Unit outline_

# DATA1901: Foundations of Data Science (Adv)

## 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 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 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

Academic unit Mathematics and Statistics Academic Operations 6 None None MATH1005 or MATH1905 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015 or STAT1021 An ATAR of 95 or more Yes

### Teaching staff

Coordinator Diana Warren, diana.warren@sydney.edu.au Diana Warren Daniel Kim

## Assessment

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 continuous assessment Masterclasses
Produce a critical review of research masterclasses delivered during Labs.
8% Multiple weeks Reflection (c250words, wks 3,6 and 9)
Outcomes assessed:
Assignment Project 1 (group)
Plan a data project, based on research data.
0% Week 04
Due date: 25 Aug 2023 at 23:59

Closing date: 25 Aug 2023
1 page PDF
Outcomes assessed:
Assignment Project 1 (individual)
Reproduce a given ggplot in RStudio.
0% Week 04
Due date: 25 Aug 2023 at 23:59

Closing date: 25 Aug 2023
html file
Outcomes assessed:
Assignment Project 2 (group)
Produce a data project in RStudio, based on research data, and presentation
12% Week 08
Due date: 22 Sep 2023 at 23:59

Closing date: 03 Oct 2023
html file:c650 words;presentation:1.5min
Outcomes assessed:
Assignment Project 2 (individual)
Produce a ggplot in RStudio, using survey data.
3% Week 08
Due date: 22 Sep 2023 at 23:59

Closing date: 03 Oct 2023
html file
Outcomes assessed:
Assignment Project 3
Produce a client report, based on given data.
15% Week 12
Due date: 27 Oct 2023 at 23:59

Closing date: 06 Nov 2023
html file (c650 words)
Outcomes assessed:
Participation Labs
Participation in lab classes
2% Weekly 2 hrs/week
Outcomes assessed:
= group assignment

### Assessment summary

• Masterclass: You will attend research masterclasses (Sydney Data Stories) as part of your Lab classes, and then submit a short scholarly reflection on what you have learnt, through Canvas. Masterclass reflections are eligible for special consideration - simple extension ot if eligible, a longer extension up to 10 days. After the 10 day closing date, you are only eligible for a mark adjustment for the final 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. 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.

• 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 unit.

Fail

0 - 49

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

### 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.

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.

## Weekly schedule

WK Topic Learning activity Learning outcomes
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.

For optional 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.

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.

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

GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

## 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.

• 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.