Skip to main content

During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

MATH1115: Interrogating Data

In a data-rich world, global citizens need to problem solve with data, and evidence based decision-making is essential is every field of research and work. This unit equips you 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, you will experience most of your learning in weekly collaborative 2 hour labs, supplemented by readings and lectures. Working in teams, you will explore three real data stories across different domains, with associated literature. The combination of MATH1005 and MATH1115 is equivalent to DATA1001, allowing you to pathway to the Data Science, Statistics, or Quantitative Life Sciences majors.

Code MATH1115
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

At the completion of this unit, you 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.