• Class Number 5601
  • Term Code 3040
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Priya Dev
    • Dr Priya Dev
  • Class Dates
  • Class Start Date 16/03/2020
  • Class End Date 15/05/2020
  • Census Date 03/04/2020
  • Last Date to Enrol 20/03/2020
SELT Survey Results

This course introduces the principles of data representation, summarisation and presentation with particular emphasis on the use of graphics. The course will use the R Language in a modern computing environment. Topics to be discussed include:

·        Data representation; examples of good and bad graphics; principles of graphic construction; some pitfalls to be avoided; presentation graphics.

·        Graphics environments; interactive graphics; windows; linked windows; graphics objects.

·        Statistical graphics; stem and leaf plots, box plots, histograms; smoothing histograms; quantile-quantile plots; representing multivariate data; scatterplots; clustering; stars and faces; dynamic graphics including data rotation and brushing.

·        Relationships between variables; smoothing scatterplots; simple regression; modelling and diagnostic plots; exploring surfaces; contour plots and perspective plots; multiple regression; relationships in time and space; time series modelling and diagnostic plots.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Demonstrate detailed knowledge of the R statistical computing language, particularly graphical capabilities;
  2. Explain in detail and be able to apply the principles of good data representation;
  3. Explain in detail and be able to use various graphics environments, interactive graphics and graphics objects;
  4. Construct graphical representations of one dimensional data;
  5. Construct graphical representations for multivariate data including scatterplots, and dynamic graphics;
  6. Use diagnostic plots when conducting statistical modelling to explore and refine statistical models for data, including detailed explanations of such use; and
  7. Construct and interpret graphical displays for dependent data.

Research-Led Teaching

This course builds upon the foundation in statistical thinking and evidence-based logic that you have acquired from previous statistics courses. The course contents and activities are designed to help you learn, apply and hone this foundation to prepare yourself for life in the workforce. Course contents and activities heavily involve statistical computing with R interfaced through R Studio. Additional materials (e.g. news reports, journal publications) will be discussed as examples and case studies in which research questions relevant to the course are tackled. 

Required Resources

Computing equipment during non-intensive weeks:

  • You must make your own arrangements to purchase such equipment or to access ANU on-campus computers which are preloaded with course-relevant software R and Rstudio (free to download by clicking the links). More instructions about the software can be found on Wattle.
  • Using a tablet (e.g., iPad, Android tablets) for course-related computing is possible via a cloud computing account. See the Wattle course page for instructions to obtain complimentary NeCTAR cloud computing accounts and course-relevant software. 

Staff Feedback

Feedback from the teaching staff will aim to facilitate the learner's ongoing self assessment of his/her progress in achieving the learning objectives of the course. There are several ways to get feedback from the teaching staff:

(a) to benefit all the students, teaching staff strongly suggests the learners post course-related question publicly on the discussion forum of Wattle so that the teaching staff can reply those questions publicly.

(b) those students who have private things to discuss can email the teaching staff for consultation.

(c) students can also make an appointment by email to schedule a face-to-face consultation on campus or online consultation by Zoom or Skype.

In order to safeguard student privacy, staff members need to be sure that they are dealing with the right student, therefore course- related messages sent from non-ANU email accounts will be ignored.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.

Class Schedule

Week/Session Summary of Activities Assessment
1 Pre-intensive period: Mar 16 - Apr 13 Introduction to R Representing and comparing distributions Relationship between two variables, scatterplot smoothing, simple linear models Students watch recorded videos and self study additional learning materials published on Wattle. Self assessment in R Assignment 1 (40%): available on April 3 due April 14
2 Intensive period: Apr 14 - Apr 17 Daily Lecture + Tutorial: Review Relationship between several variables, multiple linear regression, model diagnostics, variable selection Time dependent data: trend, seasonal and irregular components, autoregressive (AR) and moving-average (MA) models A combination of lectures and tutorials will be held during the intensive week. Refer to Wattle for further information one week before the intensive week. Self assessment. Analyse a data set.
3 Post-intensive period: Apr 18 - May 10 Review Summary of graphical construction Students watch recorded videos and self study additional learning materials published on Wattle. Assignment 2 (60%): available on April 18 due on May 15 (class end date)

Tutorial Registration

Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 40 % 14/04/2020 28/04/2019 1-5
Assignment 2 60 % 15/05/2020 30/05/2018 1-7

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


Students will be required to be on campus in Canberra during the one-week intensive period. Outside of the one-week intensive period, students are expected to use the materials available on Wattle to learn on their own. It is advised to regularly check Wattle for updated information.

Assessment Task 1

Value: 40 %
Due Date: 14/04/2020
Return of Assessment: 28/04/2019
Learning Outcomes: 1-5

Assignment 1

Details: Students are expected to complete the assignment individually. This assignment is designed to cover topics from the pre-intensive period. The assignment will be released on April 7 by 5pm.

Due date: April 14 by 5pm. 

Value: 40%

Assessment Task 2

Value: 60 %
Due Date: 15/05/2020
Return of Assessment: 30/05/2018
Learning Outcomes: 1-7

Assignment 2


Students are expected to complete the assignment individually. Assignment 2 will consist of two parts:

a. (20% = 4*5%)   A collection of four statistical graphics with written comments on each graphic. These graphics should be collected during the semester from published work (eg. journal articles, books, newspapers, websites, social media etc). Credit will be given for interesting graphics that uniquely conveys a message, either poorly or well. In addition to including a copy of the graphic itself, you should document the source of the graphic (title of article, authors, page numbers, website or source link etc.) and discuss the graphic. Your discussion on the graphic should be brief and may include why you chose the graphic, the message it wishes to convey and whether it has achieved this or not - ie the strengths and weaknesses. Your discussion may also include a redrawn or improved version of the graphic.

b. (40%)  This part will focus on a topic that utilises the graphical tools your have learned in the course.

Assignment 2 will be released on April 17 by 5pm.

Due date: May 15 by 5pm. 

Value: 60%

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Each assignment must be submitted as a single electronic file using Turnitin within the course Wattle site. Each of these assessments will require both statistical computing via R Studio and mathematical derivations (either handwritten or typeset using R Studio). If submitting handwritten mathematical derivations, ensure that your handwriting is legible, and scan the derivations (e.g., by using your smartphone camera) to be incorporated into your single electronic file. Prior to submission, you should practice using the Turnitin system here. Students can upload draft versions to the designated Turnitin web link on the Wattle course page, and change those drafts every 24 hours up until the due date.

Submissions outside of the designated Turnitin web link and/or after the due date will be ignored.

Hardcopy Submission

Assessment submission for this course will be online (see above).

Late Submission

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Graded assignments should be available via Turnitin within 14 days after the due date. Written feedback will be published on Wattle.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Resubmission of assessments is not allowed under any circumstance.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Priya Dev
6125 7336

Research Interests

Data science for decentralised financial products and distributed ledger technologies.

Dr Priya Dev

Tuesday By Appointment
By Appointment
Dr Priya Dev
6125 7336

Research Interests

Dr Priya Dev

Tuesday By Appointment
By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions