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LocationDomesticInternational
MelbourneN/A
Session2
Online
Session2
Session2
PerthN/A
Session2
SydneyN/A
Session2

Unit description

Provides students with the necessary knowledge and skills to effectively use business analytics to support decision making in the context of ‘big data’ as a strategic resource. As a ‘data-driven’ unit, students will be exposed to a variety of analytical techniques using software applications.

Unit content

Topic 1: Introduction to business analytics

Topic 2: Data visualisation

Topic 3: Descriptive numerical measures

Topic 4: Simple linear regression

Topic 5: Multiple linear regression - Part I

Topic 6: Multiple linear regression - Part II

Topic 7: Business Forecasting - Part I

Topic 8: Business Forecasting - Part II

Topic 9: Model Building - Part I

Topic 10: Model Building - Part II 

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:
1Appraise the role of data analytics and implications of big data in helping organisations identify new opportunities and turn big data into a strategic resource.
2Identify patterns and trends for transformation of big data into meaningful information for use to gain competitive advantage.
3Apply statistical techniques using industry software to analyse relationships in the data for forecasting and evaluation purposes.
4Evaluate and communicate results of analysis in a framework for translating data analysis into decision-making outcomes in a variety of business settings.

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

  1. Appraise the role of data analytics and implications of big data in helping organisations identify new opportunities and turn big data into a strategic resource.
  2. Identify patterns and trends for transformation of big data into meaningful information for use to gain competitive advantage.
  3. Apply statistical techniques using industry software to analyse relationships in the data for forecasting and evaluation purposes.
  4. Evaluate and communicate results of analysis in a framework for translating data analysis into decision-making outcomes in a variety of business settings.

Prescribed texts

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

Teaching and assessment

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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