KF7032: Apply Big Data Analytic Algorithms Report Writing - IT Assignment Help

Asignment Task:

Task:

Aims
The aim of this assignment is to introduce a practical application of Big Data and Cloud Computing using a realistic big data problem. Students will implement a solution using an industry leading Cloud computing provider together with the distributed processing environment Apache Spark. This will involve the selection of problem appropriate Machine

Learning algorithms and methods.

Learning Outcomes Assessed:
Knowledge & Understanding
LO 1. Apply big data analytic algorithms, including those for visualization and cloud computing techniques to multi-terabyte datasets.
LO 2. Critically assess data analytic and machine learning algorithms to identify those that satisfy given big data problem requirements Intellectual / Professional skills & abilities:
LO 3. Critically evaluate and select appropriate big data analytic algorithms to solve a given problem, considering the processing time available and other aspects of the problem.
LO 4. Design and develop advanced big data applications that integrate with third party cloud computing services Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
LO 5. Critically assess the relationship between knowledge and the ethical and social interpretation of primary research using big data.

Training Tasks
Training Task 1: Peer Reviewed Task (24%)
The objective of this task is to ensure that students have mastered these skills which are required for final module assessment:
1. Process a data set using the recommended software environment for the module.
2. Explaining the logical reasoning behind your code.
This work will be peer assessed as recommended the British Computer Society. That is, you will critically assess the work of fellow students (your peers) and THEY will assess yours.

In detail:
1. You will create a Jupyter notebook based the scenario below (which is derived from weekly worksheets 1-4) explaining your code using notebook embedded Markdown (i.e. formatted text, not just comments)
2. You will post your notebook to the module discussion board on Blackboard
3. You will then mark (i.e. peer review) the submission preceding yours on the discussion board, and the one following it, using the marking scheme below and post these mark sheets
4. Your mark for this task will be the average of your peer marks.

Scenario:
Suppose you are a police department with a limited budget. You plan to reduce road-traffic accidents by a one-month targeted advertising campaign.
Using the given dataset, which gender, age group, and month would be the largest target group as indicated by positive breath tests?

Training Task 2: Group work participation Task (6%)
The objective of this task is to derive background study materials for the big data product to be used by the whole class. These may include (but are limited to) reviewing the literature on crime and big data, examining published work on violent crime and its causes, technical approaches to crime and big data, relevant statistics and other computational methods. That is, to research the topic in general.
Working in teams of up to four students, each group will produce at least 2000 original words, plus 10 references to scientific conference or journal papers.
Since this is a group training task, your participation is assessed, rather than your content (Students will be able to receive staff feedback on content during taught sessions). Group work participation Task Marking Scheme

Each group will score 6% of the module mark proportionally reduced by percentage of copied work as determined by Turnitin (threshold 10%), number of words less than 2000, number of references less than 10.

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