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Students
Tuition Fee
AUD 38,400
Per year
Start Date
Medium of studying
Duration
36 months
Program Facts
Program Details
Degree
Bachelors
Major
Data Science | Data Analytics
Area of study
Information and Communication Technologies
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
AUD 38,400
Intakes
Program start dateApplication deadline
2024-02-01-
2024-07-01-
About Program

Program Overview


RMIT University's Bachelor of Data Science equips students with in-demand skills in data analytics, statistics, computer science, and machine learning. Through a mix of lectures, seminars, and hands-on experience, students develop independent learning abilities and prepare for careers as data scientists, business intelligence developers, and other data-related roles. The program includes a capstone project with industry and a choice of majors and minors in data science fields.

Program Outline

Degree Overview:

The Bachelor of Data Science at RMIT University is designed to equip students with in-demand skills in data analytics, statistics, computer science, machine learning, programming, mathematics, and data wrangling. Graduates will be able to play a central role in business decision-making, corporate strategy, and government planning.


Outline:


Year 1:

  • Core data science subjects: mathematics for computing, artificial intelligence, data science, programming, and cyber security
  • Innovative programming bootcamp and studio subjects

Year 2:

  • Choice of majors and minors in data science fields
  • Data science case study
  • STEM for sustainable development or cyber-physical-biological systems

Year 3:

  • Capstone project with industry
  • Machine learning or data mining subject

Assessment:

  • Exams, tests, quizzes, essays, reports, assignments, projects, presentations, and laboratory assessments

Teaching:

  • Focus on developing independent learning skills
  • Mix of lectures, seminars, tutorials, workshops, and practical and laboratory classes
  • Bootcamp2Studio model for hands-on experience and real-world skills

Careers:

  • Data scientists
  • Business intelligence developers
  • Data analysts
  • Data architects
  • Data engineers
  • Data miners
  • Machine learning engineers
  • Programmers
  • Research data scientists
  • Web analysts
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