Graduate Certificate in Analytics
Royal Melbourne Institute of Technology
About
Analytics is an established quantitative field within both industry and higher education.
With exponential growth in available data, the analytics discipline has emerged as a key field requiring skilled analysts.
The Graduate Certificate in Analytics is designed to enable you to develop your critical knowledge of analytics and statistics, and your ability to apply this knowledge across a range of professions.
The program will provide you with a foundation to develop your analytical capability further through your professional practice and/or further study.This program is delivered on campus;
some courses may be available online.
Structure
Year One of Program
Complete the following Three (3) Courses;
| Course Title | Credit Points | Course Code | Campus |
|---|---|---|---|
| Applied Analytics | 12 | MATH1324 | City Campus |
| Data Wrangling | 12 | MATH2349 | City Campus |
| Database Concepts | 12 | ISYS1055 | City Campus |
Select and complete One (1) of the following Courses;
| Course Title | Credit Points | Course Code | Campus |
|---|---|---|---|
| Essential Mathematics | 12 | MATH2267 | City Campus |
| Machine Learning | 12 | MATH2319 | City Campus |
| Time Series Analysis | 12 | MATH1318 | City Campus |
| Data Visualisation and Communication | 12 | MATH2270 | City Campus |
| Introduction to Statistical Computing | 12 | MATH1322 | City Campus |
| Introduction to Information Security | 12 | INTE1120 | City Campus |
Entry requirements
Program Entry Requirements: An Australian Bachelor degree (AQF 7) or equivalent from a recognised tertiary institution OR No formal qualification but a minimum of 5 years full time work experience in an industry setting
International qualifications are assessed according to the Australian Qualifications Framework (AQF).
English language Requirements:
IELTS - 6.5+ (no band less than 6.0) For equivalents to English entry requirements, see the English equivalents web page
Learning outcomes
You are expected to develop the following Program Learning Outcomes:
Personal and professional awareness
- critically reflect on your own practice to support your continual improvement and become a life long learner.
Knowledge and technical competence
- demonstrated knowledge of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
Problem-solving
- bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
Information literacy
- locate and use data and information and evaluate its quality with respect to its authority and relevance.
Institution
