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Unit of study_

DATA1001: Foundations of Data Science

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

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 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, DATA1001 develops critical thinking and skills to problem-solve with data. It is the prerequisite for DATA2002.

Unit details and rules

Unit code DATA1001
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
DATA1901 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Diana Warren,
Lecturer(s) Diana Warren,
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Written Exam testing statistical thinking with given R Output.
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
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: LO1 LO2 LO3 LO9
Assignment group assignment Project 2
A data project based on own survey data: written + verbal report
15% Week 08
Due date: 23 Oct 2020 at 23:59
Self-directed learning till Week 8.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO9 LO10
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: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Assignment LQuizzes
To review learning in lectures.
0% Weekly By the end of each designated week.
Outcomes assessed: LO1 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment RQuizzes
To review learning of R and lectures.
10% Weekly By the end of each designated week.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
group assignment = group assignment ?
Type C final exam = Type C final exam ?

Assessment summary

  • LQuizzes: The LQuizzes are randomised quizzes on Canvas. They are designed to help you review your learning of lectures. They are formative (worth 0%).
  • RQuizzes: The RQuizzes are randomised quizzes on Canvas. They are designed to help you review your learning of R and lectures. The best 10 of your 12 RQuizzes will count, making each worth 1%. The better mark principle will be used for the total marks on the RQuizzes so do not submit an application for Special Consideration or Special Arrangements if you miss an RQuiz. The better mark principle means that the total quiz mark counts if and only if it is better than or equal to 10% of your exam mark. If your total quiz mark is less than 10% of your exam mark, then 10% 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, as an HTML file via the DATA1001 Canvas site by the deadline. It is your responsibility to check that your project has been submitted correctly, otherwise it will not be marked. The better mark principle does not apply to the projects, as they assess different learning outcomes to the final exam. Late submissions will receive a penalty.
  • Final exam: 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


High distinction

85 - 100

Representing complete or close to complete mastery of the material.


75 - 84

Representing excellence, but substantially less than complete mastery.


65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.


50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Design of experiments Lecture (3 hr) LO1 LO2 LO9 LO10
Lab 1 Computer laboratory (2 hr) LO1 LO9
Week 02 Data & graphical summaries Lecture (3 hr) LO3
Lab 2 Computer laboratory (2 hr) LO2 LO3
Week 03 Numerical summaries Lecture (3 hr) LO3
Lab 3 Computer laboratory (2 hr) LO3
Week 04 Normal model Lecture (3 hr) LO4
Lab 4 Computer laboratory (2 hr) LO3
Week 05 Linear model Lecture (3 hr) LO5
Lab 5 Computer laboratory (2 hr) LO1 LO2 LO3 LO9 LO10
Week 06 Linear model Lecture (3 hr) LO5
Lab 6 Computer laboratory (2 hr) LO4
Week 07 Understanding chance Lecture (3 hr) LO6
Lab 7 Computer laboratory (2 hr) LO5
Week 08 Chance variability Lecture (3 hr) LO6
Lab 8 Computer laboratory (2 hr) LO6
Week 09 Sample surveys Lecture (3 hr) LO6
Lab 9 Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO9 LO10
Week 10 Hypothesis testing Lecture (3 hr) LO7 LO8
Lab 10 Computer laboratory (2 hr) LO6
Week 11 Tests for a mean Lecture (3 hr) LO7 LO8
Lab 11 Computer laboratory (2 hr) LO7 LO8
Week 12 Tests for a relationship Lecture (3 hr) LO7 LO8
Lab 12 Computer laboratory (2 hr) LO7 LO8

Attendance and class requirements

Specific Information on DATA1001 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.

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, see Statistics (4th Edition) – Freedman, Pisani, and Purves (2007). An e-text version is available.

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.

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

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

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

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.


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.