Bachelor of Science (Statistics)
Royal Melbourne Institute of Technology
About
The program BP245 Bachelor of Science (Statistics) will:enable you to become highly employable in the field of statistics provide you with a sound knowledge of basic (and some advanced) statistical theory expose you to a wide range of statistical models, approaches and software enable you to select and apply the appropriate statistical theories, techniques and software to solve a wide range of problems expose you to related fields of study which require statistical expertise, such as finance, marketing or environmental modelling develop your knowledge of the types of industry which employ statisticians and the variety of tasks they undertake develop your range of generic skills and abilities to operate effectively in professional settings that involve mathematical and statistical expertise.
These include good communication skills, technology literacy and the ability to work in a team and interact with others identify the need for an ethical approach to your work.Year 1 is designed to build a common platform of mathematical and statistical knowledge, conceptual and analytical skills that are essential for Years 2 & 3.
It also includes an overview of the different components of applied mathematics which can be explored in depth in subsequent years.
In Years 2 and 3 of BP245 Bachelor of Science (Statistics), there is a large number of option courses which allow you to tailor your studies according to your interests in areas like finance, marketing, biology and ecology modelling and analytics.
You are also able to select two University-wide electives.
In Semester 1 of Year 3 you have the possibility of undertaking a student mobility program overseas, either in industry or in a partner university.MATH2197 Industrial Applications of Mathematics and Statistics 2 serves as a capstone experience - a culmination and application of knowledge and skills from your prior studies.
You will participate in a group project or industry placement under the supervision of the teaching team and eventually under the supervision of a representative from a partner institution.
Regular interactions with your supervisor(s) will allow you to obtain technical advice, to construct a milestone(s) plan and get feedback on your progress.
You will respond to real world problems using the knowledge and competencies acquired during your program to propose solutions.MATH2197 Industrial Applications of Mathematics and Statistics 2 and its pre-cursor, MATH2196 Industrial Applications of Mathematics and Statistics 1 are the designated WIL courses for the BP245 program.This program requires on-campus attendance.
Courses include on-line components.
Structure
Year One of Program
Complete the following Eight (8) Courses:
Course Title | Credit Points | Course Code | Campus |
---|---|---|---|
Calculus and Analysis 1 | 12 | MATH1142 | City Campus |
Introduction to Probability and Statistics | 12 | MATH2200 | City Campus |
Mathematical Computing and Algorithms | 12 | MATH2109 | City Campus |
Discrete Mathematics | 12 | MATH1150 | City Campus |
Problem Solving and Algorithms | 12 | MATH2313 | City Campus |
Calculus and Analysis 2 | 12 | MATH1144 | City Campus |
Basic Statistical Methodologies | 12 | MATH2201 | City Campus |
Data Preparation for Analytics | 12 | MATH2202 | City Campus |
Year Two of Program
Complete the following Four (4) Courses:
Course Title | Credit Points | Course Code | Campus |
---|---|---|---|
Linear Algebra and Vector Calculus | 12 | MATH2140 | City Campus |
Industrial Applications of Mathematics and Statistics 1 | 12 | MATH2196 | City Campus |
Linear Models and Experimental Design | 12 | MATH2203 | City Campus |
Statistical Inference | 12 | MATH2155 | City Campus |
Select and Complete Three (3) Courses from the following list:
Select and Complete One (1) Course from the following list:
ANDYear Three of Program
Complete the following Two (2) Courses:
Course Title | Credit Points | Course Code | Campus |
---|---|---|---|
Multivariate Analysis | 12 | MATH2142 | City Campus |
Industrial Applications of Mathematics and Statistics 2 | 12 | MATH2197 | City Campus |
Select and Complete Two (2) Courses from the following list:
Select and Complete Two (2) Courses from the following list:
Select and Complete Two (2) Courses from:
ANDList of Option Courses:
Option List 1 (Year Two and Year Three)
Course Title | Credit Points | Course Code | Campus |
---|---|---|---|
Data Visualisation | 12 | MATH2237 | City Campus |
Analysis of Categorical Data | 12 | MATH2300 | City Campus |
Sampling and Quality Control | 12 | MATH2205 | City Campus |
