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SIT112 - Data Science Concepts

Year:

2021 unit information

Important Update:

Unit delivery will continue to be provided in line with the most current COVIDSafe health guidelines. This may include a mix of on-campus and online activities. To find out how you are impacted, please check your unit sites for announcements and updates. Unit sites open one week prior to the start of each Trimester/Semester.

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Last updated: 4 June 2021

Enrolment modes:Trimester 1: Burwood (Melbourne), Online
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Sergiy Shelyag
Prerequisite:

Nil

Corequisite:

Nil

Incompatible with:

SIT199

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 2 hour practical per week.

Scheduled learning activities - cloud:

1 x 1 hour scheduled online workshop per week.

Content

Data science is an emerging field and data scientists must be able to know how to make sense of data. In SIT112, students will develop knowledge of fundamentals in data science, in particular data manipulation and algorithms for analytics. The unit will also cover the practice of data science including ethical and responsible behaviour when crawling, cleaning, analysing, representing and repurposing the data. Students will be able to obtain data, recognise data formats, summarise and visualise relationships in the data, perform exploratory data analysis tasks and build predictive models.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
ULO1

Demonstrate data acquisition, data representation and data pre-processing skills to describe, analyse and repurpose data from a variety of sources.


GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving
GLO7: Teamwork

ULO2

Apply critical thinking and statistical techniques to understand and visualize relationships in data

GLO2: Communication
GLO4: Critical thinking
GLO5: Problem solving
GLO7: Teamwork

ULO3

Apply machine-learning techniques in exploratory data analysis for problems related to commerce, industry and research.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving
GLO7: Teamwork

ULO4

Design and compute a statistical relationships in data including correlation and linear regression

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving
GLO7: Teamwork

ULO5

Design and develop data-driven algorithms for outcome prediction

GLO1: Discipline-specific knowledge and capabilities
GLO5: Problem solving
GLO7: Teamwork

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
Quizzes Two online quizzes 20% (2 x 10%) Weeks 5 and 9
Individual problem solving task Written report, 15 page maximum 25% Week 6
Group problem solving task Written collaborative report, 30 page maximum 30% Week 11
Examination 2-hour written examination 25% 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: SIT112 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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