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UniMelb-Data-Science

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Lecture Notes, Assignments and Past Exams

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## 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](https://handbook.unimelb.edu.au/2019/components/b-sci-major-8).

If you're from COMP20003 (or possibly COMP10002), refer to [this guide](https://github.com/akiratwang/COMP20003-Setting-Up) for setting up your Windows device for C.

For a (very very) neat timetable planner for university, visit Rohyl's [lookahead](https://lookahead.rohyl.io/). 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!

UniMelb-Data-Science

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