SIT742 - Modern Data Science

Unit details


2024 unit information

Enrolment modes:Trimester 2: Burwood (Melbourne), Online
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 2: Gang Li


Incompatible with: Nil
Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

This will include educator guided online learning activities within the unit site.

Educator-facilitated (scheduled) learning activities - on-campus unit enrolment:

1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week. Weekly meetings.

Educator-facilitated (scheduled) learning activities - online unit enrolment:

Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour practical experience (workshop) per week, weekly meetings.


In this unit, students will have the opportunity to learn fundamental aspects of data science, modern methods, techniques and applications of data science. Upon successful completion of study, students will be able to use distributed storage and computing platform to process and analyse big data, and use modern techniques in data analytics.

Learning activities in this unit are designed for students to develop knowledge and skills in reviewing tabular data such as relational database, distributed storage and computing platforms with materials on Apache Spark. In learning data analytics, students will use feature selection, data reduction and machine learning methods. Students will also have the opportunity to learn advanced concepts in prediction including linear regression, logistic regression and decision tree classifiers, and to learn frequent pattern discovery using association rule mining algorithms.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes

Develop knowledge of and discuss new and emerging fields in data science.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO8: Global citizenship


Describe advanced constituents and underlying theoretical foundation of data science.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO6: Self-management


Evaluate modern data analytics and its implication in real-world applications.

GLO4: Critical thinking
GLO5: Problem solving
GLO8: Global citizenship


Use appropriate platform to collect and process relatively large datasets.

GLO2: Communication
GLO4: Critical thinking
GLO5: Problem solving


Collect, model and conduct inferential as well predictive tasks from data.

GLO4: Critical thinking
GLO5: Problem solving
GLO7: Teamwork


Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week

Assessment 1
Critical analysis (Individual)

Coding and written report 30% Week 5

Assessment 2
Project (Group)

Coding and written report 50% Week 10
End-of-Unit Assessment  Timed online test 20% End-of-Unit Assessment Period

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Learning Resource

The texts and reading list for the unit can be found on the University Library via the link below: SIT742 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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