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Data Capture and Preparations G (11520.1)

Level: Graduate Level
Credit Points: 3
HECS Bands: 2
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


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For the remainder of 2020, resulting from Australian Government's directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, any exams that are required for assessment in a unit will be online exams. Online exams may also use online proctoring to help assure the academic integrity of those exams. Please contact your unit convener with any questions.

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Unit Outlines

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  • Semester 1, 2020, ON-CAMPUS, BRUCE (192330) - View

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A score skill of a data scientist is to capture, extract and clean data. Real world data often come from various data sources, in various formats and are unorganized. This unit introduces students to the concepts and techniques a data scientist employs in the early stages of data analysis process. This unit will provide hands-on experience in capturing data from sensors, collecting data from public information as well as working with existing data sets using real-world examples. Such data may be temporal or spatial, ordinal or categorical, embedded in documents or files. Students will learn how to import and clean the data, which usually involves multiple, often complicated, steps to convert data from its raw format to a clean format that greatly facilitates the later stages of the data analysis. This is known as data wrangling.

Learning Outcomes

After successful completion of this unit, students will be able to:

1. Work with sensors for capturing data;

2. Choose and apply appropriate techniques for capturing data from existing sources;

3. Import data into R;

4. Convert data from one format to another one in R;

5. Employ suitable techniques for tidying data; and

6. Develop a sound understanding of text mining methods in R.

Assessment Items

Contact Hours

Four hours of problem-based learning activities, interactive workshops & practical work in laboratory classes on campus per week.





Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

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