Data Analysis

This subject has the following teaching availabilities in 2014:

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: http://services.unimelb.edu.au/disability

Contemporary business is awash in data. Modern businesses’ business processes and activities usually involve multiple streams of data from areas as diverse as marketing activities, operational processes and financial activities. Therefore, managers are frequently confronted with how to harness these to understand their business better, so that they can make more informed decisions. Data analysis is the process of converting such raw data into meaningful information to inform business decision-making.

This subject provides an introduction to the fundamental data analysis skills and techniques that are used in contemporary business and management. These skills are essential both for later MBA subjects, and also for solving managerial problems in your later career. The course is computer based and students are required to perform all manipulations and computations themselves.

On completion of this subject, students should be able to apply quantitative methods in management decision making processes. Specifically they should be able to:

• find probablities for normal distributions
• compute a confidence interval for a mean, proportion and the difference between two means
• conduct a hypothesis test for a mean or a proportion
• identify possible errors or biases in sampling
• compute the correlations between variables
• use Excel to run a regression analysis
• interpret the output of a regression analysis and evaluate the validity and usefulness of a regression model in the context of the business issues that are being analysed

• Individual assignment (10%)
  • 500 words
  • Week 3
• Quiz (20%)
  • 30 minutes
  • Week 4
• Syndicate project
  • Report (1500 words, week 7) (20%)
  • Presentation (15 minutes, week 8) (10%)
• Final Exam (40%)
  • Hurdle requirement
  • 90 minutes
  • End of subject

This subject is not available as a breadth subject.