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SIT112 - Data Science Concepts

Year:

2020 unit information

Important Update:

Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.

Last updated: 2 June 2020

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.

 

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
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving

ULO2

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

GLO4: Critical thinking
GLO5: Problem solving

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

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

ULO5

Design and develop data-driven algorithms for outcome prediction

GLO1: Discipline-specific knowledge and capabilities
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
Quizzes Two online quizzes 20% (2 x 10%) Weeks 4 and 8
Individual problem solving task Written report, 15 page maximum 25% Week 5
Group problem solving task Written collaborative report, 30 page maximum 30% Week 9
Data science project Programming and data analytics 25% Week 11

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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