Statistics is concerned with the process of planning how to collect data, collection of the data and extracting information from data in the presence of uncertainty and variation. The process is guided by the underlying purpose of the investigation and the formulation of hypotheses and models. The major covers the core components of statistical theory and a range of key applications, such as survival analysis and graphical statistical modelling, and provides a solid foundation for a career as statistician.
Employment prospects for statisticians are very bright and varied.
Learning Outcomes
- Recognise the importance of proper collection and management of quantitative information to the scientific process, including appreciation of the importance of data accuracy, verifiability and context.
- Apply data analytic techniques that are appropriate to inquiry context, including demonstrating appreciation for the underlying assumptions and data requirements for these techniques to be valid.
- Communicate the principles and results of data analyses using the language and conventions of the discipline.
- Apply a wide range of statistical testing and estimation techniques, including modern and computer based modelling, in appropriate contexts.
- Develop the skills necessary to critically engage with and evaluate literature on data analysis.
- Explain and convey findings and information from data analysis.
- Develop the skills necessary to work independently and collaboratively to collect, process, interpret and represent data and inferential outcomes.
Other Information
Which courses should you take in first year?
This major will require you to complete the following 1000-level courses:
- MATH1013, MATH1115 or MATH1113. If MATH1013 is chosen, MATH1014 must also be taken.
- STAT1003 or STAT1008
Additional advice:
- Students undertaking this major are strongly encouraged to seek advice regarding the first year Math requirements. Students wishing to complete only a single MATH course must choose MATH1113 to complete this major.
- Students who select MATH1013 will need to choose a second first year MATH course: MATH1014 or MATH1116.
- Students may consider various themes through the courses. A few examples are:
- Probability Theory and Stochastic Processes: STAT2005, STAT3004, and STAT3006.
- Statistical Theory: STAT3012, STAT3013, and STAT3056.
- Statistical Data Science: STAT3011, STAT3015, STAT3016, STAT3017, STAT3040, and STAT3050.
Academic or enrolment advice
Students can seek further advice from the academic contact for this major (details above), or the College of Science Student Services Team (students.cos@anu.edu.au).
Back to the topRequirements
Courses marked with an asterisk (*) have 1000-level prerequisites which must be selected in the first year of study and will contribute towards satisfying the 1000-level course requirements of the Bachelor of Science or Bachelor of Science (Advanced) (Honours). Please check individual courses for details however the courses listed below will cover most 1000-level requirements for 2000-3000- level courses listed in this major.
- MATH1013, MATH1115 or MATH1113 (required for most 2000-level STAT). If MATH1013 is chosen, MATH1014 must also be taken.
- STAT1003 or STAT1008 (required for all 2000-level STAT)
This major requires the completion of 48 units, which must include:
6 units from completion of an Introductory Mathematical Statistics course from the following list:
*STAT2001 Introductory Mathematical Statistics (6 units)
*STAT2013 Introductory Mathematical Statistics for Actuarial Studies (6 units)
6 units from completion of a Regression Modelling course from the following list:
*STAT2008 Regression Modelling (6 units)
*STAT2014 Regression Modelling for Actuarial Studies
36 units from completion of courses from the following list:
*STAT2005 Introduction to Stochastic Processes (6 units)
STAT3004 Stochastic Processes (6 units)
STAT3006 Advanced Stochastic Processes (6 units)
STAT3011 Graphical Data Analysis (6 units)
STAT3012 Design of Experiments and Surveys (6 units)
STAT3013 Statistical Inference (6 units)
STAT3015 Generalised Linear Modelling (6 units)
STAT3016 Introduction to Bayesian Data Analysis (6 units)
STAT3017 Big Data Statistics (6 units)
STAT3040 Statistical Learning (6 units)
STAT3050 Advanced Statistical Learning (6 units)
STAT3056 Advanced Mathematical Statistics (6 units)
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