progress in the era of Data-Driven Science
4. Apply e-Science strategies and approaches to science discipline-specific requirements
Unit information in detail
- Teaching methods, assessment, general skills outcomes and content.
Teaching methods
Scheduled face to face: Lectures (24 hours), Laboratory Work (10 hours (Hawthorn))
*Scheduled synchronous online Learning events N/A
Non-scheduled online learning events and activities: Online resources, lab discussion groups and online tests (Approx. 8 hrs)
Other non-scheduled learning events and activities including independent study (Approx. 120 hrs (Hawthorn)
*Scheduled synchronous online Learning events N/A
Non-scheduled online learning events and activities: Online resources, lab discussion groups and online tests (Approx. 8 hrs)
Other non-scheduled learning events and activities including independent study (Approx. 120 hrs (Hawthorn)
Assessment
| Types | Individual or Group task | Weighting | Assesses attainment of these ULOs |
| Examination | Individual | 30-40% | 1,2,3,4 |
| Computer Laboratory Reports | Individual | 35-45% | 1,2,3,4 |
| Research Questions (H) /Topic Activities (S) | Individual | 15-25% | 1,2,3,4 |
| Online Tests | Individual | 10-20% | 1,2,3,4 |
Minimum requirements to pass this Unit
As the minimum requirements of assessment to pass the unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:
(i) An aggregate mark of 50% or more, and
(ii) Obtain at least 40% in the final exam
Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 44% as the total mark for the unit and will not be eligible for a conceded pass.
General skills outcomes
During this unit students will receive feedback on the following key generic skills:
• problem solving skills
• analysis skills
• communication skills
• ability to tackle unfamiliar problems, and
• ability to work independently
• problem solving skills
• analysis skills
• communication skills
• ability to tackle unfamiliar problems, and
• ability to work independently
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)
• 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
- References.
References
A list of reading materials and/or required texts will be made available in the Unit Outline.