GC173 - Graduate Certificate in Data Science

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Plan: GC173 - Graduate 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, tutorials, practical classes, studios, project work and seminars, using face-to-face, on-line, intensive, 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 all or some of the following:

  • Examinations: an individual form of assessment where you are asked to demonstrate your ability to explain principles and to solve problems;
  • 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;
  • Reflective journals: where you pause to consider what you have learnt and reflect on the further development of the related capability;
  • 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 assessment you receive, with the exception of exams, 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 have special needs or a disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You should contact the Program Manager or Equitable Learning Services.

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Work integrated learning

RMIT is committed to providing students with an education that strongly links formal learning with workplace experience. As a student enrolled in an RMIT program you will:

  • undertake and be assessed on a structured activity that allows you to learn, apply and demonstrate your professional or vocational practice
  • interact with industry and community when undertaking this activity
  • complete an activity in a work context or situation that may include teamwork with other students from different disciplines.
  • underpin your learning with feedback from interactions and contexts distinctive to workplace experiences.

In this program, you will be doing specific courses that focus on work integrated learning (WIL). You will be assessed on professional work in a work place setting (real or simulated) and receive feedback from those involved in your industry.

In MATH2349 Data Preprocessing you will develop and apply your data preprocessing skills to complex, noisy, and inconsistent real world data using leading open source software.

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Program Structure

To graduate you must complete the following. Please note, all courses listed may not be available each semester.
 

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Year One of Program
Complete the following Four (4) Courses:

Course Title

Credit Points

Course Code

Campus
Practical Data Science with Python 12 COSC2670 City Campus
Applied Analytics 12 MATH1324 City Campus
Data Wrangling 12 MATH2349 City Campus
Data Visualisation and Communication 12 MATH2270 City Campus
 

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