Program Overview

Overview

DATA1901 is an advanced level unit that is foundational to the new major in Data Science. The unit focuses on developing critical and statistical thinking skills for all students. 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.

Unit Details and Rules

• Academic unit: Mathematics and Statistics Academic Operations
• Credit points: 6
• Prerequisites: None
• Corequisites: None
• Prohibitions: MATH1005 or MATH1905 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015 or STAT1021
• Assumed knowledge: An ATAR of 95 or more
• Available to study abroad and exchange students: Yes

Teaching Staff

• Coordinator: Diana Warren
• Lecturer(s): Diana Warren

Assessment

• Type:
  • Supervised exam: Final exam testing statistical thinking with given R Output (60%, 2 hours, during formal exam period)
  • Small continuous assessment: Masterclasses, critical review of research masterclasses delivered during Labs (8%, multiple weeks, reflection of c250 words in weeks 3, 6, and 9)
  • Assignment: Project 1, a data project based on given data (0%, due Week 04, pdf of c250 words, 2 x html files)
  • Assignment: Project 2, a data project based on research data (15%, due Week 08, video of 2 minutes, html file of c650 words)
  • Assignment: Project 3, a data project based on client data (15%, due Week 12, html file of c650 words)
  • Participation: Labs, participation in lab classes (2%, weekly, 2 hours/week)

Assessment Summary

• Masterclass: Attend research masterclasses (Sydney Data Stories) as part of Lab classes, and submit a short scholarly reflection on what has been learned.
• Projects: Data projects designed to develop statistical thinking and computational skills, submitted electronically as HTML files.
• Final exam: Compulsory, must be attempted, failure to attempt will result in an AF grade for the course.
• Participation mark: Satisfactory
  on-satisfactory, assessing participation in class activities during labs.

Assessment Criteria

• Result name:
  • High distinction: 85-100, complete or close to complete mastery of the material
  • Distinction: 75-84, excellence, but substantially less than complete mastery
  • Credit: 65-74, creditable performance, beyond routine knowledge and understanding, but less than excellence
  • Pass: 50-64, at least routine knowledge and understanding over a spectrum of topics and important ideas in the unit
  • Fail: 0-49, does not meet the learning outcomes of the unit to a satisfactory standard

Late Submission

• 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 University expects students and staff to act ethically and honestly, and will treat all allegations of academic integrity breaches seriously.
• Use of generative artificial intelligence (AI) and automated writing tools is only permitted if approved by the unit coordinator, and must be acknowledged in the work.

Weekly Schedule

• Week 01: Design of experiments, lecture and tutorial (5 hours), LO1, LO2, LO9, LO10
• Week 02: Data & graphical summaries, lecture and tutorial (5 hours), LO3
• Week 03: Numerical summaries, lecture and tutorial (5 hours), LO3
• Week 04: Normal model, lecture and tutorial (5 hours), LO4
• Week 05: Linear model, lecture and tutorial (5 hours), LO5
• Week 06: Project preparation week, project (5 hours), LO5
• Week 07: Understanding chance, lecture and tutorial (5 hours), LO6
• Week 08: Chance variability (The Box Model), lecture and tutorial (5 hours), LO6
• Week 09: Sample surveys, lecture and tutorial (5 hours), LO6
• Week 10: Hypothesis testing, lecture and tutorial (5 hours), LO7, LO8
• Week 11: Tests for a mean, lecture and tutorial (5 hours), LO7, LO8
• Week 12: Tests for a relationship, lecture and tutorial (5 hours), LO7, LO8

Learning Outcomes

• 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 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.

Graduate Qualities

• GQ1: Depth of disciplinary expertise
• GQ2: Critical thinking and problem solving
• GQ3: Oral and written communication
• GQ4: Information and digital literacy
• GQ5: Inventiveness
• GQ6: Cultural competence
• GQ7: Interdisciplinary effectiveness
• GQ8: Integrated professional, ethical, and personal identity
• GQ9: Influence

Outcome Map

• Learning outcomes are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Responding to Student Feedback

• Changes made to this unit following staff and student reviews include adding a small lab participation mark and reducing quiz weightings.

Additional Information

• Lectures: The Monday Intro Lecture is face-to-face and streamed live, the Friday Revision Lecture is on Zoom.
• Labs: Labs start in week 1.
• Unit material: All learning activities are found in Canvas.
• Work, health, and safety: The University is governed by the Work Health and Safety Act 2011, and everyone has a responsibility for health and safety at work.
• Disclaimer: The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.