Introduction to Data Analytics

This subject has the following teaching availabilities in 2016:

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

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.

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%).

This subject is not available as a breadth subject.