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Elements of Data Processing (COMP20008)

Undergraduate level 2Points: 12.5On Campus (Parkville)

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Overview

Year of offer2017
Subject levelUndergraduate Level 2
Subject codeCOMP20008
Campus
Parkville
Availability
Semester 1
FeesSubject EFTSL, Level, Discipline & Census Date

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 outcomes

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

Generic skills

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.

Eligibility and requirements

Prerequisites

One of the following subjects

Code Name Teaching period Credit Points
COMP10001 Foundations of Computing
Semester 1
Semester 2
12.5
COMP10003 Media Computation
Semester 1
12.5

And

Code Name Teaching period Credit Points
COMP10002 Foundations of Algorithms
Semester 1
Semester 2
12.5

Corequisites

None

Non-allowed subjects

INFO20002 Foundations of Informatics

Core participation requirements

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Student Equity and Disability Support website: http://www.services.unimelb.edu.au/disability

Assessment

Description

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

Dates & times

  • Semester 1
    Principal coordinatorJames Bailey
    Mode of deliveryOn Campus — Parkville
    Contact hours48 hours, comprising of two 1-hour lectures and one 2-hour workshop per week
    Total time commitment170 hours
    Teaching period27 February 2017 to 28 May 2017
    Last self-enrol date10 March 2017
    Census date31 March 2017
    Last date to withdraw without fail 5 May 2017
    Assessment period ends23 June 2017

    Semester 1 contact information

    Prof James Bailey

    email: baileyj@unimelb.edu.au

Time commitment details

170 hours

Further information

Prescribed texts

None

Recommended texts and other resources

None

Notes

EARNING AND TEACHING METHODS

INDICATIVE KEY LEARNING RESOURCES

CAREERS / INDUSTRY LINKS

Related majors/minors/specialisations

Last updated: 28 November 2017