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SMA212 - Data Analytics

2024

Sector

Higher Education

Discipline

Engineering

Credit Points

Equivalent Units

(SMA211)

Prerequisites

Incompatible

Assumed Knowledge

Basic knowledge of statistics in High School.

Unit Description

The field of data analytics systematically analyses data to draw conclusions about the information contained in the data. Topics covered include: descriptive and inferential statistics for data analytics, data pre-processing (data mining), data clustering, frequent pattern mining of big data, regression, classification algorithms, data ownership and privacy, and ethical issues. Students will develop the ability to statistically compare large sets of data as well as to develop models using regression analysis techniques. Students will also develop knowledge around the ethical impact of using data analytics to process large sets of data.

Unit Learning Outcomes

1.	Apply mathematical statistics, including concepts of probability, random variables, standard distributions, transformations, sampling distributions, point and interval estimations, and hypothesis testing, to solve science, engineering or business related problems.
2.	Apply appropriate techniques of data visualisation, data pre-processing, clustering, frequent pattern mining for small and large data sets to solve a range of scientific, engineering or business-related data analytic problems.
3.	Develop and critically evaluate empirical models from data using regression techniques.
4.	Apply classification algorithms to develop classification models for a given data set.
5.	Critically evaluate data ownership, privacy and ethical issues related to data analytics.
6.	Apply effective communication methods including assignment and practical reports, to convey ideas and principles.

Assessment Overview

	Assessment Task	Value (of total mark)	Related Learning Outcome/s
1.	Project proposal for data analytics group project	5%	1 - Apply mathematical statistics, including concepts of probability, random variables, standard distributions, transformations, sampling distributions, point and interval estimations, and hypothesis testing, to solve science, engineering or business related problems. 2 - Apply appropriate techniques of data visualisation, data pre-processing, clustering, frequent pattern mining for small and large data sets to solve a range of scientific, engineering or business-related data analytic problems. 3 - Develop and critically evaluate empirical models from data using regression techniques. 4 - Apply classification algorithms to develop classification models for a given data set.
2.	Online Test 1 (60 minutes)	30%	1 - Apply mathematical statistics, including concepts of probability, random variables, standard distributions, transformations, sampling distributions, point and interval estimations, and hypothesis testing, to solve science, engineering or business related problems. 2 - Apply appropriate techniques of data visualisation, data pre-processing, clustering, frequent pattern mining for small and large data sets to solve a range of scientific, engineering or business-related data analytic problems. 3 - Develop and critically evaluate empirical models from data using regression techniques.
3.	Online Test 2 (60 minutes)	30%	SMA212 1 - Apply mathematical statistics, including concepts of probability, random variables, standard distributions, transformations, sampling distributions, point and interval estimations, and hypothesis testing, to solve science, engineering or business related problems. 2 - Apply appropriate techniques of data visualisation, data pre-processing, clustering, frequent pattern mining for small and large data sets to solve a range of scientific, engineering or business-related data analytic problems. 3 - Develop and critically evaluate empirical models from data using regression techniques. 4 - Apply classification algorithms to develop classification models for a given data set. 5 - Critically evaluate data ownership, privacy and ethical issues related to data analytics. 6 - Apply effective communication methods including assignment and practical reports, to convey ideas and principles.
4.	Data Analytics Group Project Report (20-25 pages)	35%	SMA212 1 - Apply mathematical statistics, including concepts of probability, random variables, standard distributions, transformations, sampling distributions, point and interval estimations, and hypothesis testing, to solve science, engineering or business related problems. 2 - Apply appropriate techniques of data visualisation, data pre-processing, clustering, frequent pattern mining for small and large data sets to solve a range of scientific, engineering or business-related data analytic problems. 3 - Develop and critically evaluate empirical models from data using regression techniques. 4 - Apply classification algorithms to develop classification models for a given data set. 5 - Critically evaluate data ownership, privacy and ethical issues related to data analytics. 6 - Apply effective communication methods including assignment and practical reports, to convey ideas and principles.

Unit Availability

Year	Semester	Att Mode	Location	Learning Method	Timetable*
2024	Semester 2	Internal	Casuarina Campus	OLR	View
2024	Semester 2	External	External Studies	OLR	View

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OLR: Internet access is required for this unit as students are required to access study materials provided on-line through Learnline. Please log into Learnline on the first day of each teaching period. For more information about Learnline, please click here.

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Student Services and Amenities Fees
2024 Domestic Unit Fees (PDF)