CMT212留学生代做、Visual Communication代写、代做Python代写、代写Python程序语言

日期：2019-03-16 11:11

Coursework Assessment Pro-forma

Module Code: CMT212

Module Title: Visual Communication and Information Design

Assessment Title: Data Analysis and Visualisation Creation

Assessment Number: 2

Date Set: 4th March 2019

Submission Date and Time: 7th May 2019 at 9:30am.

Return Date: 4th June 2019

This assignment is worth 70% of the total marks available for this module. The penalty for

late or non-submission is an award of zero marks.

Your submission must include the official Coursework Submission Cover sheet, which can be

found here:

https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf

Submission Instructions

All submission should be via Learning Central. The current electronic coursework

submission policy can be found at:

http://www.cs.cf.ac.uk/currentstudents/ElectronicCourseworkSubmissionPolicy.pdf

Your submission should consist of a collection of code/documents used to analyse and

visualise your selected data, alongside a report detailing the process used.

Description Type Name

Cover sheet Compulsory One PDF (.pdf) file [student number].pdf

Data Analysis and

Visualisation

Compulsory One zip archive (.zip) containing all code used to extract,

analyse and visualise data

DAV_[student number].zip

Process Report Compulsory One PDF (.pdf) or Word file (.doc or .docx) PR_[student

number].pdf/doc/docx

Any deviation from the submission instructions above (including the number and types of

files submitted) may result in a mark of zero for the assessment or question part.

Assignment

You are asked to carry out an analysis of a dataset(s) and to present your findings in the

form of a report and visualisation(s), along with a record of your analysis.

You should find one or more freely available dataset(s) on any topic, from a reliable source.

You may wish to choose something from data.gov.uk or ons.gov.uk for example.

You should then carry out an analysis of this data to determine what the data tells you

about its particular topic. You may wish to use different statistical methods to describe the

data set, or to infer what the data tells us in a wider context. You should then visualise your

data in a way that allows a user to understand the data and what the data shows about its

topic. You can use any language or tool you like to carry out both the analysis and the

visualisation, but all code used must be submitted as part of the coursework. For example,

you may wish to extract, transform and analyse the data using Python, and then create

visualisations using d3.js.

You should create a report of your process that includes a description of the data and your

visualisation(s). Alongside this you should document your analysis methods and the

procedure used to create your visualisation. This record should include a commentary of the

code used to extract/transform/analyse data and show the development of the resulting

visualisation(s), including any prototype or rejected visualisations/analyses. It should also

include a reflective evaluation of your finished analysis.

Important! It is expected that each student will choose a different dataset. Once you have

chosen your dataset(s) for analysis, you should complete the form at http://bit.ly/cmt212-

1819-cw2 with your selection to confirm it is a unique choice. Dataset allocation will be

done on a first-come, first-served basis, so do not delay, as another student may ‘claim’ the

dataset first! Data selection should be completed by 18th March at 5PM. Any data

redistribution as part of your submission must abide by the licence under which the data

was obtained.

Learning Outcomes Assessed

3. Examine and explore data to find the best way it can be visually represented

4. Apply statistical methods to data

5. Access web APIs and data sources, retrieve and manipulate data

6. Create static, animated and interactive visualisations of data

Criteria for assessment

Credit will be awarded against the following criteria.

Component

&

Contribution

Fail Pass Merit Distinction

Dataset

selection and

analysis

(20%)

No real data used,

or dataset ‘fake’

No/basic analysis of

data

Real-world data

selected

Cursory high-level

analysis of data

Real-world data

selected

Data analysed in

detail

Appropriate

statistical methods

used to draw

conclusions

Multiple real-world

datasets on similar

theme selected

Appropriate

statistical methods

used to

compare/relate

datasets

Visualisation

and Data

Presentation

(60%)

None/poor

visualisation of data

Poor data

presentation

No story conveyed

to user,

story/findings

unclear

Data visualised

appropriately

Message/story clear

to end user

Multiple appropriate

visualisations

End user able to

explore/interpret

data and affect

display

Message/story clear

Multiple appropriate

visualisations with

interaction and/or

appropriate

animation

End user able to

explore/interpret

data and affect

display

Message/story clear

Process

Report

(20%)

No report/report

lacking in content

Little to no

evaluation

Analysis and

visualisation

process described

well.

Some effort at

evaluation

Analysis and

visualisation

process well

documented.

Reasonable

evaluation

Analysis and

visualisation

process thoroughly

documented.

Insightful evaluation

Feedback and suggestion for future learning

Feedback on your coursework will address the above criteria. Feedback and marks will be

returned on 4th June 2019 via email. Additional group feedback will be provided online via

video.