Outline

Overview

Technological advances in science, business, and engineering have given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2902 is an intermediate unit in statistics and data sciences, focusing on learning advanced data analytic skills for a wide range of problems and data. How should the Australian government measure and report employment and unemployment? Can we tell the difference between decaffeinated and regular coffee? In this unit, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforcing your programming skills through experience with statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skill to analyse various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	A mark of 65 or above in any of the following (DATA1001 or DATA1901 or ENVX1001 or ENVX1002) or (MATH10X5 and MATH1115) or (MATH10X5 and STAT2011) or STAT2911 or (MATH1905 and MATH1XXX [except MATH1XX5]) or (BUSS1020 or ECMT1010 or STAT1021)
Corequisites ?	None
Prohibitions ?	STAT2012 or STAT2912 or DATA2002
Assumed knowledge ?	Basic linear algebra and some coding for example MATH1014 or MATH1002 or MATH1902 and DATA1001 or DATA1901
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Garth Tarr, garth.tarr@sydney.edu.au
Lecturer(s)	Garth Tarr, garth.tarr@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Final exam	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Report Written report + Shiny app	10%	Week 05 Due date: 23 Sep 2020 at 23:59	5 pages + Shiny app
Outcomes assessed:
Online task	Computer quiz Computer quiz	15%	Week 08 Due date: 22 Oct 2020 at 15:00	1 hour
Outcomes assessed:
Presentation	Group project presentation Oral presentation	10%	Week 11 Due date: 11 Nov 2020 at 23:59	8 minutes
Outcomes assessed:
Assignment	Group project executive summary Executive summary	10%	Week 12 Due date: 18 Nov 2020 at 23:59	3 pages
Outcomes assessed:
Assignment	Report: project reflection Reflection on your group project experience	5%	Week 12 Due date: 20 Nov 2020 at 23:59	1 page
Outcomes assessed:
= group assignment ? = Type C final exam ?

Assessment summary

Report: A statistical analysis of a data set, including exploratory data analysis and hypothesis tests.
Computer quiz: This will be administered during lecture time but you will do it remotely. You will analyse a data set and generate a document using R that will be uploaded to Canvas.
Group project: The group project will consist of a presentation and executive summary. There will also be an individual reflection component.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Module 1: categorical data	Lecture (3 hr)
Week 01	Module 1: categorical data	Computer laboratory (2 hr)
Week 02	Module 1: categorical data	Lecture (3 hr)
Week 02	Module 1: categorical data	Computer laboratory (2 hr)
Week 03	Module 1: categorical data	Lecture (3 hr)
Week 03	Module 1: categorical data	Computer laboratory (2 hr)
Week 04	Module 2: data from case control study	Lecture (3 hr)
Week 04	Module 2: data from case control study	Computer laboratory (2 hr)
Week 05	Module 2: data from case control study	Lecture (3 hr)
Week 05	Module 2: data from case control study	Computer laboratory (2 hr)
Week 06	Module 2: data from case control study	Lecture (3 hr)
Week 06	Module 2: data from case control study	Computer laboratory (2 hr)
Week 07	Module 3: multiple factors comparison	Lecture (3 hr)
Week 07	Module 3: multiple factors comparison	Computer laboratory (2 hr)
Week 08	Module 3: multiple factors comparison	Lecture (3 hr)
Week 08	Module 3: multiple factors comparison	Computer laboratory (2 hr)
Week 09	Module 3: multiple factors comparison	Lecture (3 hr)
Week 09	Module 3: multiple factors comparison	Computer laboratory (2 hr)
Week 10	Module 4: learning and prediction	Lecture (3 hr)
Week 10	Module 4: learning and prediction	Computer laboratory (2 hr)
Week 11	Module 4: learning and prediction	Lecture (3 hr)
Week 11	Module 4: learning and prediction	Computer laboratory (2 hr)
Week 12	Learning and prediction	Lecture (3 hr)
Week 12	Module 4: learning and prediction	Computer laboratory (2 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

An Introduction to Mathematical Statistics and Its Applications by Richard J. Larsen and Morris L. Marx. 5th Ed Chapters 6-14
R Markdown: The Definitive Guide by Yihui Xie, J.J. Allaire, Garrett Grolemund.
Happy Git and GitHub for the useR by Jenny Bryan and the STAT 545 TAs.
Modern Data Science with R by Baumer, Kaplan and Horton. Chapters 2-5, 8-10, Appendix B and E

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. formulate domain/context specific questions and deduce appropriate statistical analysis
LO2. extract and combine data from multiple data resources
LO3. construct, analyse and evaluate numerical and graphical summaries of different data types including large and/or complex data sets
LO4. have developed expertise in the use of a software version control system
LO5. identify, justify and implement an appropriate parametric or non-parametric two sample statistical test
LO6. formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple factors
LO7. demonstrate statistical machine learning using a given classifier and design a cross-validation scheme to calculate the prediction accuracy
LO8. create a reproducible report to communicate outcomes using a programming language.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The unit received generally favourable reviews last year. In response to feedback this year, the number of assessment tasks has been reduced and it will also be made clearer when non-assessable material is presented.

Additional information

Science student portal

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

DATA2902: Data Analytics: Learning from Data (Adv)

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect