• Class Number 3543
• Term Code 3240
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Priya Dev
• LECTURER
• Dr Priya Dev
• Class Dates
• Class Start Date 14/03/2022
• Class End Date 13/05/2022
• Census Date 01/04/2022
• Last Date to Enrol 01/04/2022
SELT Survey Results

Graphical Data Analysis (STAT7026)

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 14 - Apr 10 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. Campus attendance is not required. Self assessment in R Assignment 1 (40%): available on April 1 2022 due April 11 2022
2 Intensive period: Apr 11 - Apr 14 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 Campus attendance is recommended but not mandatory during the intensive period. Some students cannot be in Canberra, others have work obligations or health concerns. If you are one of these students, it is possible to complete the intensive period online. Students will work in groups to analyse data sets and complete tasks. The lecturer will be available in the allocated computer labs to summarise video lectures, discuss topics and assist with questions. Students may work with their group remotely or on campus in the allocated computer lab with the lecturer. If working remotely, each group will have to book an online appointment with the lecturer to discuss progress and ask questions. The intensive period lab sessions or online group discussions won't be recorded. All lectures and tutorials are pre-recorded and available online so the focus of the intensive period is to summarise key concepts and identify gaps that need to be focussed on. Self learn video materials. Summarise video learnings & analyse data sets, either in the lab, or with groups remotely if you cannot be on campus.
3 Post-intensive period: Apr 15 - May 13 Review Summary of graphical construction Students watch recorded videos and self study additional learning materials published on Wattle. Campus attendance is not required. Assignment 2 (60%): available on April 14 2022 due on May 15 2022 (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 % 11/04/2022 25/04/2022 1-5
Assignment 2 60 % 15/05/2022 31/05/2022 1-7

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

## Policies

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 Academic Integrity . 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.

## Participation

Students are recommended to be on campus in Canberra during the one-week intensive period but this will not be mandatory. 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. The teaching format will be regular in-person classroom discussion labs during the intensive period. Remote students may be able to schedule check-ins during this week via Zoom. The labs will not be recorded.

Value: 40 %
Due Date: 11/04/2022
Return of Assessment: 25/04/2022
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 Friday April 1 by 5pm. An email will be sent via Wattle as soon as the assignment is released on Wattle.

Due date: Monday April 11 by 9am prior to the commencement of the intensive period.

Value: 40%

Value: 60 %
Due Date: 15/05/2022
Return of Assessment: 31/05/2022
Learning Outcomes: 1-7

Assignment 2

Details:

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

Part A. (20% = 4*5%) Communication Graphics:  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 convey a statistical 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 clearly communicate your analysis of the graphic. Your analysis should synthesise why you chose the graphic, with the statistical message it wishes to convey; how it has effectively communicated this statistical message or not - ie the graphics strengths, weaknesses and how the audience is likely to interpret the graphic. Your discussion may also include a redrawn or improved version of the graphic or a summary of how it should have been constructed so that the audience interprets the statistical message correctly.

Part B. (40%) Analysis Graphics: This part will focus on a topic that utilises the graphical tools you have learned in the course to analyse data.

Assignment 2 will be released on April 14 by 5pm.

Due date: May 15 by 11:59pm.

Value: 60%

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.

## 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.
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.

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).

## Convener

 Dr Priya Dev 6125 7336 priya.dev@anu.edu.au

### Research Interests

Financial technology for Environmental, Social, Governance (ESG) risk & decentralised financial products; Data Analysis techniques of financial time series.

### Dr Priya Dev

 By Appointment By Appointment

## Instructor

 Dr Priya Dev 6125 7336 priya.dev@anu.edu.au

### Dr Priya Dev

 By Appointment By Appointment