LO1 Practically use advanced software tools and frameworks in relation to big data analytics and visualisation
2024-08-21 14:35:20
Key team contact details
Module content
Big data is a fast-growing field and skills in the area are some of the most in demand today. Big data technologies cover a range of architectures,frameworks and algorithms designed to handle very large, often highly complex datasets. The module aims to provide a balanced view of the theory and practice on big data analytics, allowing students to develop a variety of big data analytics knowledge and skills. The module will enable the student to:
·gain a systematic understanding of big data and its applications, and a critical awareness of current issues for storing, managing, processing, analysing and visualising massive amounts datasets
·achieve an in-depth practical understanding of big data software tools and frameworks underpinning big data analytics and visualisation
·acquire a comprehensive knowledge of statistical, mathematical and machine learning techniques, and develop the ability to critically design, implement and evaluate big data analytics modelling and applications to complex real-world problems.
This module includes the following key topics pertaining to big data analytics:
· big data, its applications, and common issues in big data analytics
· big data ecosystem, its common software tools, frameworks and platforms
· statistical and machine learning techniques used for big data analytics, and associated software tools and frameworks
· mathematical modelling underpinning big data analytics
· concepts, tools and techniques for data visualisation to support
Learning materials
The reading list for this module is available on Blackboard in the module area and online by searching readinglists. This shows real-time availability of books in the library and provides direct links to digital items, recommended by your lecturer.
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Subject guides (libraryguides) are also available to help you find relevant information for assignments, with contact details of the Subject Librarian for your School.
Maintaining Academic Honesty and Integrity
Academic Integrity means avoiding plagiarism and cheating and owning your own work, the use of essay mills and AI content is also considered academic misconduct. This is when you submit a piece of work which is not completely your own, but which you are presenting as your own without acknowledging the author or properly referencing the original source. All your work must demonstrate Academic Integrity; it must be an honest and fair submission, complying with all the requirements of the assessment. Failure to meet these standards of behaviour and practice is academic misconduct, which can result in penalties being applied under the Academic Offences Regulations. You can get support with your academic writing by speaking to our Study support team.
Meeting Deadlines
Getting Support
There may be times when you experience circumstances outside of your control and talking to your Module Leader and other support services available to you in the university will help keep you on track with your studies. You can access information on support services and further guidance at our Support for current students page.
LO1 Practically use advanced software tools and frameworks in relation to big data analytics and visualisation
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If you request an extension or mitigation before the deadline you can choose to self-certify, without providing evidence, so long as you have a valid reason. You can only self-certify three assessments per academic year.If you have used all your self-certification opportunities, or requested mitigation after the deadline, you will need to provide evidence of your exceptional circumstances for your request to be granted.
Your Students’ Union Advice Team will be able to support you through the process.
Preparing for your Assessment
A key part of your learning will be preparation for your summative assessment.You will be provided feedback on your formative assessments, and this will help you to better understand what is required of you when you submit your summative assessment.Please see section below for the guidance on your formative assessment and how to access your feedback.
LO2 Critically and creatively model and analyse data by applying selected big data analytics techniques
Summative Assessments
The assessment of this module contains two written assignments, each having a specific weighting, and its own criteria. The assessment structure is described below. The learning outcomes of the module are assessed by a successful completion of all the assessments. You must achieve an overall mark of at least 50%.
Summative Assessment 1
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Assessment title
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Application of Big Data
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Submission date and time
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Week 8
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Word Count (or equivalent)
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3,000 – 3,500 Order now
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Where to submit
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Blackboard
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Feedback date
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Feedback in 15 working days
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Assessment Weighting
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40%
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PSRB requirements (if applicable)
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Submitting, feedback & grades online using Blackboard
Main objectives of the Assessment
Please see a description of the assessment content below.
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No.
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Learning Outcome
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Marking Criteria
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1
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Critically identify big data application areas, and address associated legal,social,ethical,professional,environmental and societal issues and implications, where appropriate
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See marking rubrics on Blackboard
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Summative Assessment 2
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Assessment title
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Big Data Analytics Project
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Submission date and time
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Week 15
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Word Count (or equivalent)
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Task sheet with 10 tasks
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Where to submit
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Blackboard
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Feedback date
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Feedback in 15 working days
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Assessment Weighting
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60%
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PSRB requirements (if applicable)
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Submitting, feedback & grades online using Blackboard
Main objectives of the Assessment
Please see a description of the assessment content below.
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No.
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Learning Outcome
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Marking Criteria
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1
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Practically use advanced software tools and frameworks in relation to big data analytics and visualisation
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See marking rubrics on Blackboard
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2
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Critically and creatively model and analyse data by applying selected big data analytics techniques
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See marking rubrics on Blackboard
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3
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Present demonstrate a systematic and integrated approach to big data analytics
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See marking rubrics on Blackboard
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Assessment 1
Assessment task: Application of Big Data Weighting: 40%
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Assessment 1
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Title
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Application of Big Data
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Task details
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Big data analytics often harnesses the power of parallel and distributed computing to handle the immense volume and complexity of modern datasets. This assignment requires you to conduct a technical review of a specific big data analytics technology (or a mixture of several technologies) powered by parallel and distributed computing and review its use in big data applications. Based on the literature review, you should prepare both a talk (to be given to the class), and a written report (to be submitted onto Blackboard).
