Introduction to Data Analytics

Subject MGMT90215 (2016)

Note: This is an archived Handbook entry from 2016.

Credit Points: 6.25
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2016:

August, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period 04-Aug-2016 to 05-Aug-2016
Assessment Period End 06-Sep-2016
Last date to Self-Enrol 04-Aug-2016
Census Date 12-Aug-2016
Last date to Withdraw without fail 26-Aug-2016

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 16 hours
Total Time Commitment: Not available
Prerequisites: None
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

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:


Subject Overview:

Data analytics is becoming an increasingly important part of contemporary decision-making and strategic analysis. This is equally true in both public and private sector organizations. Sound knowledge of the basic statistical approaches to summarize and analyse data are becoming an important set of managerial skills.

This course is an introductory course on the basics of data analysis and analytics. These include basic data management, methods to summarize data effectively, and regression modelling and analysis. An emphasis will be placed on how such analyses can help guide effective decision-making. The course will also involve a significant amount of hands-on data analysis using the public domain software “R”, and one of the aims of the course is introduce participants to this leading software environment. Participants will have to employ the methods and models covered in the course to solve problems in cases drawn from the contemporary business and public policy environment, and recommend appropriate decisions.

The course also serves as a preparatory course for the further study of more advanced models and methods for data analysis and analytical decision-making.

Learning Outcomes:

On completion of this subject, students should be able to demonstrate;

  • An understanding of different types of quantitative data
  • An understanding of how to summarise the empirical distribution of data, and create simple visualisations.
  • A regression analysis, and draw meaningful inference from the results
  • A basic statistical analysis to solve decisions in complex strategic scenarios
  • The use the software platform R to undertake basic statistical analysis.
  • Multiple-choice on the advantages and uses of data analysis and analytical methods in contemporary organizations (30 mins) to be completed at the end of the second day (20%).
  • Essay – employ a regression analysis to recommend an evidence-based decision in the workplace (2000 words) due four weeks after the class (80%).
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Related Course(s): Specialist Certificate in Strategic Marketing

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