Handbook

MAST30027 Modern Applied Statistics

Credit Points: 12.5
Level: 3 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2017:

Semester 2, Parkville - Taught on campus.Show/hide details
Pre-teaching Period Start not applicable
Teaching Period 24-Jul-2017 to 22-Oct-2017
Assessment Period End 17-Nov-2017
Last date to Self-Enrol 04-Aug-2017
Census Date 31-Aug-2017
Last date to Withdraw without fail 22-Sep-2017


Timetable can be viewed here.
For information about these dates, click here.
Time Commitment: Contact Hours: 3 x one hour lectures per week, 1 x one hour computer laboratory class per week
Total Time Commitment:

Estimated total time commitment of 170 hours

Prerequisites:
Subject
Study Period Commencement:
Credit Points:
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects:

Students who gain credit for both ACTL30001 Actuarial Modelling 1 and ACTL30004 Actuarial Statistics cannot also gain credit for MAST30027 Modern Applied Statistics.

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

Coordinator

Dr Hee Jung Shim

Contact

Email: khoak@unimelb.edu.au

Subject Overview:

Modern applied statistics combines the power of modern computing and theoretical statistics. This subject considers the computational techniques required for the practical implementation of statistical theory, and includes Bayes and Monte-Carlo methods. The subject focuses on the application of these techniques to generalised linear models, which are commonly used in the analysis of categorical data.

Learning Outcomes:

At the completion of the subject, students should:

  • Understand the theory and applications of various mainstream applied statistical methods;
  • Be able to use appropriate statistical methods to develop effective models or inferential procedures and provide sound interpretations for real-world data analysis;
  • Be able to use a computer package to perform statistical computing and data analysis.
Assessment:

Six written assignments due at regular intervals during semester amounting to a total of up to 50 pages (30%), and a 3-hour written examination in the examination period (70%).

Prescribed Texts: None
Recommended Texts:
  • Faraway, Extending the Linear Model, R. Chapman & Hall, 2006.
  • McCullagh & Nelder, Generalised Linear Models, 2nd edition. Chapman & Hall, 1989.
  • Jones, Maillardet & Robinson, Introduction to Scientific Programming and Simulation Using R, 2nd Edition, Taylor and Francis, 2014.
  • Gelman, Carlin, Stern, Dunson, Vehtari & Rubin, Bayesian Data Analysis, 3rd Edition, CRC Press, 2014.
  • Lunn, Jackson, Best, Thomas & Spiegelhalter, The BUGS Book: A Practical Introduction to Bayesian Analysis, CRC Press, 2013.
Breadth Options:

This subject potentially can be taken as a breadth subject component for the following courses:

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct andexpress logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time management skills: the ability to meet regular deadlines while balancing competing commitments;
  • computer skills: the ability to use statistical computing packages.
Notes:

This subject is available for science credit to students enrolled in the BSc (both pre-2008 and new degrees), BASc or a combined BSc course.

Related Majors/Minors/Specialisations: Data Science
Science-credited subjects - new generation B-SCI and B-ENG.
Selective subjects for B-BMED
Statistics / Stochastic Processes
Statistics / Stochastic Processes
Statistics / Stochastic Processes
Statistics / Stochastic Processes
Statistics / Stochastic Processes (specialisation of Mathematics and Statistics major)

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