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 ETC1010.



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.

We will use the rstudio cloud server. In the future we may have R and Rstudio installed locally. When this happens, you can use USB stick, attach to the borrowed laptop, and install R, RStudio and all your packages on this. Use can then use the USB stick as your working environment, with the borrowed laptop simply as the computing engine.


Exercises on rstudio cloud (

  • You can follow the link on this slide to establish your account (this link will expire in week 3)
  • Every time we go to use in class, you will log in to using your monash gmail account.

Consultation times

Consultations begin from Week 2 (16th March)

All consultations are in Menzies W1105

  • Steph Kobakian: Tuesday 2-5pm
  • Sarah Belet: Monday 3-4pm
  • Sherry Zhang: Friday 1-2pm
  • Nitika Kandhari: Tuesday 4-5pm
  • Nick Tierney: Monday 12:00pm - 1pm
  • Nick Tierney: Wednesday 10:30 - 11:30am

Practical Exam

Download the practical exam at this link (Opens at 12pm June 3rd)

Tentative Schedule

There are two lectorials posted online every week:

  • Monday 4-6pm (online)
  • Wednesday 12 - 2pm (online)

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

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
Teaching Pause
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

Harvesting the web with rvest

rvest description of selectorGadget
6 a 2020-04-27 Introduction to modeling, and programming

Intro to Modelling

Modelling Basics
6 b 2020-04-29

Introduction to programming

Pipes (%>%)



7 a 2020-05-04 Intermediate models and programming

Building Models

Many Models
7 b 2020-05-06 none NA
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 Assignment 2
9 b 2020-05-20 None NA
10 a 2020-05-25 Computational modeling, and good data collection practices None NA
10 b 2020-05-27 None NA
11 a 2020-06-01 Guest Lecturer None Project Due
11 b 2020-06-03 Practical Exam None Practical Exam
12 a 2020-06-08 Project Presentation None Projects Presented
12 b 2020-06-10 Final Exam Review None Final Exam Review