3 Guide for choosing MDSI Subjects
The Master of Data Science and Innovation (MDSI) focuses on filling the talent gap for people interested in expanding their knowledge in analytics, data science and innovation. MDSI is currently under UTS’s Faculty of Transdisciplinary Innovation (FTDi) and their vision is to ensure students stand out, become better decision makers, are creative and gain hands-on skills with both data and analytical tools. They are committed to keep the students up to date with what’s current in the world of data science and that’s why there’s adjustments in the core and elective subjects. For graduates to succeed it’s important for them to be well equipped.
3.1 MDSI Core subjects
It’s mandatory for students to complete 96 credit points(cp) consisting of 56CP core and 40CP electives. Optional subjects can be selected from a list that has been made available specifically for development of MDSI students or across the university’s discipline which is conditional upon approval by the course director or subject coordinator and normally requires one to meet prerequisites. This course structure is very accommodating as it gives students the ability to set and achieve career goals. Full-time students who enrol for the Autumn session intake, this course is offered on a two-year plan. And for those who enrol for the Spring session intake, this course is offered on a two-and-a-half-year, full-time plan. Part-time students usually plan to complete the course in four years with the flexibility to extend the duration up to six years.
List of core subjects which the students must study as part of their MDSI course:
| . | ID | Subject | Credits | Prerequisites | Semester |
|---|---|---|---|---|---|
| 1 | 36102 | iLab 1 | 12 | 36100 - Data Science for Innovation 36106 - Data, Algorithms and Meaning |
Spring |
| 2 | 36105 | iLab 2 | 12 | 36100 - Data Science for Innovation 36106 - Statistical Thinking for Data Science |
Spring |
| 3 | 36104 | Data Visualisation and Narratives | 8 | 36100 - Data Science for Innovation 36103 - Statistical Thinking for Data Science |
Autumn |
| 6 | 36106 | Data, Algorithms and Meaning | 8 | None | Autumn |
| 7 | 36100 | Data Science for Innovation | 8 | None | Spring / Autumn |
| 8 | 36103 | Statistical Thinking for Data Science | 8 | None | Spring / Autumn |
iLabs are the capstone projects, available only in the Spring sessions. ILab 1 and ILab 2 are the two capstone projects each for 12 credit points, and are mandatory for all the students, in order to complete the master’s degree in Data Science and Innovation. ILab 1 is for the students in the first year and it is a prerequisite for the ILab 2. These capstone projects are with the industry partners, giving you a hands-on experience, working with real dataset and also in real industry environment.
3.2 MDSI Elective subjects
Students of Master of Data Science (MDSI) are required to choose 40 cp (credit points) worth of electives from the list of subjects that are offered. Refer
UTS MDSI Electives for complete list of FTDI electives
Since the Spring session of 2019, these two Core subjects have been made electives:
Data and Decision Making
Leading Data Science Initiatives
The following two subjects have been added to the FTDI electives choice block:
Data Science Practice
Deep Learning
Please note that these new electives are currently being offered every two years. Please check with your course coordinator if you wish to study these subjects.
3.3 MDSI Other offerings
3.3.1 Non-Award Study
One is able to study a single subject without fully committing to a degree - through the non-award study. This is one of the ways you can upgrade your skills in data science. According to UTS non-award-study these subjects can actually be recognised in future studies.
3.3.2 Special Subject
A student can opt for either a small project or self-study subject (2 credit points). The student needs to find a faculty member who is willing to be their academic advisor. The proposal is essentially a one-pager on what the student plans to do and the deliverables includes a report/code (for project option), a teaching slide deck, an assignment (for self study option). This subject is generally available to only those who require 2cp to complete the program.
3.3.3 Flexible Teaching Mode
MDSI classes are usually held after 1730hrs during the week and during the day on saturdays.Usually, students are expected to be on campus approximately 6 or 8 times a month during the semester.This enables students to have a work-school balance.
3.4 Guide for choosing Core subjects
As part of the MDSI course, students are required to complete two iLab projects. The iLabs are only available in Spring session to enrol and has certain prerequisites.
Full-time students (especially the international students) of Autumn session intake must complete Data Science Practices block of Core subjects to eligible for iLab1 in the following Spring session and must complete Data Visualisation and Narratives Core subject to eligible for iLab2 in the following year’s Spring session.
