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Unit of study_

# DATA2002: Data Analytics: Learning from Data

### 2024 unit information

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.

## Unit details and rules

#### Science

Study level Undergraduate Mathematics and Statistics Academic Operations 6
 Prerequisites: ? DATA1X01 or ENVX1002 or BUSS1020 or ECMT1010 or [(MATH1062 or MATH1962 or MATH1972) and (STAT2011 or STAT2911)] or [MATH1X05 and (MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1021 or MATH1023 or MATH1115 or MATH19XX)] None STAT2012 or STAT2912 or DATA2902 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.

## 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
Semester 2 2024
Normal day Camperdown/Darlington, Sydney
Session MoA   Location Outline
Semester 2 2020
Normal day Camperdown/Darlington, Sydney
Semester 2 2021
Normal day Camperdown/Darlington, Sydney
Semester 2 2021
Normal day Remote
Semester 2 2022
Normal day Camperdown/Darlington, Sydney
Semester 2 2022
Normal day Remote
Semester 2 2023
Normal day Camperdown/Darlington, Sydney

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### Modes of attendance (MoA)

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