MDDU-DATSC v.2 Data Science Double Degree Major (BSc/BA, BSc/BCom)
Curtin University
About
This major/stream is part of a larger course. Information is specific to the major/stream, please refer to the course for more information.
Data is used in all industries to drive innovation and growth. Data Scientists are able to harness the power of data to be central in these processes. A Bachelor of Science (Data Science) alongside a Bachelor of Arts or Bachelor of Commerce is a powerful combination that will enable graduates to use data to solve problems through an in-depth understanding of appropriate analytics and the field in which the solutions will be applied. Through the BSc (Data Science) Double Degree Major, students will gain a high level of computer programming competency, the capacity to apply appropriate statistical procedures to large datasets from different sources, an understanding of contemporary digital media, and the ability to communicate data-driven solutions to a range of audiences. The field of data science is truly interdisciplinary and is therefore taught by experts from an array of discipline areas including Computing, Mathematics and Statistics, Humanities and Economics.
Structure
Major/Stream Learning Outcomes
A graduate of this course can:
1. understand the theoretical background to processes for efficient collection, management, secure storage and analysis of large data sets
2. formulate hypotheses about data and develop innovative strategies for testing them by implement appropriate algorithms to analyse both large and small datasets
3. extract valid and meaningful conclusions from various types of large data sets that can support evidence based decision making
4. communicate approaches and solutions to data science problems to a range of audiences in a variety of modes
5. identify, select and use appropriate open source and proprietary data management and analysis tools to identify patterns or relationships in large volumes of data
6. recognise the importance of continuous learning opportunities in a rapidly developing field and engage in self-driven development as a data scientist
7. understand the global nature of data science and apply appropriate international standards in data science and data analytics
8. work collaboratively and respectfully with data scientists from a range of cultural backgrounds
9. work professionally and ethically on independent data science projects and as a team member working collaboratively to innovative data science solutions
Duration and Availability
4 years or equivalent part time study
YEAR 1 SEMESTER 1
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
STAT1003 | v.1 | Introduction to Data Science | 5.0 | 25.0 |
COMP1005 | v.1 | Fundamentals of Programming | 4.0 | 25.0 |
50.0 |
YEAR 1 SEMESTER 2
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
NPSC1003 | v.2 | Integrating Indigenous Science and STEM | 3.0 | 25.0 |
STAT1005 | v.1 | Introduction to Probability and Data Analysis | 3.0 | 25.0 |
50.0 |
YEAR 2 SEMESTER 1
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
MATH1015 | v.1 | Linear Algebra 1 | 4.0 | 25.0 |
ISEC2001 | v.2 | Fundamental Concepts of Data Security | 3.0 | 25.0 |
STAT2005 | v.1 | Computer Simulation | 4.0 | 25.0 |
75.0 |
YEAR 2 SEMESTER 2
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
STAT1006 | v.1 | Regression and Nonparametric Inference | 4.0 | 25.0 |
ISYS1001 | v.1 | Database Systems | 4.0 | 25.0 |
COMP1002 | v.1 | Data Structures and Algorithms | 4.0 | 25.0 |
75.0 |
YEAR 3 SEMESTER 1
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
CWRI3012 | v.3 | Interaction Design and Visualisation Technologies | 3.0 | 25.0 |
CNCO3003 | v.1 | Mobile Cloud Computing | 3.0 | 25.0 |
50.0 |
YEAR 3 SEMESTER 2
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
STAT2003 | v.1 | Analytics for Experimental and Simulated Data | 5.0 | 25.0 |
COMP3009 | v.1 | Data Mining | 3.0 | 25.0 |
50.0 |
YEAR 4 SEMESTER 1
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
COMP3006 | v.1 | Artificial and Machine Intelligence | 3.0 | 25.0 |
COMP3001 | v.1 | Design and Analysis of Algorithms | 4.0 | 25.0 |
50.0 |
YEAR 4 SEMESTER 2
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
STAT2004 | v.1 | Analytics for Observational Data | 4.0 | 25.0 |
SELECT OPTIONS TO THE TOTAL VALUE OF: | 25.0 | |||
50.0 |
OPTIONS TO SELECT FROM IN YEAR 4 SEMESTER 2
Code | Version | Course Name | HRS/WK | Credit |
---|---|---|---|---|
MATH3004 | v.1 | Industrial Project | 4.0 | 25.0 |
COMP3005 | v.1 | Computer Project 2 | 9.0 | 25.0 |
ISYS3002 | v.2 | Information Systems and Technology Project 2 | 2.0 | 25.0 |
Institution
