Skip to main content
Unit outline_

DATA2002: Data Analytics: Learning from Data

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

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. DATA2002 is an intermediate unit in statistics and data sciences, focusing on learning data analytic skills for a wide range of problems and data. 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 reinforce your programming skills through experience with a statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skills 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
? 
DATA1X01 or ENVX1002 or BUSS1020 or ECMT1010 or [(MATH1062 or MATH1962 or MATH1972) and (STAT2011 or STAT2911)] or [MATH1X05 and (MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1021 or MATH1023 or MATH1115 or MATH19XX)]
Corequisites
? 
None
Prohibitions
? 
STAT2012 or STAT2912 or DATA2902
Assumed knowledge
? 

Successful completion of a first-year or second-year unit in statistics or data science including a substantial coding component. The content from STAT2X11 will help but is not considered essential. Students who are not comfortable using the R software for statistical analysis should familiarise themselves before attempting the unit, e.g. taking OLET1632: Shark Bites and Other Data Stories

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
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO3 LO5 LO6 LO7
Small test Online quizzes
Weekly online quizzes
15% Multiple weeks 1 hour
Outcomes assessed: LO1 LO6 LO5 LO3 LO2
Short release assignment Assignment
Individual assignment
10% Week 06
Due date: 08 Sep 2024 at 23:59

Closing date: 18 Sep 2024
2000 words (guide, not a strict limit)
Outcomes assessed: LO1 LO2 LO3 LO5 LO8
Participation Project: EDA and contract
Individual exploratory data analysis and group contract submission
2% Week 09
Due date: 22 Sep 2024 at 23:59
3 pages
Outcomes assessed: LO1 LO3 LO2
Presentation group assignment Project: presentation
Oral group presentation delivered during your lab in week 12
10% Week 12 20 slides, 20 seconds per slide
Outcomes assessed: LO1 LO2 LO3 LO5 LO6 LO7 LO8
Participation Project: peer review
Provide individual feedback on presentations
3% Week 12 2 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO3
Assignment group assignment Project: report
Written group report
10% Week 13
Due date: 03 Nov 2024 at 23:59
2 pages + optional 1 page appendix
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
group assignment = group assignment ?

Assessment summary

  • Individual assignment: A statistical analysis of a data set, including exploratory data analysis and hypothesis tests.
  • Weekly online quizzes: The best 6 out of 8 quizzes will contribute to your final grade. No simple extensions will be granted and you won't need to apply for special consideration if you miss one or two. You can submit a quiz up to two days late (late penalties apply).
  • Project: The project has four components:
    1. Exploratory data analysis (EDA) and group contract. The EDA is an individual task. The group contract needs to be negotiated collectively within your group. Each student submits a document that has their individual EDA and their common group contract. Submissions are due the Sunday before week 9 and will be discussed and marked by your tutor in labs during week 9.
    2. Presentation. PechaKucha style presentation (20 slides, 20 seconds per slide) done as a group.
    3. Peer review. An individual task. You will be expected to provide constructive feedback for other presentations that you view in your lab. You will not be able to submit the peer review component late.
    4. Report. Following the presentations and peer review you will compose a short report that succinctly describes your analysis and findings. This is done as a group, i.e. one submission per group.
  • Final exam: Supervised exam in the formal exam period. If a second replacement exam is required, this may be conducted 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

None of the tasks are hurdle tasks, this means there is no "double pass" requirement - an overall final mark of 50 is sufficient to pass the unit.

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 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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Assessments (including quizzes) submitted past the due date will receive a deduction of 5% of the maximum mark for each calendar day after the due date. Simple extensions do not apply to any of the assessments, as they are a combination of short-release assignments, group work, in-class, small or continuous assessment.

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.

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

WK Topic Learning activity Learning outcomes
Week 01 Module 1: categorical data Lecture (3 hr) LO1 LO2 LO3
Revising R Computer laboratory (2 hr) LO1 LO2 LO3 LO8
Week 02 Module 1: categorical data Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Module 1: categorical data Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO8
Week 03 Module 1: categorical data Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Module 1: categorical data Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO8
Week 04 Module 2: data from case control study Lecture (3 hr) LO1 LO2 LO3 LO5
Module 1: categorical data Computer laboratory (2 hr) LO1 LO2 LO3 LO5 LO8
Week 05 Module 2: data from case control study Lecture (3 hr) LO1 LO2 LO3 LO5
Module 2: data from case control study Computer laboratory (2 hr) LO1 LO2 LO3 LO5 LO8
Week 06 Module 2: data from case control study Lecture (3 hr) LO1 LO2 LO3 LO5 LO6
Module 2: data from case control study Computer laboratory (2 hr) LO1 LO2 LO3 LO5 LO8
Week 07 Module 3: multiple factors comparison Lecture (3 hr) LO1 LO2 LO3 LO6
Module 2: data from case control study Computer laboratory (2 hr) LO1 LO2 LO3 LO6 LO8
Week 08 Module 3: multiple factors comparison Lecture (3 hr) LO1 LO2 LO3 LO6
Module 3: multiple factors comparison Computer laboratory (2 hr) LO1 LO2 LO3 LO6 LO8
Week 09 Module 3: multiple factors comparison Lecture (3 hr) LO1 LO2 LO3 LO6 LO7
Module 3: multiple factors comparison Computer laboratory (2 hr) LO1 LO2 LO3 LO6 LO7 LO8
Week 10 Module 4: learning and prediction Lecture (3 hr) LO1 LO2 LO3 LO7
Module 3: multiple factors comparison Computer laboratory (2 hr) LO1 LO2 LO3 LO7 LO8
Week 11 Module 4: learning and prediction Lecture (3 hr) LO1 LO2 LO3
Module 4: learning and prediction Computer laboratory (2 hr) LO1 LO2 LO3 LO7 LO8
Week 12 Module 4: learning and prediction Lecture (3 hr) LO1 LO2 LO3 LO7
Module 4: learning and prediction Computer laboratory (2 hr) LO1 LO2 LO3 LO7 LO8
Week 13 Revision Lecture (3 hr) LO1 LO2 LO3 LO5 LO6 LO7
Module 4: learning and prediction Computer laboratory (2 hr) LO1 LO2 LO3 LO7 LO8

Attendance and class requirements

Content will be delivered using a mix of pre-recorded videos and a weekly 2 hour in-person lecture. Each week you are expected to watch the pre-recorded video(s) before attending the lecture. The in-person lecture will also be recorded and available for viewing on Canvas within a few hours.

Lab attendance does not contribute directly to your final mark. However, the labs are an extremely important component of the unit and where a lot of the learning happens. Your tutor will record attendance in labs as this helps us understand engagement patterns across semester and identify students who might be at risk of failing.

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

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. formulate domain/context specific questions and identify appropriate statistical analysis
  • LO2. extract and combine data from multiple data resources
  • LO3. construct, interpret and compare numerical and graphical summaries of different data types including large and/or complex data sets
  • LO4. have developed familiarity with the use of a software version control system
  • LO5. identify, justify and implement appropriate parametric or non-parametric statistical tests
  • LO6. formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple factors
  • LO7. perform statistical machine learning using a given classifier and create 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

The unit receives generally favourable reviews. In 2024 the way the lectures are delivered has shifted to a hybrid approach using a combination of pre-recorded content to be viewed in your own time before a 2 hour in person lecture block. Previously it was three one hour in-person lectures across three different days each week.

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.