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HIT140 - Foundations of Data Science

2024

Sector

Higher Education

Discipline

Information Technology

(STA101)

Unit Description

This is an entry-level data science unit. This unit introduces students to foundational concepts and skills in data science and statistics. Students will be introduced to important elements in a modern data analytics ecosystem. They will learn descriptive and inferential statistics, data preprocessing techniques, basic machine learning models, and effective data visualisations using established data science platforms and analytics tools. Students will also be exposed to how data science solutions are deployed in response to real-world problem scenarios, in addition to exploring ethical approaches to contemporary issues faced by data science professionals. This unit requires the use of additional software to be provided by the University.

Unit Learning Outcomes

1.	Identify current data science solutions for real-world problems.
2.	Utilise essential data wrangling techniques.
3.	Perform descriptive and inferential statistical analyses.
4.	Implement basic supervised and unsupervised machine learning models on selected data science and visualisation tools.
5.	Construct viable data science pipelines.
6.	Demonstrate a critical awareness of the ethical and professional issues surrounding data science practices.

Assessment Overview

	Assessment Task	Value (of total mark)	Related Learning Outcome/s
1.	Online quiz (30 minutes)	10%	1 - Identify current data science solutions for real-world problems. 3 - Perform descriptive and inferential statistical analyses. 6 - Demonstrate a critical awareness of the ethical and professional issues surrounding data science practices.
2.	Group Project Presentation (4 mins per student)	40%	1 - Identify current data science solutions for real-world problems. 2 - Utilise essential data wrangling techniques. 3 - Perform descriptive and inferential statistical analyses. 6 - Demonstrate a critical awareness of the ethical and professional issues surrounding data science practices.
3.	Group Project Report (1500 words per student)	50%	1 - Identify current data science solutions for real-world problems. 2 - Utilise essential data wrangling techniques. 3 - Perform descriptive and inferential statistical analyses. 4 - Implement basic supervised and unsupervised machine learning models on selected data science and visualisation tools. 5 - Construct viable data science pipelines.

Unit Availability

Year	Semester	Att Mode	Location	Learning Method
2024	Semester 2	Internal	CDU Sydney	OLR
2024	Semester 2	Internal	Casuarina Campus	OLR
2024	Semester 2	External	External Studies	OLR

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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.

Critical Dates

Important Dates for Students

Fee Information:
Fees and Payments
Student Services and Amenities Fees
2023 Domestic Unit Fees (PDF)