Elements of Data Processing

This subject has the following teaching availabilities in 2016:

170 hours

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For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability

AIMS

Data processing is fundamental to computing and data science. This subject gives an introduction to various aspects of data processing including database management, representation and analysis of data, information retrieval, visualisation and reporting, and cloud computing. This subject introduces students to the area, with an emphasis on both tools and underlying foundations.

INDICATIVE CONTENT

The subject's focus is on the data pipeline, and activities known colloquially as 'data wrangling'. Indicative topics covered include:

• Capturing data (data ingress)
• Data representation and storage
• Cleaning, normalization and filling in missing data (imputation)
• Combing multiple sources of data (data integration)
• Query languages and processing
• Scripting to support the data pipeline
• Distributing a database over multiple nodes (sharding), cloud computing file systems

Visualisation and presentation

INTENDED LEARNING OUTCOME (ILO)

Having completed this subject the student is expected to:

1. Be familiar with the relationship of the data pipeline to data science
2. Be able to develop and critically evaluate alternative approaches to components of typical data pipelines
3. Apply data processing methodologies to preparing data while managing data quality, system scalability, and usability for decision making

Project work during semester, applying data processing to datasets, requiring approximately 45-50 hours of work in total, due in approximately week 6 and week 11, (40%). Addresses Intended Learning Outcomes, (ILO) 1, 2 and 3.

One 5-minute workshop presentation, requiring approximately 10-12 hours of work in total, presented during semester, (10%). Addresses ILO 3.

One 2-hour end-of-semester examination,(50%). Addresses ILO 1 and 2.

Hurdle requirement. To pass the subject, students must obtain at least:

• 20 / 50 in the continuous assessment
• 20 / 50 in the end-of-semester written examination

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This subject is not available as a breadth subject.

On completion of this subject, students should have developed the following generic skills:

• An ability to apply knowledge of basic science and engineering fundamentals
• An ability to undertake problem identification, formulation and solution
• The capacity to solve problems, including the collection and evaluation of information
• The capacity for critical and independent thought and reflection
• Profound respect for truth and intellectual integrity, and for the ethics of scholarship

An expectation of the need to undertake lifelong learning, and the capacity to do so.

EARNING AND TEACHING METHODS

INDICATIVE KEY LEARNING RESOURCES

CAREERS / INDUSTRY LINKS