This is the course website. This page shows information about the consultation times, MoVE, and Schedule and lecture notes.

The “Assessments” tab above provides information about all assessment for ETC5510.



Borrow a laptop: If you are enrolled in a MoVE unit and forget your laptop, or do not own one as yet, please visit Monash Connect to borrow a laptop for an activity or part of a day:

Clayton: 7.45AM - 5PM

You may be required to provide proof of ID (student card or personal ID) in order to borrow a laptop. We have a limited amount of laptops available for students to borrow (during semester only). Collect an IT Services ticket when you go to Monash Connect.

Consultation times

Consultations begin from Week 2 (16th March)

Practical Exam

Download the practical exam at this link (Opens at 6pm).


Instructions for the group project is available via the assessments tab.


The first week of semester March 9 - March 13 is online only

The lecturer and the tutors will be available this week via the ED forum and Zoom to answer any questions you have about the course or content.

The lecturer is available for consultation at the following times

  • Monday 09/03 3-5pm
  • Tuesday 10/03 4-6pm
  • Wednesday 11/03 4-6pm

The tutors will be monitoring the ED forum during class times.

Lecture recordings for this week will be posted on Moodle. These are based on lectures recorded for ETC1010, so please ignore any reference to Rstudio cloud and instead refer to the Setup R handout.

From the second week onwards

There are two lectorials every week:

  • Monday 6-8pm (Room 321 - LTB Bldg 92)
  • Wednesday 6 - 8pm (Room 321 - LTB Bldg 92)

There are no lectorials during the midsemester break.

Week Lecture Date Slides Topic Exercise Readings Assessment
1 A 2020-03-09 Introduction to the language of data analysis

Setup R Handout

Chapter 2

Chapter 3

Chapter 4 (very short)
1 B 2020-03-11

Chapter 27

Chapters 1 - 4 in Rmarkdown for Scientists
2 A 2020-03-16 Tidy data principles, reshaping your data into tidy form, and basic data wrangling Chapter 12: Tidy Data NA
2 B 2020-03-18 Chapter 5: Data Transformation NA
3 A 2020-03-30 Plotting your data, and wrangling temporal data Chapter 3: Data visualisation (again!) NA
3 B 2020-04-01 Chapter 16: Dates and Times NA
4 A 2020-04-06 Advanced wrangling, joining tables, and advanced data visualisation Chapter 13: Relational Data NA
4 B 2020-04-08 Chapter 1 of Data Visualisation: A Practical Introduction Assignment 1
Midsemester Break
5 A 2020-04-20 Handling missing values and scraping data

Getting Started with Missing values

Exploring Imputed Values

Gallery of Missing Data Visualisations
5 B 2020-04-22

rvest R package for web scraping README

rvest description of selectorGadget
6 A 2020-04-27 Introduction to programming

Introduction to programming

Pipes (%>%)

6 B 2020-04-29


Mid Semester Electronic Exam
7 A 2020-05-04 Introuction to linear models

Intro to Modelling

Modelling Basics
7 B 2020-05-06

Building Models

Many Models
8 A 2020-05-11 Analysing text data

Introduction to tidy text analysis

Sentiment Analysis with tidy text
8 B 2020-05-13

Word and document frequency

9 A 2020-05-18 Wrangling, plotting and modeling network data None NA
9 B 2020-05-20 Project Developement / Making Dashboards None Assignment 2
10 A 2020-05-25 Computational modeling - classification and regression trees None NA
10 B 2020-05-27 None NA
11 A 2020-06-01 Project Development None NA
11 B 2020-06-03 Project Development None Takehome Exam
12 A 2020-06-08 Submission and Peer Evaluation of Projects None Project Due
12 B 2020-06-10 None Project Peer Evaluation