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Unit outline_

DATA2902: Data Analytics: Learning from Data (Adv)

Semester 2, 2021 [Normal day] - Remote

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 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 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
? 
6 cp of DATA1901 or STAT2911 or (MATH1905 and MATH1XXX) or a mark of 65 or above in (DATA1001 or ENVX1001 or ENVX1002 or BUSS1020 or ECMT1010 or STAT1021 or STAT2011) or an average mark of 65 or above in (MATH10X5 and MATH1115)
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
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO3 LO5 LO6 LO7
Online task Quiz 1
Online quiz
5% Week 04
Due date: 03 Sep 2021 at 23:59
40 mins
Outcomes assessed: LO1 LO2 LO3 LO5
Assignment Report
Written report + Shiny app
10% Week 06
Due date: 19 Sep 2021 at 23:59
5 pages + Shiny app
Outcomes assessed: LO1 LO2 LO3 LO5 LO7 LO8
Online task Quiz 2
Online quiz
5% Week 07
Due date: 24 Sep 2021 at 23:59
40 mins
Outcomes assessed: LO1 LO5 LO3 LO2
Online task Quiz 3
Online quiz
5% Week 10
Due date: 22 Oct 2021 at 23:59
40 mins
Outcomes assessed: LO1 LO6 LO5 LO3 LO2
Presentation group assignment Project: presentation
Oral presentation
10% Week 11
Due date: 31 Oct 2021 at 23:59
8 minutes
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Participation Project: peer review
Provide feedback on presentations
3% Week 12
Due date: 04 Nov 2021 at 23:59
1 hour
Outcomes assessed: LO1 LO6 LO5 LO3 LO2
Assignment group assignment Project: report
Written report and shiny app
10% Week 13
Due date: 12 Nov 2021 at 23:59
2 pages + 1 page appendix + shiny app
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Project: reflection
Reflection on your group project experience
2% Week 13
Due date: 14 Nov 2021 at 23:59
1 page
Outcomes assessed: LO1 LO4 LO6 LO7
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assessment summary

  • Report: A statistical analysis of a data set, including exploratory data analysis, hypothesis tests and a shiny app.
  • Online quizzes: These will be administered through Canvas.
  • Group project: The group project will consist of a presentation, written report and shiny app. You will be provided with feedback on the presentation before the written report and shiny app are due. There will also be an individual reflection component and a peer review 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.

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 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 LO7
Module 4: learning and prediction Computer laboratory (2 hr) LO1 LO2 LO3 LO7 LO8
Week 12 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

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 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 appropriate parametric or non-parametric statistical tests
  • 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

The unit received generally favourable reviews last year. The mid-semester test has been split up into three shorter quizzes.

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.