UC008 - Undergraduate Certificate in Data Science

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Plan: UC008 - Undergraduate Certificate in Data Science
Campus: City Campus

Program delivery and structure

Approach to learning and assessment
Work integrated learning
Program structure

Approach to learning and assessment

Your learning experiences will contain a broad mix of study modes including lectures, lectorials, tutorials, practical classes, workshops, project work, using face-to-face, on-line, and other flexible delivery mechanisms.

Assessment is designed to give you opportunities to demonstrate your capabilities. You will find that the forms of assessment used may be different for each course, depending on the course objectives and learning outcomes.

Your assessment in this program will include some or all of the following:

  • Assignments and projects: some will require you to demonstrate an ability to work alone, while some will involve group work requiring you to be part of team with other students;
  • Assessed tutorials or presentations: a form of in-class test which you will be required to complete either individually or as a team;
  • Self-assessment and peer-assessment: for assessment activities such as seminars you may be asked to assess your own work, the work of your group, or the work of other groups. This is part of equipping you to become more independent in your own learning and to develop your assessment skills.

The assessments you complete will enable the teaching staff to provide you with feedback on your progress. This will enable you to improve your performance in the future.

If you are living with disability, long-term illness and/or a mental health condition, we can support you by making adjustments to activities in your program so that you can participate fully in your studies. To receive learning adjustments, you need to register with Equitable Learning Service (https://www.rmit.edu.au/students/support-and-facilities/student-support/equitable-learning-services

The University considers the wellbeing and safety of all students, staff and the community to be a priority in on-campus learning and professional experience settings. 

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Work integrated learning

This program includes a Work Integrated Learning experience in the following option course:

MATH2382 Data Preprocessing

If you choose to enrol in this course, you will be assessed on professional work in a simulated workplace setting and receive feedback from those involved in your industry.

This program builds foundational knowledge as a pathway to the RMIT Bachelor of Data Science (BP340) which contains Work Integrated Learning courses in year two and year three.

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Program Structure

To graduate you must complete the following: All courses listed may not be available each semester
 

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Year One of Program

Complete the following Three (3) Courses:

Course Title

Credit Points

Course Code

Campus

Introduction to Programming 12 COSC1519 City Campus
Practical Data Science 12 COSC2738 City Campus
Mathematics and Statistics 12 MATH2123 City Campus
AND
Select and Complete One (1) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Data Preprocessing 12 MATH2382 City Campus
Practical Database Concepts 12 ISYS3412 City Campus
Introduction to Probability and Statistics 12 MATH2200 City Campus
 

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