Availabilities:

LocationDomesticInternational
Gold Coast
Melbourne
Online
Perth
Sydney

Unit description

Equips students with the tools to build contemporary Big Data processing and analysis systems. Students learn how to create and develop each task in the machine learning pipeline from acquiring and cleaning data to analysing and visualising insights obtained from datasets including natural language datasets.

Unit content

Topic 1: Introduction to Big Data

Topic 2: Big Data pre-processing

Topic 3: Big Data technologies: Hadoop, Scala and Spark

Topic 4: Using Spark through Python

Topic 5: Dimension reduction

Topic 6: Data visualisation

Topic 7: Managing time-series data

Topic 8: Advanced Tensor Flow, NumPy and Pandas functionalities

Topic 9: Introduction to natural language processing and text mining

Topic 10: Processing raw text

Topic 11: Classifying text

Topic 12: Recent advances in Big Data processing.

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

GA1: Intellectual rigour, GA2: Creativity, GA3: Ethical practice, GA4: Knowledge of a discipline, GA5: Lifelong learning, GA6: Communication and social skills, GA7: Cultural competence
On completion of this unit, students should be able to:GA1GA2GA3GA4GA5GA6GA7
1identify, manipulate and apply Big Data storage and processing technologies Intellectual rigourKnowledge of a discipline
2acquire and clean large data sets Knowledge of a discipline
3extract features and model large data sets Intellectual rigourKnowledge of a discipline
4design algorithms and architectures for processing large data sets to extract patterns and information Intellectual rigourCreativity
5develop natural language processing algorithms for text mining Intellectual rigourCreativityKnowledge of a discipline

On completion of this unit, students should be able to:

  1. identify, manipulate and apply Big Data storage and processing technologies
    • GA1: Intellectual rigour
    • GA4: Knowledge of a discipline
  2. acquire and clean large data sets
    • GA4: Knowledge of a discipline
  3. extract features and model large data sets
    • GA1: Intellectual rigour
    • GA4: Knowledge of a discipline
  4. design algorithms and architectures for processing large data sets to extract patterns and information
    • GA1: Intellectual rigour
    • GA2: Creativity
  5. develop natural language processing algorithms for text mining
    • GA1: Intellectual rigour
    • GA2: Creativity
    • GA4: Knowledge of a discipline

Prescribed texts

  • Prescribed text information is not currently available.

  • Prescribed text information is not currently available.
Prescribed texts may change in future study periods.

Teaching and assessment

Notice

Intensive offerings may or may not be scheduled in every session. Please refer to the timetable for further details.

Southern Cross University employs different teaching methods within units to provide students with the flexibility to choose the mode of learning that best suits them. SCU academics strive to use the latest approaches and, as a result, the learning modes and materials may change. The most current information regarding a unit will be provided to enrolled students at the beginning of the study session.

Fee information

Domestic

Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.

Fee paying courses
For postgraduate or undergraduate full fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees

International

Please check the international course and fee list to determine the relevant fees.

+