Is it possible to anticipate the consumer reaction to new modes of delivery of health services? Can forecasting of emergency events be made more reliably? How much use will be made of new public infrastructure? Can predictive models be relied upon for early intervention in family services?

Welcome to the future of public policy practice, a future where long-standing techniques of policy analysis meet the power of big data and new capabilities in policy analytics.

Carnegie Mellon University in Australia has designed a new program that combines its rigorous public policy syllabus with its leading-edge courses in information technology and management.

The availability of massive data sets and of new capabilities for analysing that data will change the face of public policy formulation and analysis. The winners will be the professionals capable of understanding the generation of big data, its potential and limitations and how to utilise it. These professionals will be capable of using this data to generate evidence-based and predictive policy options; know how to communicate those options; and be able to implement and evaluate them.

Internship: 10 – 12 weeks

• All applicants must provide documentation of their completed undergraduate degree (bachelor's degree or equivalent) from a regionally accredited institution. 
• Students must achieve an average minimum score of GRE 300 or GMAT 600.
• Applicants are required to submit a resume. Please submit a one or two page summary of your education skills, accomplishments, employment experience and volunteer experience. The resume must be current and include all part-time and full-time work experience, the dates of when the employment began and ended and a brief description of your responsibilities under the title of the position.
• You are required to submit an essay (approximately 1,000 words) describing your academic and professional background, your career goals, and how the program will help you achieve your goals.
• All applicants are required to have two (2) recommendation forms submitted on their behalf. Recommendations should be provided from employers, supervisors, professors, or others who are able to comment on your professional and educational goals and potential. At least one (1) of your recommenders should know you in a professional setting (full-time employment, internship, part-time employment, research, non-academic extracurricular activity, etc.) You may opt to have up to three (3) recommendations submitted in support of your application.