BP340 - Bachelor of Data Science

Plan: BP340P23 - Bachelor of Data Science
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 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, practical and laboratory sessions,  and provision of online materials.

You will be expected to complete all prescribed out-of-class learning activities in preparation for scheduled face-to-face and online classes, and encouraged to extend your learning by completing recommended additional readings and online activities. Of particular importance is the time spent in practical, laboratory based and work integrated learning activities that will focus on developing your employability skills and capabilities. Career Development Learning (CDL) experiences are embedded within the program and provide you with the knowledge, skills and attributes to manage your career and development throughout your life. 

Several courses in the program are delivered online, rather than on-campus, and you are likely to find that other courses transition to online delivery as you progress through the program. 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, industry-based projects, in-lab assessments, practical assignments and timed assessment.   

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.

Please read the full list of the Bachelor of Data 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 a 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 : www.rmit.edu.au/students/support-services/equitable-learning. 

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 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 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.   

In this program you will be doing specific courses 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 work integrated learning (WIL) designated courses for this program are:  

• COSC2803 – Programming Studio 1 is a studio-based simulated WIL-activity based course that focusses on the further acquisition and development of technical and professional skills for developing software applications. The studio-based approach provides an authentic problem setting. Theory and practice are blended in the application of learning centred on e.g., development of a web applications. In this course you will complete a competitor analysis, front-end and back-end design of the application, conduct a usability testing survey, full implementation of the application, and finally demonstrate to an industry expert panel the project deliverables. In the projects we have chosen for this course, we endeavour to explore Computing for Good, that is, using our skills to achieve positive impacts with our communities and beyond.
   
• COSC2816 Case Studies in Data Science (12CP) includes a work integrated learning experience in which your knowledge and skills will be applied and assessed in a simulated workplace context where feedback from data scientists working in industry is integral to your experience.
   
• COSC3043 Data Science Project (12CP) capstone course is designed  to provide you with hands-on practical experience. All your learning activities in these courses are based on applying your data science knowledge in a process of planning and executing a substantial research-based project or industry-sponsored capstone project experience.

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. 

Some courses in this program also include guest lecturers from industry, government or the community and activities relating to industry as part of the learning experience.  

On successful completion of the first two years of this program at a GPA of 3.0, you also become eligible to apply for transfer with advanced standing to the BP348 Bachelor of Data Science (Professional). The Bachelor of Data Science (Professional) allows you to extend your work related studies by an additional year where you will complete an industry placement. The industry placement (internship) provides a unique opportunity to develop your professional and technical skills while creating and extending your professional network.

Please note: students may be required to undertake additional screening/compliance checks as advised by Government, Industry or RMIT University as the need arises.  If applicable, further information will be provided once enrolment has been completed. 

International Students will need to check their Visa requirements and any work regulations/limitations before they can commence any WIL Activity.  Further information can be found under the Visa Requirements for International Students section. 

