BH119 - Bachelor of Analytics (Honours)

Plan: BH119 - Bachelor of Analytics (Honours)
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 Analytics (Hons) program is offered through a flexible combination of lectures, tutorials/practice classes and computer laboratory classes. In addition, there will be opportunities for you to participate in teamwork on projects (including delivery of written reports, posters and oral presentations) and be engaged in consulting activities. This program is designed to offer you rich and diverse learning experiences with a careful balance between theory and practice. Your studies in this program will be underpinned by the use of industry standard software tools/packages such as Excel, MatLab, R and SAS, and the opportunity to use and apply these 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 feedback that does not count toward your final grade (e.g. tutorial quiz), as well as assessment activities that will count toward your final grade.

The following are details of learning activities contained in the program:

• Primarily, you will be learning face-to-face with the lecturers delivering course and other relevant materials. Online materials can be accessed through an online system called myRMIT. The lecturers will elucidate course materials through explanation of key concepts. This will be further illustrated with demonstrations and examples.
• Assessment will test your understanding of course materials. Provision for this will include written assignments and/or project works and written examinations.
• Tutorials and practice classes will provide you with extra assistance if you encounter difficulties. Content of the tutorials/practice classes will also enhance problem-solving skills.
• Group participation through discussions and seminar presentations will encourage teamwork.
• Consulting project works will provide practice in the application of theory, through analysis of real data.
• You are encouraged to seek learning materials from other sources such as libraries and the internet.
• State-of-the-art statistical and operations research software used in the program will provide you with hands-on-experience required for a statistical analysis of data.

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 Services 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.

Work Integrated Learning (WIL) courses include:

MATH2302 Analytics in Industry 1, and MATH2303 Analytics in Industry 2 are designated core WIL courses. 

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.

