DATA 201 (2020) - Home Page
Announcements14/6/2020: The assignment 5 sheet below has been updated to make things a little bit easier for you. I would recommend you download this one, and paste previous answers into it. 9/6/2020: The project is also now available. It is due on last possible semester date Friday 26th June before 6pm. 2/6/2020: Assignment 5 is now available. It is due on extended date Wednesday 17th June before 6pm.
- General course information is available here.
- The course coordinator is Stephen Marsland.
- The course tutor is Michal Salter-Duke.
- Lectures will be made available as a combination of written notes, videos, and Python notebooks as they are ready. Lecturers will have a 1 hour zoom session for discussions when the course restarts. Announcements will be made via Blackboard.
- There will be drop-in zoom sessions for lab advice when the course restarts.
- The terms test was in week 7.
- Assessment is based on:
|Two submitted or available fortnightly assignments (5% each)||10%|
|Three upcoming fortnightly assignments (20% each)||60%|
|Mid-term test (1 hour)||10%|
|One project (10 hours)||20%|
- Assignments. Assignments will become available on the website about 10-14 days before they are due. They will be submitted and returned electronically through the ECS Assessment System.
|Assignments and Tests||Solutions|
|Assignment 1: Assignment 1.ipynb||Assignment 1_Solution.ipynb: Assignment 1 Solution|
|Assignment 2: A2.ZIP||Suggested solution (uploaded 13/5): A2_sol.ipynb|
|Assignment 3: Questions and the data required: EuropeanBirds.csv, and some useful background information: EuropeanBirds-Information.txt. Submit via the ECS submission system by 6pm on Friday 15 May.||Assignment 3 Solutions|
|Term test: Questions are in this zipfile: DATA201-MidtermTest.zip Submit via the ECS submission system by Fri 22 May at 4pm.||DATA201 Test Solutions|
|Assignment 4: Questions and the data required: A4.ZIP. Submit via the ECS submission system by midnight on Saturday 30 May.||A4-solution.zip|
|Assignment 5: Questions and data. Submit via the ECS submission system by 6pm on Wednesday 17th June.|
|Project: DATA201proj.pdf by 6pm on Friday 26th June.|
- Paper Free This is a paperless course. All course materials will be placed on the website in advance of the lecture.
- Software This course will use Python 3 in Jupyter Notebooks. This is free software that should run on most computers (but not tablets or phones). The guide to running a notebook here is a good place to start with installing the software, which will also be available on Victoria computers.
|projectdata.zip||Data for the project|
|daily_flask_co2_nzd.csv||CO2 data for NZ|
|daily_flask_co2_mlo.csv||CO2 data for Mauna Loa|
|TaranakiStWharf.csv||Water quality at Taranaki St|
|LifeExpectancy.csv||Life Expectancy Dataset|
|Olympic100m.csv||Olympic Games 100m times|
|SAheart.csv||Heart Health data|
|SURFIncomeSurvey.csv||SURF Income Survey from Stats NZ|
|fishdata.csv||Fish weights and lengths|
|titanic.csv||Passenger list of the Titanic|
Mid-term TestThe mid-term test will cover material from the first half of the course (until Week 6). You will need to consider what a data scientist does, with respect to possible data problems. You will not need to write any completed code, but you will be expected to understand a given code and/or to fill in a few missing lines of a given code. The format of most of the questions will be similar to last year exam questions: 2019_1_DATA201.pdf (for example, Question 4(a-d), Question 5(a-e,g-j)).
A note on AssignmentsThe purpose of the lab session and the assignments is to help you learn. Attending labs and lectures, and working seriously on assignments, is strongly correlated with success in mathematics courses. Ignore this at your peril.
Class representativesThe class representative for this course is Elyse Smaill (email@example.com). The Facebook page is here.