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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 (i.e.: Twenty 12 credit point courses and Two 24 credit point courses) as follows: Two (2) STEM Future Technology Skills Courses (24 credit points); and Eleven (11) Core courses including one (1) 24 credit point course and ten (10) 12 credit point courses (totalling 156 credit points); and One (1) Capstone course (12 credit points); and 96 credit points (e.g. eight 12 credit point Data Science Option courses) from one of the following possible Combinations: Combination 1: Complete Eight (8) courses from one of the Data Science Majors listed below; or Combination 2: Complete Four (4) courses from two Minors in the minor lists below (one minor must be from the Data Science Minors list); or Combination 3: Complete Four (4) courses from one of the Data Science Minors AND Four (4) Data Science Option Courses; or Combination 4: Complete Four (4) courses from one of the Data Science Minors AND up to 48 credit points of University Electives; or Combination 5: Complete Four (4) Data Science Option Courses AND University Electives up to 48 credit points. Data Science Option courses mean all courses listed within each Data Science Majors and Data Science Minors. University Electives can include any Data Science Option course, or any other course on the University Electives website. Rules on completion of Majors/Minors: A major is typically 96 credit points, and a minor is typically 48 credit points. A maximum of Two (2) Minors can be completed in this program. Please note, a course can only be counted once in your program: Any course completed as part of the core courses in the program, including where you are given a choice of core option courses, cannot count towards the completion of a major or minor. If you use a course toward the completion of a minor, you cannot use that same course again to count toward another minor. The Data Science Major and Minor courses and cross-disciplinary Minor courses can be found at the end of the program structure. The courses in each Major and Minor need to be completed in the sequence listed.
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Year One of Program
Complete the following Seven (7) Courses:
Course Title Credit Points Course Code Campus Programming Bootcamp 1 12 COSC2801 City Campus Programming Studio 1 24 COSC2803 City Campus Practical Statistics 12 MATH2412 City Campus Foundations of Artificial Intelligence for STEM 12 COSC2960 City Campus Practical Data Science 12 COSC2738 City Campus Advanced Programming for Data Science 12 COSC2815 City Campus Introduction to Cyber Security 12 INTE2625 City Campus
AND
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Year Two of Program
Complete the following Five (5) Courses:
Course Title Credit Points Course Code Campus Data Visualisation with R 12 MATH2237 City Campus The Data Science Professional 12 COSC2818 City Campus Big Data Processing 12 COSC2633 City Campus Case Studies in Data Science 12 COSC2816 City Campus Innovation Ecosystem and the Future of Work 12 OENG1235 City Campus
AND
Complete Thirty Six (36) Credit Points from your Selected Combination.
AND
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Year Three of Program
Complete the following Three (3) Courses:
Course Title Credit Points Course Code Campus Data Science Project 12 COSC3043 City Campus Machine Learning 12 COSC2673 City Campus Data Mining 12 COSC2110 City Campus
AND
Complete Sixty (60) credit points from your Selected Combination.
AND
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Major Combinations:
List of Major(s):
AND
(
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Major: Advanced Data Science
Complete the following Eight (8) Courses:
Course Title Credit Points Course Code Campus Algorithms and Analysis 12 COSC2123 City Campus Software Engineering Fundamentals for IT 12 ISYS3413 City Campus Database Applications 12 ISYS1102 City Campus Social Media and Networks Analytics 12 COSC3047 City Campus Managing Semi-structured and Unstructured Data 12 ISYS1079 City Campus Artificial Intelligence 12 COSC1127 City Campus Operating Systems Principles 12 COSC1114 City Campus Deep Learning 12 COSC2972 City Campus
OR
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Major: Cyber Security
Select and Complete Eight (8) courses from the following list that have not been completed as Core Courses:
Course Title Credit Points Course Code Campus Data Communication and Net-Centric Computing 12 COSC1111 City Campus Security in Computing and Information Technology 12 COSC2536 City Campus Security Testing 12 INTE2547 City Campus Introduction to Cybersecurity Governance 12 INTE2584 City Campus Cyber Security Attack Analysis and Incidence Response 12 INTE2626 City Campus Secure Electronic Commerce 12 INTE1071 City Campus Cloud Security 12 INTE2402 City Campus Blockchain Technology Fundamentals 12 INTE2627 City Campus Computer and Internet Forensics 12 COSC2301 City Campus
OR
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Major: Enterprise Systems Development
Select and Complete Eight (8) courses from the following list that have not been completed as Core Courses:
