KPT/JPT (N/481/6/0817) 09/24 - MQA/PSA 12435
This is the era of big data and artificial
intelligence. Data science represents
a cutting-edge discipline which applies
scientific methods, mathematics,
algorithms and artificial intelligence to
extract and visualise intelligent insights
from huge volumes of data.
In the fast-progressing world of the Information
Age these insights, whether delivered via
autonomous integrated systems or in traditional
reports, have the potential to fuel innovation and
transform decision making. Data scientists deal
with the challenges of big data – its interpretation,
management and use – in fields as diverse as
marketing, information systems, engineering,
finance, arts, humanities, science and medicine.
Monash brings an enormous breadth of expertise
to bear on issues relating to big data. We have the
greatest collection of expertise in the theory and
practice of data analytics of any university in the
Asia-Pacific region. If you aspire to solve
real-world problems based on the information
challenges of big data, then specialising in data
science will equip you with the practical skills to
excel in your chosen career – whether as a data
scientist, analytics professional, big data architect,
information visualisation expert or chief
information officer.
2020 fees per year
This level consists of mathematics and
introductory computer science units. Industrial training
Core Units
• Algorithms and programming fundamentals in
Python
• Introduction to computer systems, networks
and security
• Introduction to computer science
• Introduction to data science
• Discrete mathematics for computer science
• Continuous mathematics for computer science.
Elective Units
Select two units from the list below or from
another school:
• Business and economics statistics
• Programming fundamentals in Java.
LEVEL TWO
Core Units
• Algorithms and data structures
• Theory of computation
• Modelling for data analysis
• IT professional practice and ethics
• Databases.
Areas of study LEVEL THREE
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• Core Units
• Data visualisation
• Data science project 1
• Data science project 2.
Course structure
This course consists of 14 compulsory (core)
units in computer science, data science and
mathematics, two restricted electives chosen
from an approved list of data science topics,
eight free elective units, and an industry
attachment. The free electives can be taken
as a sequence in a specific field of study within
the school or from a discipline offered by another
school. A capstone project spanning both
semesters of the third year concludes your
studies.
February, July and October
LEVEL ONE After completing this specialisation, you’ll be able to:
• analyse problems, design algorithms to solve
them, and program efficient software solutions
• apply problem solving strategies to develop
efficient solutions.
Mathematical statistics
Principles of data science
Business intelligence and data warehousing
Data analytics and visualisation
Big data
Deep learning and artificial intelligence.
3 years
RM40,820 Malaysian student
RM46,640 International student
Elective Units
Select any three units from the list below or from
another school.
• Operating systems
• Object-oriented design and implementation
• Mobile application development
• Introduction to cyber security
• Introductory econometrics.
Learning outcomes
BACHELOR OF COMPUTER
SCIENCE IN DATA SCIENCE
CAREER PATHS
Graduates with data science skills are
in high demand. Possible careers could
include:
• business intelligence analyst
• chief data officer
• data analyst
• data architect
• data mining engineer
• data scientist
• quantitative analyst
• quantitative researcher.
You’ll be able to work in a wide
range of industries, such as:
• digital humanities
• consulting
• cybersecurity
• law
• scientific research
• marketing
• robotics
• engineering
• business analytics
• banking.
Elective Units
You’re required to complete three electives in your
third year. You can select any unit from any
school, but must complete at least two approved
data science elective units from the list below:
• Data analytics
• Deep learning
• Business intelligence and data warehousing
• Big data management and processing
• Parallel computing
• Image processing
• Information and network security
• Intelligent systems
• Usability.
UNDERGRADUATE PROSPECTUS 2020
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