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Unit of study_
DATA1901: Foundations of Data Science (Adv)
2025 unit information
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
Managing faculty or University school:
Science
| Study level | Undergraduate |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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None |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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MATH1005 or MATH1905 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or MATH1115 or MATH1015 or STAT1021 |
| Assumed knowledge:
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Year 10 Mathematics or equivalent; An ATAR of 95 or more |
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.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2024
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Normal day | Camperdown/Darlington, Sydney | |
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Semester 2 2024
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Normal day | Camperdown/Darlington, Sydney |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2025
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Semester 2 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
