BP245 - Bachelor of Science (Statistics)

Plan: BP245 - Bachelor of Science (Statistics)
Campus: City Campus

Program delivery and structure

Approach to learning and assessment
Work integrated learning
Program structure
Program transition plan

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Approach to learning and assessment

The Bachelor of Science (Statistics) program offers you rich, diverse, theoretical and applied learning experiences. You will be encouraged to actively participate in class and to engage with, and learn from, teaching staff, fellow students, tutors and industry professionals both in face-to-face and online settings. Learning activities will include practical exercises, case study analysis, oral presentations, technical and business reports, and individual and group project work.

The program has been designed to balance theory and practice. Lectures, from both academic staff and industry professionals, are combined with tutorial and computer lab sessions to help you build an understanding of mathematical and statistical theories and concepts as well as skills in the use of tools/packages (e.g. R and SAS) and the opportunity to use and apply these theories and tools to a range of practical problems and situations.

The assessment in the program will provide you with feedback on your performance and the degree to which you have achieved the learning outcomes and capabilities associated with each course. The type of assessment will vary and include examinations, tests, written assignments, oral presentations and laboratory practicals. In addition, assessment activities will be progressively scheduled throughout each course and include formative feedback that does not count toward your final grade (e.g. tutorial quiz), as well as summative assessment activities that will count towards your final grade.

Work integrated learning (WIL) forms a crucial part of the program. In addition to guest lecturers and site visits, industry representatives will also be involved in developing and evaluating projects. It is envisaged that industry placements will be available.

If you have a long-term medical condition, disability and/or other form of disadvantage, it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or the Equitable Learning Service team if you would like to find out more.

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 Recognition of Prior Learning (RPL) - refer to: www.rmit.edu.au/students/enrolment/credit/he

 

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Work integrated learning

RMIT University is committed to providing you with an education that strongly links formal learning with workplace experience. As a student enrolled in an RMIT program you will:

• undertake and be assessed on a structured activity that allows you to learn, apply and demonstrate your professional or vocational practice
• interact with industry and community when undertaking this activity
• complete an activity in a work context or situation that may include teamwork with other students from different disciplines
• underpin your learning with feedback from interactions and contexts distinctive to workplace experiences

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

In this program, you will be doing specific course(s) that focus on work integrated learning (WIL). You will be assessed on professional or vocational work in a workplace setting (real or simulated) and receive feedback from those involved in your industry.

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.

Completion of WIL courses within this program will involve liaising with industry to define/create the problem; analysing and creating a report; and presenting and receiving feedback from industry partners. Each student can expect interaction with and feedback from industry. 

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Program Structure

To graduate you must complete the following: All courses listed may not be available each semester.
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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
AND
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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
AND
Select and Complete Three (3) Courses from the following list:
Option List 1
AND
Select and Complete One (1) Course from the following list:
Option List 2
AND
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Year 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
AND
Select and Complete Two (2) Courses from the following list:
Option List 1
AND
Select and Complete Two (2) Courses from the following list:
Option List 2
AND
Select and Complete Two (2) Courses from:
University Elective
AND
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List 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
AND
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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
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Program transition plan

Very Important: This program is being phased out.

This program will only be available until Sem 2 2027. If you believe you will not complete the program within this time or if you have any queries or concerns regarding the changes and how you may be impacted, academic advice from the College of Science, Engineering and Health Academic Services team or from your Program Manager will be available to you. No new students will be admitted to this program after 2019.

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