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HE Unit Template
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Experiment Design and Visualisation
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DATSC7003 - Experiment Design and Visualisation
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20260200
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Course
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3.1
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2026-09-10
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2026-09-10
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Engineering
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Institute of Innovation, Science & Sustainability
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Engineering
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Postgraduate - Coursework
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15
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.125
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020199
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Computer Science, n.e.c.
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020199 - Computer Science, n.e.c.
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<a href="http://www.federation.edu.au/locate/unit/datsc7003">View unit fees</a>
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Graded (e.g. HD, D, C, etc)
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Intermediate
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<p>EXPERIMENT DESIGN AND VISUALIZATION will focus on the application of data science techniques/tools to various domains (real-world data). It uses analytical and data science methods to solve real-world application questions and to implement the solution using tools. We will work through case studies in a variety of contexts including, e.g., business, science, healthcare, industry, education and society to investigate how knowledge and value are extracted from data. Through examining the wide-ranging applications of data science, we will further understand the underlying learning algorithms, models, codes and data. Topics will include experimental and project design, business predictive analytics, data processing, model training and evaluation, algorithm and code analysis, application cases analytics, software tools, visualisation and project management.</p>
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<p>experimental and project design data design predictive analytics data processing model training and evaluation algorithm and code analysis application case analytics data visualisation practice software tools project management ICT governance, ethical and social considerations</p>
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<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
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Year
2027
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2026
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DATSC7003 - Experiment Design and Visualisation
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DATSC7003 - Experiment Design and Visualisation
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Semester 1
Semester 2
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Year
2027
2026
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Overview
Key information
Requisites
Learning outcomes
Assessment
Parent Region
Overview
Unit description
<p>EXPERIMENT DESIGN AND VISUALIZATION will focus on the application of data science techniques/tools to various domains (real-world data). It uses analytical and data science methods to solve real-world application questions and to implement the solution using tools. We will work through case studies in a variety of contexts including, e.g., business, science, healthcare, industry, education and society to investigate how knowledge and value are extracted from data. Through examining the wide-ranging applications of data science, we will further understand the underlying learning algorithms, models, codes and data. Topics will include experimental and project design, business predictive analytics, data processing, model training and evaluation, algorithm and code analysis, application cases analytics, software tools, visualisation and project management.</p>
Unit content
<p>experimental and project design data design predictive analytics data processing model training and evaluation algorithm and code analysis application case analytics data visualisation practice software tools project management ICT governance, ethical and social considerations</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
020199 - Computer Science, n.e.c.
Offering:
<p><strong>Semester 2</strong><p>Ballarat - Mt Helen (On Campus)</p><p>Melbourne City Campus (On Campus)</p><p>On-line Learning</p></p>
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/datsc7003">View unit fees</a>
Requisites
Prerequisite
<p>must have completed "<a href="/ords/r/curriculum/catalogue/course?p6_code=ITECH7001" target="_blank">ITECH7001</a>"</p>
Corequisite
None
Exclusion
None
Learning outcomes
Learning outcomes
<p>On successful completion of this unit, students are expected to gain the following [K]nowledge, [S]kills and [A]pplication of knowledge & skills</p>
Learning Outcomes
Ref
Outcome
K1
<p>Evaluate data science applications in various domains.</p>
K2
<p>Discriminate principles that underpin data science, experimental design, algorithms, and learning models.</p>
K3
<p>Integrate knowledge of data science and associated tools into ICT program and project management.</p>
K4
<p>Analyse, evaluate and synthesise findings from data science investigations in a form suitable for specialist and non-specialist audiences.</p>
S1
<p>Critique the experimental integrity of data science project implementation.</p>
S2
<p>Apply data science tools to solve authentic problems based on business requirements.</p>
A1
<p>Utilise modelling, analysis, programming, and visualisation techniques/tools for data science projects.</p>
A2
<p>Select and employ relevant ICT governance standards, including ethical and social considerations, in the analysis of a real-world scenario of data science practice in industry.</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>Develop skills in the analysis and practical application of data science techniques/tools.</p>
<p>Tutorials, assignments, and/or exercises</p>
<p>40%-60%</p>
K1, K2, K3, K4, S1, S2, A1
2
<p>Students will critically evaluate a range of authetic problems drawn from case studies, including complex computing problems, to provide practical solutions.</p>
<p>Reports and/or presentations</p>
<p>40%-60%</p>
K1, K2, K3, K4, S1, S2, A1, A2
Supplementary assessment available:
Yes
Supplementary assessment information:
<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
020199 - Computer Science, n.e.c.
Offering:
<p><strong>Semester 2</strong><p>Ballarat - Mt Helen (On Campus)</p><p>Melbourne City Campus (On Campus)</p><p>On-line Learning</p></p>
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/datsc7003">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: DATSC7003 - U3.1