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HE Unit Template
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Data Science Case Studies
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ITECH7003 - Data Science Case Studies
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20240200
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1.2
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2026-09-10
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2026-09-10
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Information Technology
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Institute of Innovation, Science & Sustainability
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Information Technology
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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/itech7003">View unit fees</a>
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Graded (e.g. HD, D, C, etc)
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Masters Degree
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1
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<p>DATA SCIENCE CASE STUDIES 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>Topics may include: 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 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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Semester
Semester
0200
Year
Year
2027
Current Year
2026
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ITECH7003 - Data Science Case Studies
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ITECH7003 - Data Science Case Studies
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Semester
Semester 1
Semester 2
Summer Semester
Year
2027
2026
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Overview
Key information
Requisites
Learning outcomes
Assessment
FedTASK
Parent Region
Overview
Unit description
<p>DATA SCIENCE CASE STUDIES 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>Topics may include: 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 ethical and social considerations</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Information Technology
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
020199 - Computer Science, n.e.c.
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/itech7003">View unit fees</a>
Requisites
Prerequisite
<p>must have completed "<a href="/ords/r/curriculum/catalogue/course?p6_code=ITECH5007" target="_blank">ITECH5007</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>Contrast data science applications in various domains.</p>
K2
<p>Examine basic principles that underpin data science, experimental design, algorithms, and learning models.</p>
K3
<p>Integrate knowledge of data science and associated tools for developing authentic data science projects.</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>Critically evaluate the keys to successful data science project implementation.</p>
S2
<p>Effectively apply data science knowledge and techniques to solve authentic problems.</p>
S3
<p>Design and execute a data science project based on business requirements, and reflect on the experience.</p>
A1
<p>Utilise modelling, analysis, programming, and visualisation techniques/tools for data science projects.</p>
A2
<p>Select and employ relevant standards, 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, S3, A1
2
<p>Students will provide theoretical answers and provide practical solutions to a range of questions and problems drawn from case studies.</p>
<p>Test(s)</p>
<p>40%-60%</p>
K1, K2, K3, K4, S1, S2, S3, 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>
FedTASK
FedTASK
<p>Federation University recognises that students require key transferable employability skills to prepare them for their future workplace and society. FedTASKs (<strong>T</strong>ransferable <strong>A</strong>ttributes <strong>S</strong>kills and <strong>K</strong>nowledge) provide a targeted focus on five key, transferable Attributes, Skills, and Knowledge that are embedded within curriculum, developed gradually towards successful measures and interlinked with cross-discipline and co-operative learning opportunities.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Information Technology
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
020199 - Computer Science, n.e.c.
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/itech7003">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: ITECH7003 - U1.2