Course Title Credit Points Course Code Campus Further Programming 12 COSC2391 City Campus Algorithms and Analysis 12 COSC2123 City Campus Full Stack Development 12 COSC2758 City Campus Software Testing 12 ISYS1087 City Campus Database Applications 12 ISYS1102 City Campus Web Development Technologies 12 COSC2276 City Campus iPhone Software Engineering 12 COSC2471 City Campus Rapid Application Development 12 COSC2675 City Campus Software Engineering: Process and Tools 12 COSC2299 City Campus Programming Internet of Things 12 COSC2674 City Campus Enterprise Application Development 1 12 COSC3091 City Campus Mobile Application Development 12 COSC2309 City Campus
)
AND
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Data Science Minor Combinations:
List of minors:
AND
(
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Minor: Artificial Intelligence and Machine Learning
Select and Complete Four (4) Courses from the following list that have not been completed as Core Courses:
Course Title Credit Points Course Code Campus Artificial Intelligence 12 COSC1127 City Campus Games and Artificial Intelligence Techniques 12 COSC2527 City Campus Machine Learning 12 COSC2673 City Campus Deep Learning 12 COSC2972 City Campus Programming Autonomous Robots 12 COSC2814 City Campus
OR
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Minor: Blockchain Technologies
Complete the following Four (4) Courses:
Course Title Credit Points Course Code Campus Blockchain Technology Fundamentals 12 INTE2627 City Campus Developing Blockchain Applications 12 INTE2628 City Campus The Blockchain Economy 12 ECON1349 City Campus Blockchain Innovations and Case Studies 12 INTE2629 City Campus
OR
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Minor: Cloud Computing
Select and Complete Four (4) Courses from the following list that have not been completed as Core Courses:
Course Title Credit Points Course Code Campus Cloud Foundations 12 COSC2757 City Campus Cloud Developing 12 COSC2821 City Campus Cloud Operations 12 COSC2824 City Campus Cloud Architecting 12 COSC2829 City Campus Cloud Security 12 INTE2402 City Campus
OR
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Minor: Creative Computing
Complete the following Four (4) Courses:
Course Title Credit Points Course Code Campus Games Studio 1 12 COSC2348 City Campus Mixed Reality 12 COSC2476 City Campus Interactive 3D Graphics and Animation 12 COSC1187 City Campus Games and Artificial Intelligence Techniques 12 COSC2527 City Campus
OR
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Minor: Cyber Assurance
Select and Complete Four (4) courses from the following list that have not been completed as Core Courses:
Course Title Credit Points Course Code Campus Data Communication and Net-Centric Computing 12 COSC1111 City Campus Security in Computing and Information Technology 12 COSC2536 City Campus Security Testing 12 INTE2547 City Campus Cyber Security Attack Analysis and Incidence Response 12 INTE2626 City Campus Cloud Security 12 INTE2402 City Campus Computer and Internet Forensics 12 COSC2301 City Campus
OR
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Minor: Enterprise Systems Development
Complete the following One (1) Course: Note: Students who do not complete Full Stack Development as a Core Course in their program must complete it to successfully complete this minor.
Course Title Credit Points Course Code Campus Further Programming 12 COSC2391 City Campus
AND
Select and Complete Three (3) Courses from the following list that have not been completed as Core Courses:
Course Title Credit Points Course Code Campus Full Stack Development 12 COSC2758 City Campus Software Testing 12 ISYS1087 City Campus Database Applications 12 ISYS1102 City Campus Web Development Technologies 12 COSC2276 City Campus iPhone Software Engineering 12 COSC2471 City Campus Rapid Application Development 12 COSC2675 City Campus Software Engineering: Process and Tools 12 COSC2299 City Campus Programming Internet of Things 12 COSC2674 City Campus Algorithms and Analysis 12 COSC2123 City Campus Enterprise Application Development 1 12 COSC3091 City Campus Mobile Application Development 12 COSC2309 City Campus
OR
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Cross-disciplinary Minors:
List of Minors:
OR
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Minor: Data Analysis
Complete the following Three (3) Courses:
Course Title Credit Points Course Code Campus Linear Models and Experimental Design 12 MATH2203 City Campus Multivariate Analysis 12 MATH2142 City Campus Optimisation for Decision Making 12 MATH2055 City Campus
AND
Select and Complete One (1) Course from the following list:
Course Title Credit Points Course Code Campus Applied Bayesian Statistics 12 MATH2305 City Campus Analysis of Categorical Data 12 MATH2300 City Campus Time Series and Forecasting 12 MATH2204 City Campus
OR
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Minor: Bioinformatics
Complete the following Four (4) Courses:
Course Title Credit Points Course Code Campus Cell Biology and Biochemistry 12 BIOL2146 City Campus Genetics and Molecular Biology 12 BIOL2262 City Campus Computational Biology 12 BIOL2526 City Campus Genomics and Gene Technologies 12 BIOL2527 City Campus
)
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Program transition plan

Semester 2, 2024 Transition Plan

A minor amendment has been made to your program effective Semester 2, 2024. The amendments are listed below:

Old Course	Replacement Course
BIOL2527 Genomics & Gene Technologies (City)	BIOL2577 Genomics & Gene Technologies (Bundoora)

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