Program Overview

Overview

DATA1001 is a foundational unit in the Data Science major, focusing on developing critical and statistical thinking skills for all students. The unit explores 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, DATA1001 develops critical thinking and skills to problem-solve with data.

Unit Details and Rules

• Academic unit: Mathematics and Statistics Academic Operations
• Credit points: 6
• Prerequisites: None
• Corequisites: None
• Prohibitions: DATA1901 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021
• Assumed knowledge: None
• Available to study abroad and exchange students: Yes

Teaching Staff

• Coordinator: Diana Warren

Assessment

• Type:
  • Final exam (Record+): 60%, 2 hours, during the formal exam period
  • Assignment:
    • Project 1: 0%, due Week 04, self-directed learning till Week 4
    • Project 2: 15%, due Week 08, self-directed learning till Week 8
    • Project 3: 15%, due Week 12, self-directed learning till Week 12
    • Evaluate Quizzes: 10%, weekly, by the end of each designated week
• Assessment summary:
  • Evaluate Quizzes: randomised quizzes on Canvas, designed to help review learning of the week’s topic, worth 10%, with the best 10 of 11 quizzes counting
  • Projects: designed to develop statistical thinking and computational skills, must be submitted electronically as an HTML file via the DATA1001 Canvas site by the deadline
  • Final exam: 2 hours duration, held on Canvas, with a possible alternative assessment method for a second replacement exam

Assessment Criteria

• The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1)
• Result name:
  • 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 in the course
  • 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, 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 University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously
• Similarity detection software is used to detect potential instances of plagiarism or other forms of academic integrity breach
• Use of generative artificial intelligence (AI) and automated writing tools:
  • Permitted only if specified by the unit coordinator
  • Must be acknowledged in the work, either in a footnote or an acknowledgement section
  • Final submitted work must be original, with any use of AI tools properly referenced

Learning Support

• Simple extensions: not given in first-year units in the School of Mathematics and Statistics
• Special consideration: for exceptional circumstances, longer periods, or essential commitments impacting performance
• Using AI responsibly: resources available on the AI in Education Canvas site

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), LO1, LO2, LO3, LO4, LO5, LO9, LO10
• 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

• At the completion of this unit, you should be able to:
  • LO1: articulate the importance of statistics in a data-rich world
  • 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
  • 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
  • LO7: 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
  • LO10: perform data exploration in a team and communicate findings via oral presentations and reproducible reports

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
• 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

Additional Information

• The University reserves the right to amend units of study or no longer offer certain units
• This unit of study outline was last modified on 17 Jun 2022
• A glossary of common terms used at the University is available online
• The University recognises and pays respect to the Elders and communities of the lands that the University of Sydney's campuses stand on.