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Unit of study_

DATA2002: Data Analytics: Learning from Data

Technological advances in science, business and engineering have given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2002 is an intermediate unit in statistics and data sciences, focusing on learning data analytic skills for a wide range of problems and data. In this unit, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforce your programming skills through experience with a statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skills to analyse various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.

Code DATA2002
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites:
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DATA1X01 or ENVX1002 or [MATH1X05 and MATH1XXX (excluding MATH1X05)] or BUSS1020 or ECMT1010
Corequisites:
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None
Prohibitions:
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STAT2012 or STAT2912 or DATA2902
Assumed knowledge:
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Successful completion of a first-year or second-year unit in statistics or data science including a substantial coding component. The content from STAT2X11 will help but is not considered essential. Students who are not comfortable using the R software for statistical analysis should familiarise themselves before attempting the unit, e.g. taking OLET1632: Shark Bites and Other Data Stories

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

  • LO1. formulate domain/context specific questions and identify appropriate statistical analysis
  • LO2. extract and combine data from multiple data resources
  • LO3. construct, interpret and compare numerical and graphical summaries of different data types including large and/or complex data sets
  • LO4. have developed familiarity with the use of a software version control system
  • LO5. identify, justify and implement appropriate parametric or non-parametric statistical tests
  • LO6. formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple factors
  • LO7. perform statistical machine learning using a given classifier and create a cross-validation scheme to calculate the prediction accuracy
  • LO8. create a reproducible report to communicate outcomes using a programming language.