What happens to you if your ancestors have a terrible start in life? Could it influence your future livelihood and health? How is climate change affecting your favourite surf break or fishing spot? Will massive green-blobs of algae take over the coastal ocean? The answers to these questions are at the tip of everyone’s fingers, but how do we generate the knowledge to answer these questions? Data science is rapidly growing as a new way to generate knowledge, rivalling the scientific technique of hypothesis testing & experimentation. Huge, publicaly accessible data sets are becoming increasingly available, and understanding how to gather, process, and visualize this data to generate new knowledge is a vital skill necessary for graduates across all disciplines. Utilizing concepts of computation thinking and spanning a variety of case studies in marine science, engineering, logistics and management, social history and archaeology, students will learn how to unlock insights from some of the world’s greatest data repositories and archives. The unit widens the understanding of the complex world of data acquisition, processing, interpretation, and visualization, utilizing modern, effective, yet simple, computer-intensive procedures for visualization and analysis. Within the unit, students will familiarize themselves with how to work and interact with high-level computational environments, such as MATLAB, to access data repositories and generate new knowledge. Students will also explore how the spatial and sensory experiences of data visualizations can affect learning affect learning and debate the ethical considerations of data sharing. The unit is not meant to teach a programming language, but provide approaches to using programming recipes and tools to examine different data domains in different interdisciplinary settings in order to begin to address complexity of real world interdisciplinary problems.
|Unit name||Making Data Work for Us - Simplifying a Complex World|
|Faculty/School||College of Sciences and Engineering
Institute for Marine & Antarctic Studies
|Discipline||Education|History and Classics|National Centre for Maritime Engineering and Hydrodynamics|Maritime and Logistics Management|Information & Communication Technology|Mathematics|Ecology and Biodiversity|
Andrew Fluck, Hamish Maxwell-Stewart, Nataliya Nikolova, Kiril Tenekedjiev, Anya Reading, Saurabh Garg
|Available as student elective?||No|
|Location||Study period||Attendance options||Available to|
|Launceston||Semester 2||On-Campus||Off-Campus||International International||Domestic Domestic|
- International students
- Domestic students
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
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|Study Period||Start date||Census date||WW date||End date|
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
On completion of this unit, you will be able to:
- On completing this unit, you will be to acquire, process, interpret and visualize large data sets from the web.
- On completing this unit, you will have learned how to use a programming language and will be able to implement supplied recipes and algorithms to analyze large data sets.
- On completing this unit you will be able to begin to address complex, real-world problems using computational thinking approaches and programming tools.
|Band||CSP Student Contribution||Full Fee Paying (domestic)||Field of Education|
|2||2020: $1,190.00||2020: $2,402.00||010199|
Fees for next year will be published before the end of the year. The fees above only apply for the year shown.
Please note: international students should refer to this page to get an indicative course cost.
On-line (equivalent of 60 min lecture weekly and 3-hr practical)
Assessment 1: Quizzes and online discussion – 20% - Students participate in online quizzes and discussion groups to solve coding challenges related to the basic principles covered in the lectures. Project 1 – 15% - Design and complete a project to demonstrate the skills learned. Components of the project should include accessing data from a data repository, some sort of the data analysis and a visualization of the data , Project 2 – 15% - Project 2 will focus on data visualization and analysis using the programming skills learned in the first seven weeks and the data accessed from Project 1, Final Project – 50% - Design and complete a project. Components of the project should include accessing data from a data repository, some sort of the data analysis and a visualization of the data. Students should submit a (~1000 word) description of their project, the code used to complete their projects, plus an audio-visual presentation.
|Timetable||View the lecture timetable | View the full unit timetable|
Computational Thinking for the Modern Problem Solver (Chapman & Hall/CRC Textbooks in Computing) 1st Edition
The Visual Display of Quantitative Information” by Edward R.Tufte
Storytelling With Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic
Booktopia textbook links
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.