SIT742 - Modern Data Science

Unit details

Year:

2021 unit information

Important Update:

Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered.

For Semester 1/Trimester 1 2021, teaching will be delivered in line with the most current COVIDSafe health guidelines.  This may include a mix of online and on-campus activities.  Please check your unit sites for announcements and updates one week prior to the start of Semester/Trimester.

Thank you for your flexibility and commitment to studying with Deakin in 2021.

Last updated: 16 November 2020

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

Nil

Corequisite:

Nil

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.

Scheduled learning activities - campus:

1 x 2 hour class per week, 1 x 1 hour practical per week.

Scheduled learning activities - cloud:

1 x 1 hour scheduled online workshop per week.

Content

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.

 

These are the Learning Outcomes (ULO) for this Unit

At the completion of this unit successful students can:

Deakin Graduate Learning Outcomes

ULO1

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

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

ULO2

Describe advanced constituents and underlying theoretical foundation of data science.

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

ULO3

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

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

ULO4

Use appropriate platform to collect and process relatively large datasets.

GLO2: Communication
GLO4: Critical thinking
GLO5: Problem solving

ULO5

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

GLO4: Critical thinking
GLO5: Problem solving

These Unit Learning Outcomes are applicable for all teaching periods throughout the year

Assessment

Assessment Description Student output Weighting (% total mark for unit) Indicative due week
Critical analysis Written report 25% Week 5
Project Written project report 40% Week 10
Quiz Online quiz (maximum 1-hour) 5% Week 10
Examination 2-hour written examination 30% Examination 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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