A typical load for an Autumn session intake full-time student looks like below:
| Year | Semester | ID | Subject | Credits |
|---|---|---|---|---|
| 1 | Autumn | 36100 | Data Science for Innovation | 8 |
| 1 | Autumn | 36103 | Statistical Thinking for Data Science | 8 |
| 1 | Autumn | 36106 | Data, Algorithms and Meaning | 8 |
| 1 | Spring | 36102 | iLab 1 | 12 |
| 1 | Spring | cbk91807 | Elective subjects | 12 |
| 2 | Autumn | 36104 | Data Visualisation and Narratives | 8 |
| 2 | Autumn | cbk91807 | Elective subjects | 16 |
| 2 | Spring | 36105 | iLab 2 | 12 |
| 2 | Spring | cbk91807 | Elective subjects | 12 |
A typical load for a Spring session intake full-time student looks like below:
| Year | Semester | ID | Subject | Credits |
|---|---|---|---|---|
| 1 | Spring | 36100 | Data Science for Innovation | 8 |
| 1 | Spring | 36103 | Statistical Thinking for Data Science | 8 |
| 1 | Spring | cbk91807 | Elective subjects | 8 |
| 1 | Autumn | 36106 | Data, Algorithms and Meaning | 8 |
| 1 | Autumn | 36106 | Elective subjects | 16 |
| 2 | Spring | 36102 | iLab 1 | 12 |
| 2 | Spring | 36106 | Elective subjects | 12 |
| 2 | Autumn | 36104 | Data Visualisation and Narratives | 8 |
| 2 | Autumn | 36106 | Elective subjects | 4 |
| 3 | Spring | 36105 | iLab 2 | 12 |
Some of the important criteria, a student should keep in mind while choosing the iLab project are:
- Identify / create a project which means something to you and in future will help you become more employable. Where do you want to work? What do you want to be doing? What do you need to learn to get you there and how can this opportunity help make that happen? Students get an opportunity to choose the project from the one’s that are being provided by UTS, or they can get their own project on which they wish to work, but those need to get approved by the Faculty. These projects are available in different domains, and students have full freedom to choose the project of their interest as per the industry that they aiming to work in future.
- Mentors are assigned to all the students, who periodically keep guiding the students, to ensure they are on the correct learning path and also to guide them in order to get the deliverable completed on time.
- An added advantage for international students in terms of capstone project is that they get to work in the Australian work culture and that obviously makes them industry ready in both the aspects – Technically and Non- Technically by interacting with the Client time to time and at the end delivering the project with the final report and the presentation.
Notes for international students:
- You must be enrolled to full 24 credit points in each semester
- If you take less than 24, you must check your visa conditions and requires approval from the course coordinator
3.5 Guide for choosing Elective subjects
Students are advised to plan the subjects well in advance, as some subjects are only offered once per year while some are offered once every two years. There are a couple of electives which are taught every two years because of low enrollment figures. These two subjects (Data Science Practice and Deep Learning) have recently been added to the MDSI course and are currently being offered less frequently than the Core subjects. While this arrangement may change over time and these subjects may be offered more frequently, yet students may end up in a situation where a subject they are interested in, is not being offered because of low enrollment volumes. It is advised that if a student is interested in such subjects, to form a group on Slack and generate interest (use mdsi_announcement channel to create awareness and form a community). If the faculty sees interest from enough students, then they can look at running the subject out of the usual planned cycle.
Sign-up to the MDSI Slack group at: https://utsmdsi.slack.com/
Tips:
Do your directed electives first. Directed electives are where a subject is a prerequisite for your higher studies. For example, you would want to brush your database/unix/Python skills before pursuing Advanced Data Analytics subject; in which case, plan to do those directed electives first before taking up advance subjects Unix Systems Programming Database Fundamentals Data Science Practice
Do your specialisation electives in the last year/semester. These electives could be specific to your future objectives like entrepreneurship or marketing or finance fields Start-up Data, Marketing and Sales Financial Modelling and Analysis Deep Learning
Join the MDSI Slack channel mdsi_electives The students community on slack is really helpful. You can browse through the chat history on the feedback and recommendation on elective subjects and also ask for any new info on that channel.
There is an official repository/database of electives that are currently offered in MDSI. The link to the database is https://selectives.utscic.edu.au/ Login using the student id and password. It captures MDSI student reviews on the subjects that they have undertaken as part of their MDSI course. But it is recommended to look for the latest information on the slack channel (mdsi_electives) or ask your course coordinator.
Enrol in to additional subjects than you intend to do in your semester. This gives you an opportunity to try multiple subjects before the census date and drop the subjects that you dislike. Every semester students get the opportunity to withdraw from their enrolled subject before the census date, which is usually within 4 weeks of the semester start date.
Your two friends, who will help you in choosing your subjects, are the UTS Handbook and Timetable Planner
- Choose a subject you are interested in from a list of subjects from the Handbook.
- Locate the course code of the subject you are interested in the handbook.
- Look up that course code in UTS Timetable and check if the subject is available in the semester you wish to study.
If you are working full-time, you would be interested in the subjects that are offered outside the business hours. The timetable will help you choose the subjects which are offered in the semester/timing of your choice. Note: The MDSI students are allowed to enrol in to up to two undergraduate subjects from any faculty. Make sure to confirm the undergraduate allowance with your course coordinator before applying as the allowance may change every year.
3.6 Enrolling in the subjects
All of the FTDI Core and Elective subjects are available under One-Stop Admin to enrol. For enrolling into any elective subjects from other faculties, you will need to raise an e-request. Make sure you submit your e-requests well before the semester is due to start. It takes a couple of weeks for your e-requests to go through. You can also raise online general enquiries through e-request, for getting any type of information or for requesting the status of your e-request. For some subjects (especially in the other faculties) you can request a waiver for prerequisite of those subjects. Please speak to your course coordinator to arrange prerequisite waiver. You might need to submit a cover letter to justify the exception.