1. Assignment tasks
· Identifying technology – Based on your interest, you need to identify one (or multiple) big data analytics technology (or platform/system) powered by parallel and distributed computing. Some prominent technologies of such kind may include Apache Hadoop, Apache Spark, Apache Flink, Apache Kafka, etc.
·Conducting review – The review should focus on the technicalities and applications of the big data analytics technology you identified. Strengthen your review by referencing relevant academic papers from credible sources like ACM Digital Library, IEEE Xplore, or Elsevier ScienceDirect (all accessible through the UWL library website). Focus on papers that explore the specific big data analytics technology you identified.
‐ Firstly, the review should address the technicalities of the big data analytics technology such as its core features, functionality, architecture, and processing models/methods, etc. It is also expected to see your critical analysis on the strengths and weaknesses of the technology compared to similar technologies.
‐ Secondly, analyse the practical applications of the chosen big data analytics technology by drawing upon specific cases / examples identified through your literature review. Real-world applications of big data analytics technologies can be drawn from a wide range of fields, such as healthcare, massive transportation systems, social networks, smart homes, cybersecurity, and business intelligence. The specific cases / examples reviewed should showcase the diverse ways the big data technology has been implemented and driven improvements in various sectors.
‐ Lastly, present the findings and conclusions of your review, including literature analysis, insights gained, and the impact of the technology. You may also address potential challenges, limitations of the technology and suggest potential future advancements of the technology.
2. Assignment marking
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This assessment includes a formative assessment which requires each student to give an in-class presentation. You will receive feedback from tutor and peers. Please consider feedback received and write your individual report accordingly prior to uploading it onto Blackboard in a single MS Word or PDF file by the submission date.
The written report will be graded according to the following factors:
· Technical understanding and knowledge
· Critical analysis and evaluation of literature
· Writing style and presentation
Attention- Students caught with plagiarism will fail this module. The Blackboard has electronic checking system that automatically identifies any sources that could be used to copy from. Please do NOT think you can get away with this.
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General Criteria
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Issues
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Mark
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Topic and Issues
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A specific topic of big data analytics technology (or a mixture of multiple technologies) powered by parallel and distributed computing identified with appropriate complexity and significance (5)
Issues and challenges properly addressed in the selected topic of big data application (5)
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/10
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Review and Analysis
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Critical review and analysis on the technicalities of the chosen big data analytics technology (10)
Critical review and analysis on the practical applications of the chosen big data analytics technology supported by specific cases/ examples (10)
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/20
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Organization and Presentation
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Logical and fluent organization and content (5)
Proper presentation of report (layout, headings, diagrams, citations and references, etc.) (5)
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/10
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Total mark
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/40
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Assessment 2
Assessment task: Big Data Analytics Project Weighting: 60%
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Assessment 2
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Title
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Big Data Analytics Project
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Task details
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This assignment requires you to apply appropriate big data analytics methods, models and techniques on specific use case involving a series of tasks such as dataset summary, pre- processing, processing, analytics, visualisation and so on (Each task carries certain marks and datasets will be provided). You need to formally analyse requirements, formulate solution, and implement your solution in Python programming. Finally, you need to present your solution and results in a written technical report.
1. Assignment tasks
For each task given in this coursework assignment, you need to
·Formulate technical solution step by step, e.g., building appropriate big data analytics models / algorithms, justifying appropriateness of models/algorithms/techniques used, applying them in the context of the given task, and practising data visualisation techniques where appropriate. Please provide referencing on all appropriate literature.
·Implement the proposed solution using Python programming language. Explain the source code in details.
·Test your solution and conduct experiments where appropriate. Evidence of working program (e.g., screenshots) should be presented.
· Evaluate the performance of implemented solution and analyse results where appropriate. Delve into deep technical explanations of the results and suggest possible improvement, etc.
· Make conclusions where appropriate.
2. Project report
You need to present your solution into a proper technical report. The report should be submitted onto Blackboard.
3. Project marking
Every student will be invited to give an in-class presentation on your project topic. Your tutor and fellow student may provide feedback/comments to you.The final project report should be uploaded onto Blackboard in a single PDF via the specified link.
The report will be graded according to factors including:
· Appropriateness of technical solution and evidence of implementation
· Depth of critical analysis
· Writing style and presentation
Attention - Students caught with plagiarism will fail this module.The Blackboard has electronic checking system that automatically identifies any sources (literature and programming source code) that could be used to copy from. Please do NOT think you can get away with this.For more queries
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