Welcome!

Hi, this repository is created to give a taste of what the UniMelb Data Science major (undergraduate) is like. All core subjects, as well as the electives that I took will be provided.

If you're looking for subject content, some of the following are provided:

• Lecture Notes
• Past Exams / MSTs (Crowd-Sourced cohort solutions are also provided for those pesky subjects without exam solutions)
• Textbooks
• Assignments (for the CIS subjects only)

The handbook for the Data Science major can be viewed here.

If you're from COMP20003 (or possibly COMP10002), refer to this guide for setting up your Windows device for C.

For a (very very) neat timetable planner for university, visit Rohyl's lookahead. It even has a neat interface :)

If ya liked this guide / repository, leave a cheeky Star ;)

About Me

• I'm currently a 3rd year undergrad majoring in Data Science.
• Lab demonstrator for Foundations of Computing (COMP10001) and Algorithm & Data Structures (COMP20003).
• Former Spatial Data Analyst Intern at UniMelb Infrastructure Services.
• To-be graduate Business Analyst at DXC Technology for 2020.

Data Science Major Subjects

Core Computing & Information Systems (CIS) Subjects:

• Foundations of Computing (COMP10001)
• Foundations of Algorithms (COMP10002)
• Database Systems (INFO20008)
• Elements of Data Processing (COMP20004)
• Machine Learning (COMP30027)

CIS Electives:

• Algorithm Data Structures (COMP20003)
• Artificial Intelligence (COMP30024)
• Declarative Programming (COMP30020)

Core Math Subjects:

There's a lot more maths than you think (the major shares the same core maths as Actuarial Science up to second year), and so I highly recommend you take either AM1 / AM2 or Real Analysis, or Probability will be a hard leap.

• Calculus 2 (MAST10006)
• Linear Algebra (MAST10007)
• Probability (MAST20004)
• Statistics (MAST20005)
• Linear Statistical Models (MAST30025)
• Modern Applied Statistics (MAST30027)
• Applied Data Science (MAST30034)

Science Electives:

• Physics 1 (PHYC10003)
• Fundamentals of Chemistry (CHEM10007)
• Engineering Systems Design 2 (ENGR10003)
• Science and Internship Program (SCIE30002)

Breadths:

• Japanese 3 (JAPN10007)
• Music in the Culture of the Renaissance (MUSI30011)
• High Baroque Music of the German World (MUSI30014)
• Music Health (MUSI20150)
• Positive Leadership and Careers (EDUC30072)

Subject Reviews (Pooled from a variety of people)

Foundations of Computing:

• Subject is run well both semesters and is a great introduction to Python and Computer Science.
• Not crammable. If you want to do well, you have to consistently grind and practice it (like maths).
• I recommend you waive this (and possibly FoA) if you did VCE algorithms, but otherwise do it.

Foundations of Algorithms:

• Introduction to basic sorting algorithms and the C programming language.
• Makes you appreciate memory management in Python because of bloody malloc.
• Tutors are amazing and can actually teach. (Shout out to my tutor Alex Zable).
• Usually the make it or break it point for CompSci/DataSci students.
• Learn to use valgrind if you don't want Segmentation Faults

Elements of Data Processing:

• This was THE biggest joke of a subject (in a bad way).
• Lecturer presumably got a very low SES feedback because she was fired since it was so badly run.
• Absolutely no reply from the lecturers / tutors on the discussion forum for weeks.
• Assignments / Labs / Tutorials had little correlation with the lecture content.
• If you know how to use pandas and jupyter notebook, try your hardest to get this subject waived.
• EDIT: apparently the old lecturer is back which should hopefully revive this subject.

Database Systems:

• Reneta / David are the lecturers, and both are very clear and passionate about teaching.
• Reneta is actually good once you get used to her accent trust me.
• The theory content is very useful, and the concepts taught are very applicable in real life jobs.
• 1st assignment is a bit iffy since it's a conceptual diagram of an ER-Model
• The intuition will help you for ML, AI, and any Data Science or Data Analytics position.

Algorithm Data Structures:

• By far the best 2nd year CIS subject ever (better alternative to Design of Algorithms)
• Goes through all the great algorithms, including path-finding algorithms (unlike DoA which covers hashing and compression instead)
• Assignments are great fun, and after FoA you should (hopefully) be experienced enough in C to appreciate it.
• If you're rusty on C don't worry, first few lectures is revision (we recover malloc as well for eng comp kids8)
• The 2018 Exam question about electrical outages landed me a Graduate offer at EssentialEnergy (ayyyy)
• If you took Engineering Computation instead of FoA, you're going to suffer a lot...

Artificial Intelligence:

• First third of the lectures are review of basic search algorithms (ADS students will find it a breeze).
• Assignments ARE AMAZINGLY FUN.
• Tutors and lectures are ACTUALLY GOOD.
• Hard and conceptual tutorial questions (although there is no full solution) but are quite useful in expanding your problem solving.
• Notation for Probability (YES PROB IS IN THE SUBJECT) uses logical AND/OR/NOT, so you have been warned.
• Do yourself a favor and take the subject!!!!

Machine Learning::

• Subject has no maths pre-req, but they did try to attempt to cover "Linear Statistical Models" in 1 lecture.
• The usual lecturer is great, given that the maths is not a pre-req (at least tries to make it interesting). However, he was sick for a vast majority of the time we took the subject so my experience may be a bit more biased to the worse side.
• A lot of content was attempted to be covered and felt a bit too ambitious given that the maths was not pre-req.
• First assignment is a joke if you have done maths, but the second is a lot more interesting.
• Quoting my tutor: _"this subject is a money grabber because no one would take this subject if Probability was an actual pre-req".
• Tim Baldwin got us good for Exam Section D

Maths Subjects:

• They're all well run
• Probability is a big jump (if you didn't do Real Analysis or AM1/AM2) so prepare to grind
• Have yet to find anything bad about the lecturers / lectures and content
• Yao-Ban is the best lecturer (you'll have him for LSM)

Final Tips

• I highly recommend you go learn LaTeX, which can be easily done through www.overleaf.com
• DO try your best, but WAM isn't everything (I have a mediocre WAM in the 70s) and anything above a 65+ will land you a job provided you can sell yourself well
• Try to do projects outside of university. A lot of job offers were as a result of several projects I completed outside of University.
• Be active in your tutorial and labs! It helps the tutor a lot and will help you learn better!