Outline

Overview

DATA1901 is an advanced level unit (matching DATA1001) that is foundational to the new major in Data Science. The unit focuses on developing critical and statistical thinking skills for all students. Does mobile phone usage increase the incidence of brain tumours? What is the public's attitude to shark baiting following a fatal attack? Statistics is the science of decision making, essential in every industry and undergirds all research which relies on data. Students will use problems and data from the physical, health, life and social sciences to develop adaptive problem solving skills in a team setting. Taught interactively with embedded technology and masterclasses, DATA1901 develops critical thinking and skills to problem-solve with data at an advanced level. By completing this unit you will have an excellent foundation for pursuing data science, whether directly through the data science major, or indirectly in whatever field you major in. The advanced unit has the same overall concepts as the regular unit but material is discussed in a manner that offers a greater level of challenge and academic rigour.

Unit details and rules

Unit code	DATA1901
Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prohibitions ?	MATH1905 or ECMT1010 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	An ATAR of 95 or more
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Diana Warren, diana.warren@sydney.edu.au
Lecturer(s)	Diana Warren, diana.warren@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Written Exam testing statistical thinking with given R Output.	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Masterclasses Critical review of 4 research masterclasses.	10%	Multiple weeks	Reflection following a 1 hour class.
Outcomes assessed:
Assignment	Project 1 A data project based on given data: written report + peer review	0%	Week 04 Due date: 18 Sep 2020 at 23:59	Self-directed learning till Week 4.
Outcomes assessed:
Assignment	Project 2 A data project based on research data: written + verbal report	15%	Week 08 Due date: 23 Oct 2020 at 23:59	Self-directed learning till Week 8.
Outcomes assessed:
Assignment	Project 3 A data project based on client data: written + verbal report	15%	Week 12 Due date: 20 Nov 2020 at 23:59	Self-directed learning till Week 12.
Outcomes assessed:
Assignment	LQuizzes & RQuizzes To review learning of lectures and R.	0%	Weekly	By the end of each designated week.
Outcomes assessed:
= group assignment ? = Type C final exam ?

Assessment summary

Masterclasss: You will attend 4 research masterclasses (Sydney Data Stories) and then submit a short scholarly reflection on what you have learnt, through Canvas.
Projects: The data projects are designed to develop your statistical literacy and computational ability. They must be submitted electronically, as an HTML file via the DATA1901 Canvas site by the deadline. Late submissions will receive a penalty.
LQuizzes & RQuizzes: The LQuizzes and RQuizzes are randomised quizzes on Canvas. They are designed to help you review your learning of lectures and R. They are formative (i.e for learning, but don’t count for your final mark).
Examination: There is an examination of 2 hours duration held 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 unit.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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
Multiple weeks	Masterclasses: Weeks 3,6,9,12	Lecture (4 hr)
Week 01	Design of experiments	Lecture (3 hr)
Week 01	Lab1	Computer laboratory (2 hr)
Week 02	Data & graphical summaries	Lecture (3 hr)
Week 02	Lab 2	Computer laboratory (2 hr)
Week 03	Numerical summaries	Lecture (3 hr)
Week 03	Lab 3	Computer laboratory (2 hr)
Week 04	Normal model	Lecture (3 hr)
Week 04	Lab 4	Computer laboratory (2 hr)
Week 05	Linear model	Lecture (3 hr)
Week 05	Lab 5	Computer laboratory (2 hr)
Week 06	Linear model	Lecture (3 hr)
Week 06	Lab 6	Computer laboratory (2 hr)
Week 07	Understanding chance	Lecture (3 hr)
Week 07	Lab 7	Computer laboratory (2 hr)
Week 08	Chance variability	Lecture (3 hr)
Week 08	Lab 8	Computer laboratory (2 hr)
Week 09	Sample surveys	Lecture (3 hr)
Week 09	Lab 9	Computer laboratory (2 hr)
Week 10	Hypothesis testing	Lecture (3 hr)
Week 10	Lab 10	Computer laboratory (2 hr)
Week 11	Tests for a mean	Lecture (3 hr)
Week 11	Lab 11	Computer laboratory (2 hr)
Week 12	Tests for a relationship	Lecture (3 hr)
Week 12	Lab 12	Computer laboratory (2 hr)
Week 13	Revision	Lecture (3 hr)

Attendance and class requirements

Specific Information on DATA1901 Classes:

All lectures and lab demos are pre-recorded, for you to work through at your own pace, supported by a live Zoom Revision class at the end of each week.

You will revise your learning through Lab Exercises, LQuizzes and RQuizzes, completed at your own pace.

You will not have a separate Lab class. Rather, there will be Drop in Support, available on 3 channels: Ed, Zoom and on Campus.

The 4 Masterclasses will either be live on Zoom or pre-recordings.

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

For extra reading: Statistics (4th Edition), Freedman, Pisani, and Purves (2007). An e-text version is available.

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. assess the importance of statistics in a data-rich world, including current challenges such as ethics, privacy and big data
LO2. analyse the study design behind a dataset, seeing additional evidence from literature, and evaluate how the study design affects context specific outcomes
LO3. design, produce, interpret and compare graphical and numerical summaries of data from multiple sources in R, using the use of interactive tools
LO4. apply the Normal approximation to data, with consideration of measurement error
LO5. model the relationship between 2 variables using linear regression, and explain linear regression in terms of projection
LO6. use the box model to describe chance and chance variability, including sample surveys and the central limit theorem
LO7. formulate an appropriate hypothesis and perform a range of hypothesis tests on given real multivariate data and a problem
LO8. interpret the p-value, conscious of the various pitfalls associated with testing
LO9. critique the use of statistics in media and research papers in a wide variety of data contexts, with attention to confounding and bias
LO10. perform data analysis in a team, on data requiring multiple preprocessing steps, and communicate the findings via oral and written reproducible reports, with extensive interrogation.

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.

No changes have been made since this unit was last offered.

Additional information

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

Attendance and class requirements

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

DATA1901: Foundations of Data Science (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

Attendance and class requirements

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