Program Overview

Overview

In a data-rich world, global citizens need to problem solve with data, and evidence-based decision-making is essential in every field of research and work. This unit equips students with foundational statistical thinking to interrogate data. Focusing on statistical literacy, the unit covers foundational statistical concepts such as visualising data, the linear regression model, and testing significance using the t and chi-square tests. Based on a flipped learning approach, students will experience most of their learning in weekly collaborative 2-hour labs, supplemented by readings and lectures. Working in teams, students will explore three real data stories across different domains, with associated literature. The combination of MATH1005 and MATH1115 is equivalent to DATA1001, allowing students to pathway to the Data Science, Statistics, or Quantitative Life Sciences majors.

Unit Details and Rules

• Academic unit: Mathematics and Statistics Academic Operations
• Credit points: 3
• Prerequisites: MATH1005 or MATH1015
• Corequisites: None
• Prohibitions: STAT1021 or ENVX1001 or ENVX1002 or BUSS1020 or ECMT1010 or DATA1001 or DATA1901
• Assumed knowledge: None
• Available to study abroad and exchange students: Yes

Teaching Staff

• Coordinator: Diana Warren
• Tutor(s): Januar Harianto

Assessment

• Type: Final exam (Take-home short release)
  • Description: Computer Prac Exam. For a given dataset with context, write a statistical report in RStudio.
  • Weight: 65%
  • Due: Formal exam period
  • Length: 1.5 hours
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
• Type: Presentation
  • Description: Project 1 report + presentation. A data project, demonstrating ggplot, for own choice of data.
  • Weight: 10%
  • Due: Week 05, Due date: 05 Sep 2021 at 23:59
  • Outcomes assessed: LO1 LO2 LO3 LO6
• Type: Assignment
  • Description: Project 1 interrogation. Code checking and review of another group's Project 1.
  • Weight: 5%
  • Due: Week 06, Due date: 17 Sep 2021 at 23:59
  • Outcomes assessed: LO1 LO2 LO3 LO6
• Type: Presentation
  • Description: Project 2 report + presentation. Data project, showing synthesis of course material, based on client data.
  • Weight: 10%
  • Due: Week 10, Due date: 17 Oct 2021 at 23:59
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
• Type: Assignment
  • Description: Project 2 interrogation. Code checking and review of another student's Project 2.
  • Weight: 5%
  • Due: Week 11, Due date: 29 Oct 2021 at 23:59
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
• Type: Assignment
  • Description: LQuiz. The LQuizzes allow weekly revision of RGuide and Course Material.
  • Weight: 5%
  • Due: Weekly, Due end of each week
  • Outcomes assessed: LO2 LO3 LO4 LO5

Assessment Summary

• LQuizzes: The LQuizzes are designed to help students interact with the readings, in preparation for each lab. The LQuizzes will be held on the MATH1115 Canvas site. Each LQuiz consists of 5 randomised questions. The better mark principle will be used for the total marks on the LQuizzes so do not submit an application for Special Consideration or Special Arrangements if you miss a quiz.
• Projects: The data projects are designed to develop students' statistical literacy and computational ability. They must be submitted electronically as an HTML file via the MATH1115 Canvas site by the deadline. Late submissions will receive a penalty.
• Examination: There is one prac examination of 1.5 hours’ duration during the examination period.

Assessment Criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

• Result name: High distinction
  • Mark range: 85 - 100
  • Description: Representing complete or close to complete mastery of the material.
• Result name: Distinction
  • Mark range: 75 - 84
  • Description: Representing excellence, but substantially less than complete mastery.
• Result name: Credit
  • Mark range: 65 - 74
  • Description: Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
• Result name: Pass
  • Mark range: 50 - 64
  • Description: Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
• Result name: Fail
  • Mark range: 0 - 49
  • Description: When students 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.

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.

Weekly Schedule

Learning Outcomes

At the completion of this unit, students should be able to:

• LO1: Interrogate data in a team and communicate findings to diverse audiences through reproducible written and oral reports.
• LO2: Explain the complexities of data wrangling.
• LO3: Produce, interpret and compare graphical and numerical summaries, using ggplot.
• LO4: Examine the relationships between variables using correlation and visualisation, and justify whether regression is an appropriate model for the data.
• LO5: Formulate an appropriate hypothesis and perform a range, on a given real multivariate data and a problem, of hypothesis tests.
• LO6: Investigate a real data story by researching associated literature, both in media and research journals.

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 students 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.