BP083 - Bachelor of Applied Mathematics and Statistics

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Plan: BP083P23 - Bachelor of Applied Mathematics and Statistics
Campus: City Campus

Program delivery and structure

Approach to learning and assessment
Work integrated learning
Program structure

Approach to learning and assessment

The teaching approach in this program is designed to foster your development as an independent learner so you will be able to extend your capabilities once you graduate. The teaching method includes lectorial, seminar, tutorial, workshop, and provision of online materials. You will be expected to complete all prescribed out-of-class learning activities in preparation of scheduled face-to-face and online classes and encouraged to extend your learning through additional recommended readings and online activities. Of particular importance is the time spent in practice, laboratory based and work integrated learning activities that will develop your employability skills and capabilities. 

Several courses in the program are delivered online, rather than on-campus, while other courses are delivered via a combination of predominantly online activities supported by some campus-based activities. All courses use Canvas for electronic provision of course material, tutorial problems and/or other relevant documents.

Assessment is designed to give you the opportunity to demonstrate your capabilities. Various forms of assessment will be used throughout the program since the assessment you undertake will be appropriate to the objectives and student learning outcomes for each course. Assessment may include class tests, quizzes, essays/reports, oral class presentations, group projects, research projects, laboratory projects, practical assignments and timed assessments.

Inherent requirements   

The following information on inherent requirements outlines the tasks you will be required to undertake during professional placement and on-campus learning activities. The non-academic abilities listed are provided for information only and are not entry requirements. 

If there are any activities outlined which may be difficult for you to undertake, there are a range of adjustments to your study conditions available to enable and support you to demonstrate these abilities. Please contact the Equitable Learning Service to discuss any adjustments you may require. 

BP083 Bachelor of Applied Mathematics and Statistics share the same inherent requirements as BP350 Bachelor of Science. Please read the full list of the BP350 Bachelor of Science inherent requirements (https://www.rmit.edu.au/study-with-us/applying-to-rmit/local-student-applications/entry-requirements/inherent-requirements/bachelor-of-science)

By understanding the types of activities you’ll participate in, you can:

  • understand more about the program
  • determine if you may need support during your studies
  • make an informed decision about whether the program is suitable for you

If you are living with disability, long-term illness and/or a mental health condition, we can support you by making adjustments to activities in your program so that you can participate fully in your studies. To receive learning adjustments, you need to register with Equitable Learning Service

The University considers the wellbeing and safety of all students, staff and the community to be a priority in on-campus learning and professional experience settings. 

Credit Transfer and Recognition of Prior Learning 

If you have already developed areas of skill and knowledge included in this program (for example, through prior studies or work experience), you can apply for credit once you have enrolled in this program. There is information on the RMIT University website about how to apply for Credit: https://www.rmit.edu.au/students/student-essentials/enrolment/apply-for-credit

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Work integrated learning

RMIT University is committed to providing you with an education that strongly links formal learning with professional or vocational practice.

As a student enrolled in this program you will:  

  • Undertake and be assessed on structured activities that allow you to learn, apply and demonstrate your professional or vocational practice  

  • Interact with industry and community when undertaking these activities  

  • Complete these activities in real work contexts or situations.  

Any or all of these aspects of a WIL experience may be in a simulated workplace environment.   

The Bachelor of Applied Mathematics and Statistics includes several Work Integrated Learning (WIL) courses. In these WIL courses, you will engage in activities that integrate theoretical learning with practical application in professional contexts and you will engage in meaningful and consequential learning activities. You will interact with organisations (industry, government and community) through discipline relevant projects and work placements. These interactions and the work context provide a distinctive source of feedback to you to assist your learning.  

Work integrated learning (WIL) designated courses are: 

The designated WIL courses for this program are: 

  • MATH2196 Industrial Applications of Mathematics and Statistics 1, and 

  • MATH2197 Industrial Applications of Mathematics and Statistics 2 

In these WIL courses, you will interact with organisations (industry, government and community) through discipline relevant projects and activities. These interactions and the work context provide a distinctive source of feedback to you to assist your learning.  

