Outline

Overview

DATA1001 is a foundational unit in the Data Science major. 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 that 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, DATA1001 develops critical thinking and skills to problem-solve with data. It is the prerequisite for DATA2002.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	DATA1901 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021
Assumed knowledge ?	None
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
Lecturer(s)	Cosette Saunders, cosette.saunders@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Exam testing statistical thinking with given R Output.	60%	Formal exam period	2 hours
Outcomes assessed:
Small test	Early Feedback Task (Evaluate Quiz 3) Review the week's topic.	1%	Week -03 Due date: 10 Mar 2024 at 23:59 Closing date: 10 Mar 2024	30mins (week 3)
Outcomes assessed:
Assignment	Project 1 (group) Plan a data project, based on collecting survey data.	0%	Week 04 Due date: 15 Mar 2024 at 23:59 Closing date: 15 Mar 2024	1 page PDF
Outcomes assessed:
Assignment	Project 1 (individual) Reproduce a given ggplot in RStudio.	0%	Week 04 Due date: 15 Mar 2024 at 23:59 Closing date: 15 Mar 2024	html file
Outcomes assessed:
Assignment	Project 2 (group) Produce a data report in RStudio, based on survey data, and presentation.	13%	Week 08 Due date: 19 Apr 2024 at 23:59 Closing date: 29 Apr 2024	html file:c650words presentation:3mins
Outcomes assessed:
Assignment	Project 2 (individual) Produce a ggplot in RStudio using survey data.	2%	Week 08 Due date: 19 Apr 2024 at 23:59 Closing date: 29 Apr 2024	html file
Outcomes assessed:
Assignment	Project 3 Produce a client report, based on given data.	15%	Week 12 Due date: 17 May 2024 at 23:59 Closing date: 27 May 2024	html file: c650 words
Outcomes assessed:
Small test	Evaluate Quizzes Review the week's topic.	7%	Weekly	30mins (weeks 1-2,4-8,10-12)
Outcomes assessed:
Participation	Labs Participation in lab classes	2%	Weekly	2hrs/week
Outcomes assessed:
= group assignment ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

The following information to to help you navigate what tasks are eligible for special considerations.

Evaluate Quizzes: The Evaluate Quizzes are randomised quizzes on Canvas, which help you to review each week’s topic. They are worth 8%, and due at 23:59pm each Sunday night in weeks 1-8 and 10-12 (unless there's a public holiday , when it is due the next work day). Quiz 3 is designated as the 'Early Feedback task'. Once started, the Quizzes cannot be re-set. The Early Feedback Task. will count, plus the best 7 of the remaining 10 Quizzes. If you miss a Quiz, it is not eligible for special consideration. Instead, the better mark principle will be used for the total marks on the Quizzes, towards the exam. This means that the total Quiz mark counts if and only if it is better than or equal to 8% of your exam mark. If your total Quiz mark is less than 8% of your exam mark, then 8% of your exam mark will be used instead. This allows you to improve in the exam.
Projects: The data projects are designed to develop your statistical thinking and computational skills. They must be submitted electronically, via the DATA1001 Canvas site by the deadline. Late penalities apply. It is your responsibility to check that your project has been submitted correctly, otherwise it will not be marked.
- Project 1 (group & individual) are not eligible for special consideration, as they are formative tasks.
- Project 2 (group) is not eligible for special consideration, as it involves group work.
- Project 2 (individual) and Project 3 are eligible for special consideration. You can be granted a simple extension, or if eligible, a longer extension up to a maximum of 10 days. If you do not submit by the approved deadline, you will need to re-apply for a special consideration. No submissions can be made more than 10 days past the original date - any further special considerations can only be granted a mark adjustment for the final exam.
- The better mark principle will be used for Project 2, towards Project 3. This means that the Project 2 mark counts if and only if it is better than or equal to your Project 3 mark. If your Project 2 mark is less than your Project 3 mark, then your Project 3 mark will replace your Project 2 mark. This will not happen if your Project 2 mark is less than 50%.
Participation mark: This is a satisfactory/non-satisfactory mark assessing whether or not you participate in class activities during the labs. It is 0.25 marks per lab class up to 8 labs (there are 12 labs). You can apply for a special consideration - if eligible, it will be a mark adjustment for the final exam.
Final exam: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Design of experiments	Lecture and tutorial (5 hr)
Week 02	Data & graphical summaries	Lecture and tutorial (5 hr)
Week 03	Numerical summaries	Lecture and tutorial (5 hr)
Week 04	Normal model	Lecture and tutorial (5 hr)
Week 05	Linear model	Lecture and tutorial (5 hr)
Week 06	Understanding chance	Lecture and tutorial (5 hr)
Week 07	Chance variability (The Box Model)	Lecture and tutorial (5 hr)
Week 08	Sample surveys	Lecture and tutorial (5 hr)
Week 09	Project Week	Project (5 hr)
Week 10	Hypothesis testing	Lecture and tutorial (5 hr)
Week 11	Tests for a mean	Lecture and tutorial (5 hr)
Week 12	Tests for a relationship	Lecture and tutorial (5 hr)

Attendance and class requirements

Lecture attendance: You are expected to attend lectures, either face-face or livestream, or by catching up, in a timely manner, through the recordings in Canvas.
Lab attendance: Labs (one x 2 hours per week) start in Week 1. You must attend the Lab given on your personal timetable. Attendance at labs and participation will be recorded to determine the participation mark. Your attendance will not be recorded unless you attend the Lab in which you are enrolled. We strongly recommend you attend Labs regularly to keep up with the material and to engage with the Lab questions.

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 optional extra reading, see 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. articulate the importance of statistics in a data-rich world, including current challenges such as ethics, privacy and big data
LO2. identify the study design behind a dataset and how the study design affects context specific outcomes
LO3. produce, interpret and compare graphical and numerical summaries, using base R and ggplot
LO4. apply the normal approximation to data, with consideration of measurement error
LO5. model and explain the relationship between 2 variables using linear regression
LO6. use the box model to describe chance and chance variability, including sample surveys and the central limit theorem
LO7. given real multivariate data and a problem, formulate an appropriate hypothesis and perform a range of hypothesis tests
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 exploration in a team, and communicate the findings via oral presentations and reproducible reports, with 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.

Small lab participation mark added with a consequent reduction in the quiz weightings. No changes have been made since this unit was last offered.

Additional information

Lectures: The Monday Intro Lecture is face-face and streamed live. The Friday Revision Lecture is on Zoom, as it involves demonstration of computation. Links are found in Canvas.
Labs: Labs start in week 1.
Unit material: All learning activities are found in Canvas.
Ed Discussion Board: https://edstem.org

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

Early feedback task

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

DATA1001: Foundations of Data Science

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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