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Program Structure

For students who commence their study in this program from 1 January 2016 onwards, please note that some courses listed in this structure will have their course marks count toward your program's weighted average mark. Your weighted average mark will determine the honours level of your award once you have completed the program. If a course counts toward your weighted average mark, that fact will be stated in its course guide. In Enrolment Online, after you completed your course enrolment, you will be notified which of the enrolled courses will count toward the weighted average mark.
For more information about the weighted average mark, please click here 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 Practice of Analytics 12 MATH2392 City Campus Calculus and Analysis 1 12 MATH1142 City Campus Introduction to Programming 12 COSC1519 City Campus Introduction to Probability and Statistics 12 MATH2200 City Campus Discrete Mathematics 12 MATH1150 City Campus Calculus and Analysis 2 12 MATH1144 City Campus Programming 1 12 COSC1073 City Campus Basic Statistical Methodologies 12 MATH2201 City Campus
AND
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Year Two of Program
Complete the following Six (6) Courses:
Course Title Credit Points Course Code Campus Applied Linear Algebra 12 MATH2311 City Campus Linear Models and Experimental Design 12 MATH2203 City Campus Practical Data Science 12 COSC2738 City Campus Spatial Information Science Fundamentals 12 GEOM1033 City Campus Database Concepts 12 ISYS1057 City Campus Advanced Programming Techniques 12 COSC1076 City Campus
AND
Select and Complete One (1) Course from the Analytics Options list. (Please see the end of the program structure for all option course lists)
AND
Select and Complete One (1) Course from the Business Options list or Data Science Options list. (Please see the end of the program structure for all option course lists)
AND
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Year Three of Program
Complete the following Three (3) Courses:
Course Title Credit Points Course Code Campus Multivariate Analysis 12 MATH2142 City Campus Algorithms and Analysis 12 COSC2123 City Campus Machine Learning 12 COSC2673 City Campus
AND
Select and Complete Three (3) Courses from the Analytics Options list. (Please see the end of the program structure for all options course lists)
AND
Select and Complete Two (2) Courses from any:
University Elective
AND
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Year Four of Program
Complete the following Two (2) Courses:
Course Title Credit Points Course Code Campus Analytics in Industry 1 12 MATH2302 City Campus Analytics in Industry 2 12 MATH2303 City Campus
AND
Select and Complete Four (4) Courses from the Analytics Options list. (Please see the end of the program structure for all option course lists)
AND
Select and Complete Two (2) Courses from the Business Options list or Data Science Options list. (Please see the end of the program structure for all option course lists)
AND
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List of Options:
Analytics Options List:
Course Title Credit Points Course Code Campus Linear Programming and Modelling 12 MATH1288 City Campus Graph Algorithms and Applications 12 MATH2308 City Campus Modelling with Differential Equations 12 MATH2138 City Campus Algebra for Information Security 12 MATH2148 City Campus Statistical Inference 12 MATH2155 City Campus Mathematical Modelling 12 MATH2194 City Campus Time Series and Forecasting 12 MATH2204 City Campus Sampling and Quality Control 12 MATH2205 City Campus Sports Statistics 12 MATH2206 City Campus Data Visualisation 12 MATH2237 City Campus Analysis of Categorical Data 12 MATH2300 City Campus Predictive Modelling 12 MATH2301 City Campus Applied Bayesian Statistics 12 MATH2305 City Campus Complex Networks 12 MATH2312 City Campus Numerical Techniques 12 MATH2391 City Campus
AND
Data Science Options List:
Course Title Credit Points Course Code Campus Programming Fundamentals for Scientists 12 COSC2676 City Campus Artificial Intelligence 12 COSC1127 City Campus Programming Using C++ 12 COSC1254 City Campus Data Mining 12 COSC2110 City Campus Web Development Technologies 12 COSC2276 City Campus Database Systems 12 COSC2406 City Campus Web Programming 12 COSC2413 City Campus Cloud Computing 12 COSC2626 City Campus Big Data Management 12 COSC2632 City Campus Big Data Processing 12 COSC2633 City Campus Rapid Application Development 12 COSC2675 City Campus Web Search Engines and Information Retrieval 12 ISYS1079 City Campus Information Systems Solutions and Design 12 ISYS2047 City Campus Enterprise Information Systems 12 ISYS2425 City Campus
AND
Business Options List:
Course Title Credit Points Course Code Campus Financial Markets 12 BAFI1002 City Campus Business Finance 12 BAFI1008 City Campus Investment 12 BAFI1042 City Campus International Finance 12 BAFI1018 City Campus Risk Management 12 BAFI1026 City Campus Macroeconomics 1 12 ECON1010 City Campus Prices and Markets 12 ECON1020 City Campus Macroeconomics 2 12 ECON1042 City Campus Price Theory 12 ECON1048 City Campus International Trade 12 ECON1086 City Campus Marketing Principles 12 MKTG1025 City Campus Marketing Communication 12 MKTG1041 City Campus Buyer Behaviour 12 MKTG1050 City Campus Sales Strategy and Communication Skills 12 MKTG1048 City Campus Transportation and Freight Logistics 12 OMGT1062 City Campus Introduction to Logistics and Supply Chain Management 12 OMGT1082 City Campus Procurement Management and Global Sourcing 12 OMGT1070 City Campus Supply Chain Analysis and Design 12 OMGT2146 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 2029. If you believe you will not complete this 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.

 

Changes to BH119 Bachelor of Analytic(Honours) Effective Semester 1, 2020

In accordance with the continuous program improvement process, some changes will be made to BH119 Bachelor of Analytics (honours) effective Semester 1, 2020.

 

Courses replacements

Year or Semester	Former course/s no longer offered	Credit Points	New or amended course
Year 1	MATH2202 - Data Preparation for Analytics	12	MATH2392 - Practice of Analytics
Year 2	MATH2140 - Linear Algebra and Vector Calculus	12	MATH2311 - Applied Linear Algebra
Year 3	MATH2307 - Industrial Research Methods	12	Any Analytics Option
Program Options	Deleted Option Courses	12	Any Program Options

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