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Program Structure

To graduate you must complete the following: All courses listed may not be available each semester
 

You must complete a total of 288 credit points (e.g. Twenty-Four 12 credit point courses) as follows:

Year 1: Five (5) Core courses (60 credit points); and
Year 2: Two (2) Core courses (24 credit points); and
Year 3: One (1) Core course (12 credit points); and
Years 1 to 3: Total of 192 credit points from one of the following combinations:

List of Combinations

Combination 1: Complete Mathematics Major (96 credit points), Statistics Minor (48 credit points), Two (2) Mathematics Advanced Courses (24 credit Points), and Two University Electives (24 credit points); or

Combination 2: Complete Statistics Major (96 credit points), Mathematics Minor (48 credit points), Two (2) Statistics Advanced Courses (24 credit Points), and Two University Electives (24 credit points)

 

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Year One of Program

Complete the following Five (5) Courses:

Course Title

Credit Points

Course Code

Campus

Data for a Scientific World 12 ONPS2700 City Campus
Physical Sciences in Action 12 ONPS2701 City Campus
The World of Life Sciences 12 ONPS2699 City Campus
A Mathematical Toolbox for Scientists 12 MATH2443 City Campus
STEM for Sustainable Development 12 ONPS2702 City Campus
AND
Complete Thirty-six (36) credit points from your selected combination.
 
AND

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Year Two of Program

Complete the following Two (2) Courses:

Course Title

Credit Points

Course Code

Campus

Foundations of Artificial Intelligence for STEM 12 COSC2960 City Campus
Cyber-Physical-Biological Systems: Technology for a Digital World 12 BIOL2525 City Campus
AND
Complete Seventy-two (72) credit points from your selected combination.
 
AND

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Year Three of Program

Complete the following One (1) Course:

Course Title

Credit Points

Course Code

Campus

Industrial Applications of Mathematics and Statistics 2 12 MATH2197 City Campus
AND
Complete Eighty-four (84) credit points from your selected combination.
 
AND

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Major List

List of Major(s):
 
AND
(

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Major: Mathematics

Complete the following Seven (7) Courses:

Course Title

Credit Points

Course Code

Campus

Vectors and Calculus 12 MATH1144 City Campus
Advanced Linear Algebra with Vector Calculus 12 MATH2311 City Campus
Discrete Mathematics 12 MATH1150 City Campus
Modelling with Differential Equations 12 MATH2138 City Campus
Real and Complex Analysis 12 MATH2150 City Campus
Optimisation 12 MATH2390 City Campus
Mathematical Computing and Algorithms 12 MATH2109 City Campus
AND
Select and Complete One (1) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Complex Networks 12 MATH2312 City Campus
Graph Algorithms and Applications 12 MATH2308 City Campus
 
OR

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Major: Statistics

Complete the following Seven (7) Courses:

Course Title

Credit Points

Course Code

Campus

Data Visualisation with R 12 MATH2237 City Campus
Statistical Methodologies 12 MATH2201 City Campus
Industrial Applications of Mathematics and Statistics 1 12 MATH2196 City Campus
Statistical Inference 12 MATH2155 City Campus
Linear Models and Experimental Design 12 MATH2203 City Campus
Time Series and Forecasting 12 MATH2204 City Campus
Machine Learning 12 MATH2387 City Campus
AND
Select and complete One (1) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Analysis of Categorical Data 12 MATH2300 City Campus
Applied Bayesian Statistics 12 MATH2305 City Campus
)
AND

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Minor List

List of Minor(s):
 
AND
(

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Minor: Mathematics

Complete the following Four (4) Courses:

Course Title

Credit Points

Course Code

Campus

Vectors and Calculus 12 MATH1144 City Campus
Advanced Linear Algebra with Vector Calculus 12 MATH2311 City Campus
Discrete Mathematics 12 MATH1150 City Campus
Modelling with Differential Equations 12 MATH2138 City Campus
 
OR

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Minor: Statistics

Complete the following Three (3) Courses:

Course Title

Credit Points

Course Code

Campus

Data Visualisation with R 12 MATH2237 City Campus
Statistical Methodologies 12 MATH2201 City Campus
Industrial Applications of Mathematics and Statistics 1 12 MATH2196 City Campus
AND
Select and complete One (1) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Time Series and Forecasting 12 MATH2204 City Campus
Machine Learning 12 MATH2387 City Campus
)
AND

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Advanced Course List:

Advanced Mathematics Courses:

Course Title

Credit Points

Course Code

Campus

Linear Programming and Modelling 12 MATH1288 City Campus
Algebra for Information Security 12 MATH2148 City Campus
AND
Advanced Statistics Courses:

Course Title

Credit Points

Course Code

Campus

Mathematical Computing and Algorithms 12 MATH2109 City Campus
Graph Algorithms and Applications 12 MATH2308 City Campus
 

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