SIT742 - Modern Data Science

Unit details

Year:

2022 unit information

Important Update:

Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates.

Last updated: 4 March 2022

Enrolment modes:Trimester 2: Burwood (Melbourne), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 2: 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 online class per week, 1 x 2 hour workshop per week. Weekly drop-in sessions.

Scheduled learning activities - cloud:

Online independent and collaborative learning including optional scheduled activities as detailed in the unit site.

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.

ULO 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

GLO7: Teamwork

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

Assessment

Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week
Critical analysis (individual) Coding and written report 25% Week 5
Project (group) Coding and written report 40% Week 10
Online Examination 2-hour online examination 35% 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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