Time Series and Forecasting | 12 | MATH2204 | City Campus |
Predictive Modelling | 12 | MATH2301 | City Campus |
Applied Bayesian Statistics | 12 | MATH2305 | City Campus |
Sports Statistics | 12 | MATH2206 | City Campus |
Systems Simulation | 12 | MATH2309 | City Campus |
Scientific Computing | 12 | MATH1155 | City Campus |
Complex Networks | 12 | MATH2312 | City Campus |
Modelling with Differential Equations | 12 | MATH2138 | City Campus |
Graph Algorithms and Applications | 12 | MATH2308 | City Campus |
Applied Linear Algebra | 12 | MATH2311 | City Campus |
Linear Programming and Modelling | 12 | MATH1288 | City Campus |
System Dynamic Modelling | 12 | MATH2127 | City Campus |
Advanced Mathematical Modelling | 12 | MATH2139 | City Campus |
Nonlinear Optimisation | 12 | MATH2143 | City Campus |
Numerical Solutions of DEs | 12 | MATH2144 | City Campus |
Algebra for Information Security | 12 | MATH2148 | City Campus |
Mathematical Modelling | 12 | MATH2194 | City Campus |
List of Option Courses:
Option List 2 (Year Two and Year Three)
Course Title | Credit Points | Course Code | Campus |
---|---|---|---|
Business Finance | 12 | BAFI1008 | City Campus |
Financial Markets | 12 | BAFI1002 | City Campus |
Prices and Markets | 12 | ECON1020 | City Campus |
Data Visualisation | 12 | MATH2237 | City Campus |
Analysis of Categorical Data | 12 | MATH2300 | City Campus |
Sampling and Quality Control | 12 | MATH2205 | City Campus |
Time Series and Forecasting | 12 | MATH2204 | City Campus |
Predictive Modelling | 12 | MATH2301 | City Campus |
Applied Bayesian Statistics | 12 | MATH2305 | City Campus |
Sports Statistics | 12 | MATH2206 | City Campus |
Systems Simulation | 12 | MATH2309 | City Campus |
Scientific Computing | 12 | MATH1155 | City Campus |
Complex Networks | 12 | MATH2312 | City Campus |
Modelling with Differential Equations | 12 | MATH2138 | City Campus |
Graph Algorithms and Applications | 12 | MATH2308 | City Campus |
Applied Linear Algebra | 12 | MATH2311 | City Campus |
Linear Programming and Modelling | 12 | MATH1288 | City Campus |
System Dynamic Modelling | 12 | MATH2127 | City Campus |
Advanced Mathematical Modelling | 12 | MATH2139 | City Campus |
Nonlinear Optimisation | 12 | MATH2143 | City Campus |
Numerical Solutions of DEs | 12 | MATH2144 | City Campus |
Algebra for Information Security | 12 | MATH2148 | City Campus |
Mathematical Modelling | 12 | MATH2194 | City Campus |
Marketing Principles | 12 | MKTG1025 | City Campus |
Marketing Communication | 12 | MKTG1041 | City Campus |
Market Research | 12 | MKTG1045 | City Campus |
Global Mobility Elective | 12 | EXTL1195 | City Campus |
Entry requirements
Program entry requirements
Successful completion of an Australian Year 12 senior secondary certificate of education or equivalent.
For information on international qualifications and corresponding entry requirements that are equivalent to Australian academic entry requirements, see the Country equivalents web page.
Prerequisites
Victorian Certificate of Education (VCE) prerequisite Units 3 and 4 — a study score of at least 20 in one of Mathematical Methods (CAS) or Specialist Mathematics; and a study score of at least 30 in English (EAL) or at least 25 in any other English.
English language requirements
A minimum IELTS (Academic module) overall score of 6.5, with no band below 6.0; or equivalent.
For equivalents to English entry requirements, see the English equivalents web page.
Learning outcomes
On completion of BP245 Bachelor of Science (Statistics) you will be able to:
PLO 1 Personal and professional awareness
- contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
- reflect on experience and improve your own future practice
- apply the principles of lifelong learning to any new challenge.
PLO 2 Knowledge and technical competence
- use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
PLO 3 Problem-solving
- synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
- balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.
PLO 4 Teamwork and project management
- contribute to professional work settings through effective participation in teams and organisation of project tasks
- constructively engage with other team members and resolve conflict.
PLO 5 Communication
- communicate effectively both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.
PLO 6 Information literacy
- locate and use data and information and evaluate its quality with respect to its authority and relevance.
PLO 7 Ethics
- discuss the ethical considerations that inform judgments and decisions in academic and professional settings.
Institution
