Description
The Master of Data Science and Decisions program takes two years and is meant to prepare scientists for the high demand for data scientists and analysts now and in the future.
They will all be well versed in the three areas of computer science, economics, and mathematics and statistics by the time they graduate.
By pursuing one of four specialties, they will also have advanced skills and understanding in data science in one of these three fields. Students will learn data science research and communication skills through project work.
What you will study
Program Structure
Core Courses - Students must take 66 UOC
Prescribed Electives - Students must take 24 UOC from their chosen specialization and 6 UOC from other specializations.
Learning Outcomes
Demonstrate that you are fully aware of the significance of science, technology, economics, and social issues in modern society, as well as how those factors may enhance living conditions.
To think clearly and speak successfully, one must read critically and understandably.
Build a research project that demonstrates technical research and design skills to promote inquiry, critical analysis, and problem-solving.
Apply cutting-edge computational and mathematical methods and business sense to big, complex data sets.
Analyze data in a mathematical context critically.
Demonstrate your knowledge of how to use both qualitative and quantitative data to generate problems and your ability to do so.
They adopt the most stringent moral standards in their professional lives.
Prepare, process, interpret, and present data using the appropriate qualitative and quantitative approaches.
Be neutral and accurate, and have a firm grasp of scientific standards and methods.
Demonstrate your understanding of how speculating influences problem identification and solution, hypothesis creation, and experiment design.
Career Opportunities
Database and systems administrators
ICT Security Specialists
Statistician, Business and Systems Analysts
Programmers
Computer Network Professionals
Admission Requirements
Entry Requirements
Admission to this degree program requires one of the following:
A Bachelor of Mathematics
A Bachelor of Science with a major in mathematics, statistics, or computer science.
A Bachelor of Data Science and Decisions
a bachelor's degree, as determined by the program's authority, in a related discipline.
You also need to have taken applicable level III university courses with an overall grade point average of 70 or higher to prove that you have the necessary background in arithmetic, statistics, and/or data science.
English Language Requirements
In order to study at UNSW, you may be required to provide proof of English proficiency, depending on your educational history and citizenship.
UNSW demands a minimum level of English language competency for enrolment since English language skills are crucial for coping with lectures, tutorials, assignments, and exams.
If you're studying for an Australian Year 12 certificate, you don't need to provide any additional evidence of your competence (e.g. NSW HSC or equivalent). Your diploma will be used to prove your English language proficiency.
It will be mentioned in your application if proof of English proficiency is necessary. You can do so by demonstrating that you meet at least one of the following requirements:
English language examinations and universities English courses
Prior English language schooling is also a requirement.
UNSW Global has English language courses and programs to help you meet the English language requirements for the degree you want. You have a choice of options depending on your current English language ability.
Tuition Fees and Scholarships
Tuition Fee
2022 Indicative First Year Fee - $46,600*
2022 Indicative Fee to Complete Degree - $46,600*
Scholarships
Each year, UNSW gives more than $83 million in scholarships. We like to support success and grant college access to kids from all backgrounds. Whether you're an American or an international student, our variety of scholarships, prizes, and awards will support you along the way.