University policies and statutesIt is worthwhile becoming familiar with the following information. Other relevant policies can be found at the academic policy website.
|zip||A4-solution.zip||manage||69 K||16 Jun 2020 - 23:21||Main.nguyenb5||A4 solution|
|ipynb||Assignment 1_Solution.ipynb||manage||94 K||15 May 2020 - 09:45||Main.marslast||Assignment 1 Solution|
|ipynb||Assignment5.ipynb||manage||202 K||14 Jun 2020 - 22:03||Main.marslast||A|
|DATA201proj.pdf||manage||122 K||09 Jun 2020 - 14:49||Main.marslast||Project Specification|
|ipynb||Introduction to Pandas.ipynb||manage||36 K||25 Feb 2020 - 16:49||Main.marslast||Introduction to Pandas|
|Introduction_to_Matplotlib.pdf||manage||996 K||11 Mar 2020 - 23:41||Main.nguyenb5||Introduction to Matplotlib|
|Introduction_to_NumPy.pdf||manage||252 K||09 Mar 2020 - 22:08||Main.nguyenb5||Introduction to NumPy|
|Introduction_to_Pandas.pdf||manage||412 K||11 Mar 2020 - 23:42||Main.nguyenb5||Introduction to Pandas|
|ipynb||Lecture 8.ipynb||manage||88 K||18 May 2020 - 21:29||Main.marslast||Lecture 8 Python Notebook|
|ipynb||Lecture11.ipynb||manage||83 K||16 Jun 2020 - 18:25||Main.marslast|
|ipynb||Lecture9.ipynb||manage||43 K||30 May 2020 - 10:11||Main.marslast||Lecture 9 Python Notebook|
|Linear Algebra.pdf||manage||2 MB||18 May 2020 - 21:27||Main.marslast||Notes for Week 8|
|zip||Notebooks.zip||manage||898 K||11 Mar 2020 - 23:43||Main.nguyenb5||Week 2 - Notebook files|
|Optimisation.pdf||manage||6 MB||14 Jun 2020 - 22:32||Main.marslast||Notes|
|Principal Components Analysis.pdf||manage||4 MB||30 May 2020 - 10:10||Main.marslast||Lecture 9|
|Python_Programming_Basics.pdf||manage||334 K||09 Mar 2020 - 22:08||Main.nguyenb5||Python Programming Basics|
|ipynb||Tutorial 2-Solution.ipynb||manage||409 K||02 Jun 2020 - 08:33||Main.marslast||Tutorial solution|
|ipynb||Tutorial 2.ipynb||manage||407 K||24 May 2020 - 20:49||Main.marslast||Week 8 tutorial|
|ipynb||Tutorial3.ipynb||manage||64 K||30 May 2020 - 10:16||Main.marslast||Week 9 tutorial|
|ipynb||Tutorial3_solution.ipynb||manage||66 K||08 Jun 2020 - 12:37||Main.marslast||Tutorial solution|
|ipynb||Tutorial9.ipynb||manage||4 K||14 Jun 2020 - 22:35||Main.marslast|
|ipynb||Tutorial9_solutions.ipynb||manage||26 K||22 Jun 2020 - 21:44||Main.marslast|
|jpg||cute.jpg||manage||67 K||24 May 2020 - 20:50||Main.marslast||Picture for week 8 tutorial|
|txt||landmark_faces.txt||manage||4 MB||02 Jun 2020 - 16:28||Main.marslast||Face landmark dataset|
|lecture_4.pdf||manage||3 MB||17 Mar 2020 - 01:07||Main.nguyenb5||Lecture 4 slides|
|lectures_5_6.pdf||manage||5 MB||20 Mar 2020 - 03:10||Main.nguyenb5||Lectures 5-6 slides|
|zip||lectures_5_6_nb.zip||manage||930 K||20 Mar 2020 - 03:10||Main.nguyenb5||Lectures 5-6 notebook|
|zip||projectdata.zip||manage||1 MB||09 Jun 2020 - 14:48||Main.marslast||Data for the project|
|zip||tutorial_3_sol.zip||manage||27 K||09 Apr 2020 - 20:28||Main.nguyenb5||Tutorial 3 Solution|
|w2_lecture_2.pdf||manage||2 MB||11 Mar 2020 - 23:47||Main.nguyenb5||Week 2 - Lecture 2|