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LocationDomesticInternational
Online

Unit description

Introduces students to computing for data analytics and the role it plays in problem-solving. Students will learn to break down problems into steps that can be performed by machines to solve problems and accomplish goals.    

Unit content

Topic 1: Introduction to data analytics

Topic 2: Four types of data analytics (Descriptive, Diagnostic, Predictive, Prescriptive)

Topic 3: Understanding business data

Topic 4: Data preparation: value generation from raw data

Topic 5: Explorative analysis for model planning

Topic 6: Model building and communication in data analytics projects

 

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.

On completion of this unit, students should be able to:
1articulate and apply knowledge of frameworks, processes, and techniques of data analytics and incorporate with the principles and strategies of the business environment
2apply analytical thinking on business needs assessment and analytical problem framing
3apply practical knowledge and skills of Python programming to perform data preparation, exploration, and modelling tasks
4effectively interpret and communicate insights from data analytics projects using appropriate data analytics techniques.

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

  1. articulate and apply knowledge of frameworks, processes, and techniques of data analytics and incorporate with the principles and strategies of the business environment
  2. apply analytical thinking on business needs assessment and analytical problem framing
  3. apply practical knowledge and skills of Python programming to perform data preparation, exploration, and modelling tasks
  4. effectively interpret and communicate insights from data analytics projects using appropriate data analytics techniques.

Prescribed texts

  • No prescribed texts.
Prescribed texts may change in future teaching periods.

Teaching and assessment

Teaching method
Workshop 1 hour (Weekly)
Tutorial 2 hours (Weekly)
Assessment
Quiz20%
Report30%
Report and Recorded Presentation50%
Notice

Intensive offerings may or may not be scheduled in every teaching period. 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 teaching period.

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.

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