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Computational Methods for Data Analysis
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MATHS7002 - Computational Methods for Data Analysis
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20250600
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Course
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Unit
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2.1
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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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010199
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Mathematical Sciences, n.e.c.
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010199 - Mathematical Sciences, n.e.c.
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<a href="http://www.federation.edu.au/locate/unit/maths7002">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>This unit is aimed at students who are interested in the application of computational methods to solve various problems in data analysis and in particular, to solve a variety of optimisation problems. In this unit you will be provided with a comprehensive introduction to numerical methods of linear algebra, optimisation and to numerical methods applied to model and solve problems in data science. It would be particularly valuable to prospective data scientists in improving their understanding of basic computational approaches and techniques in data science. This unit can be used by students interested in pursuing more advanced mathematical units, particularly in the areas of Computing, Information Technology and Data Science.</p>
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<p>This unit comprises a range of numerical analysis and mathematical optimisation approaches and methods to solve various data mining problems. It contains material which will be of interest and relevant to data science students who seek to develop mathematical modelling and solution methods in data mining. The unit provides valuable enrichment material for those students intending to become data analysts or data scientists. Topics may include: Multivariate calculus, derivatives, Hessians, Jacobians; chain rules; approximations of functions Methods of numerical linear algebra Graph theory, decision trees, Markov chains Elements of optimisation theory, the design of optimisation algorithms. Necessary and sufficient optimality conditions. Unconstrained optimisation, gradient methods. Conjugate gradient, Newton and quasi-Newton methods. Numerical methods for constrained optimisation. Optimisation models and methods in the supervised data classification. Methods of Evolutionary optimisation</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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2027
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2026
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MATHS7002 - Computational Methods for Data Analysis
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MATHS7002 - Computational Methods for Data Analysis
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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>This unit is aimed at students who are interested in the application of computational methods to solve various problems in data analysis and in particular, to solve a variety of optimisation problems. In this unit you will be provided with a comprehensive introduction to numerical methods of linear algebra, optimisation and to numerical methods applied to model and solve problems in data science. It would be particularly valuable to prospective data scientists in improving their understanding of basic computational approaches and techniques in data science. This unit can be used by students interested in pursuing more advanced mathematical units, particularly in the areas of Computing, Information Technology and Data Science.</p>
Unit content
<p>This unit comprises a range of numerical analysis and mathematical optimisation approaches and methods to solve various data mining problems. It contains material which will be of interest and relevant to data science students who seek to develop mathematical modelling and solution methods in data mining. The unit provides valuable enrichment material for those students intending to become data analysts or data scientists. Topics may include: Multivariate calculus, derivatives, Hessians, Jacobians; chain rules; approximations of functions Methods of numerical linear algebra Graph theory, decision trees, Markov chains Elements of optimisation theory, the design of optimisation algorithms. Necessary and sufficient optimality conditions. Unconstrained optimisation, gradient methods. Conjugate gradient, Newton and quasi-Newton methods. Numerical methods for constrained optimisation. Optimisation models and methods in the supervised data classification. Methods of Evolutionary optimisation</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:
010199 - Mathematical Sciences, 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/maths7002">View unit fees</a>
Requisites
Prerequisite
<p>must have completed "<a href="/ords/r/curriculum/catalogue/course?p6_code=STATS5000" target="_blank">STATS5000</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>Examine application of linear algebra in data science.</p>
K2
<p>Investigate the techniques of graph theory and decision trees for solving data science problems</p>
K3
<p>Analyse various optimisation models used in data science.</p>
K4
<p>Analyse various optimality conditions used in optimisation problems to obtain optimum solutions.</p>
K5
<p>Evaluate and apply optimisation methods to solve both unconstrained and constrained problems, ensuring local and global optimal solutions are obtained.</p>
K6
<p>Critically evaluate optimisation techniques, considering their applicability to diverse and complex problems that may not have unique solutions in the field of data science.</p>
S1
<p>Decompose authentic problems into sub components and apply appropriate optimisation techniques to solve them.</p>
S2
<p>Design algorithms based on optimisation models, incorporating linear algebra, graph theory, and decision trees for solving complex data science challenges.</p>
S3
<p>Model various data science problems using optimisation and numerical linear algebra techniques.</p>
A1
<p>Apply optimisation techniques for solving various data science problems</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>Computational methods for data science</p>
<p>Assignments/Quizzes</p>
<p>10%-30%</p>
K1, K2, K3, K4, K5, K6, S1, S2, S3, A1
2
<p>Application of computational methods for solving data science problems including those that may not have obvious solutions and/or those may require decomposition.</p>
<p>Presentation/Project/Report</p>
<p>20% - 30%</p>
K1, K2, K3, K4, K5, K6, S1, S2, S3, A1
3
<p>Computational methods for solving optimisation problems in data science</p>
<p>Final test</p>
<p>40% - 60%</p>
K1, K2, K3, K4, K5, K6, S1, S2, S3
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:
010199 - Mathematical Sciences, 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/maths7002">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: MATHS7002 - U2.1