Bachelor of Science (Data Science)
|Location||Curtin Singapore campus|
|Study mode||Full-time or part-time|
|Duration||2 years full-time (6 trimesters) or part-time equivalent|
Find the key to innovation, analysing big data to predict future trends and inform industry decisions.
Data scientists collate and analyse large volumes of data and communicate their findings to a range of audiences. Their ability to use big data to predict future trends is becoming an essential part of decision making in business and government.
Data is being generated at an unprecedented rate and its availability will continue to increase. Every industry is using large volumes of data – from predicting weather patterns and optimising harvesting in agriculture, to improving patient diagnosis in the health industry, to enhancing the management of remote infrastructure in mining.
This is a multidisciplinary major. It combines studies in computing, emerging internet technologies, media and statistics. You will gain a foundation in programming and statistics, which will form the basis of higher-level studies in data mining, data security and computer simulation.
This course builds your capacity to extract, analyse and visualise large volumes of data and communicate analytical outcomes to a range of audiences. You’ll graduate equipped to enter a range of industries where data science is key to innovation.
In keeping with Curtin’s strong links with industry, this course has an industry advisory group that provides guidance about the course content. The group comprises representatives from the resources sector, management consulting, data analytics services and spatial data product developers, and enterprises such as Optika Solutions and PwC.
What you’ll learn
- Understand the theoretical background to processes for efficient collection, management, secure storage and analysis of large data sets
- Formulate hypotheses about data and develop innovative strategies for testing them by implement appropriate algorithms to analyse both large and small datasets
- Extract valid and meaningful conclusions from various types of large data sets that can support evidence based decision making
- Communicate approaches and solutions to data science problems to a range of audiences in a variety of modes
- Identify, select and use appropriate open source and proprietary data management and analysis tools to identify patterns or relationships in large volumes of data
- Recognise the importance of continuous learning opportunities in a rapidly developing field and engage in self-driven development as a data scientist
- Understand the global nature of data science and apply appropriate international standards in data science and data analytics
- Work collaboratively and respectfully with data scientists from a range of cultural backgrounds
- Work professionally and ethically on independent data science projects and as a team member working collaboratively to innovative data science solutions
|GCE A Level/STPM||8|
|China Gao Kao||70%|
|ATAR (including WACE/SACE/HCE/VCE)||80|
Essential course prerequisites
Mathematics (including calculus).
Minimum English language entry requirements
|IELTS Academic (International English Language Testing System)|
|Overall band score||6.0|
Local polytechnic diploma qualifications are accepted into the course.
If your qualification is not listed above, please contact the Curtin Singapore Admissions Office at email@example.com for assistance.
|Tuition fee for local students|
|Total tuition fee||S$34,000|
|Payable over six trimester instalments|
|Tuition fee for international students|
|Total tuition fee||S$48,000|
|Payable over six trimester instalments|
All fees are inclusive of 7% GST. Fees listed are indicative and subject to annual increases.
- Data analyst
- Data scientist
- Agriculture and environment
- Economics, business, banking and finance
- Geographic information science
- Health science
- Oil and gas
- Supply chain logistics
If you have previously studied in this field, you are encouraged to contact Curtin Singapore to discuss eligibility for Credit for Recognised Learning (credit for unit exemptions). Students with a diploma qualification in a related field may be eligible for credit exemptions.
Credits needed to graduate: 600.
Each unit is 25 credits unless stated otherwise.
- Integrating Indigenous Science and STEM
- Introduction to Probability and Data Analysis
- Fundamentals of Programming
- Introduction to Software Engineering
- Data Structures and Algorithms
- Regression and Nonparametric Inference
- Linear Algebra 1
- Unix and C Programming
Units are not necessarily shown in the order studied. Units are subject to change.