CST 3130 – Advanced Web Development with Big Data – Coursework 2

CST 3130 – Advanced Web Development with Big Data – Coursework 2

Coursework 2 – Data Visualization Website

1. Summary

Version: 1.0

Individual project

Weighting: 50% of overall mark

Deadline for project proposal: 16:00 Friday 22nd January 2021 (end of Week 14)

Deadline for final submission: 16:00 Friday 16th April 2021 (end of Week 24)

2.Key Points

•You create a website that displays numerical data, predictions about the numerical data and the results of sentiment analysis.

•The numerical data will be obtained from web services. It cannot be obtained from web scraping. For example, it could be product price data from web services, stock prices, exchange rate prices, weather, football results, etc.

•The text data for sentiment analysis will be obtained from web services, such as the Twitter API or Facebook Graph.

•Machine learning will be used to make predictions about future values of the data.

•You will also display synthetic data that we will provide to check your data visualization and machine learning.

•All third party data will be stored in the cloud.

•The front end of the website only has to display visualizations of the data, predictions about the data and the results of the sentiment analysis. No other functionality is required.

•The code that downloads data from web services and uploads it to the cloud must be written in TypeScript.

•Your website will be hosted on the cloud using serverless technology. Lambda functions on the server can be written in any programming language (JavaScript is recommended).

•The front end of your website can use ordinary JavaScript or a JavaScript framework.

•WebSockets will push new data items to subscribed clients.

•The coursework and teaching materials will be based on Amazon Web Services (AWS). You are welcome to use a different cloud provider. However, we will only be able to provide very limited support with projects that are based on a different cloud provider.

•The final submission of your project will only receive a mark if your submission includes a video demonstration.

3.What Needs to be Submitted

3.1 Project Proposal (Deadline: 16:00 Friday 22nd January 2021)

A document that contains:

•Brief description of the proposed website.

•Mock-ups of front end of website showing proposed data visualization. These can be pen and paper, Word, Inkscape etc.

•List of source(s) of numerical data.

•List of source(s) of data for sentiment analysis.

•Screenshot and URL of static website hosted on cloud.

Submit Word or PDF version of project proposal using the link in the Coursework 2 section of the course website.
Your proposal must be in Word or PDF format.

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CST 3130 – Advanced Web Development with Big Data – Coursework 2

We will use the project proposal to give you feedback about your idea and help you realize it in the time available.

You can reuse material from the project proposal in the final project submission.

The project proposal will be marked online. It is worth 10% of the mark for Coursework 2.

3.2 Final Submission (Deadline: 16:00 Friday 16th April 2021)

Submit a zip file that contains:

1.Project report. This must include:

•Screenshot(s) of the front end of your website.

•Screenshots of all the data visualization. The pictures of the data visualization must be high resolution so that we can check your predictions about the synthetic data.

•Architecture diagram showing the relationships between Lambda functions, API Gateway, database, etc.

•Description of the website. You should explain the machine learning and sentiment analysis and how your lambda functions and database work.

•Do not include screenshots of code.

•This must be a Word or PDF document.

2.Source code. Your source code folder should contain the following files:

•TypeScript source files.

•Source code for Lambda functions.

•Source files for front end of website, for example, HTML, JS, Vue.js files etc.

•Please do not include the node_modules folder in your submission. It is likely to make your submission too large to upload!

3.Machine learning files.

•Include all of the files that you used for training and testing the machine learning in a separate folder.

4.5-minute video demonstration. Video demonstrations are mandatory for the final submission. I strongly recommend that you watch the talk on recording video demonstrations on the course website.

Upload the zip file using the link in the Coursework 2 section of the course website.

Marks will only be allocated for functionality that exists in your submitted files, so make sure that you upload all of the files for your project.

The final submission is worth 90% of the mark for Coursework 2.

4. Formative Feedback

Formative assessments do not directly contribute to the overall module mark but they do provide an important opportunity to receive feedback on your learning. They provide an opportunity to evaluate and reflect on your understanding of what you have learnt. They also help your tutors identify what further support and guidance can be given to improve your grade.

We are happy to give you feedback about Coursework 2 in the labs and can also give feedback about drafts of Coursework 2 that are sent to us more than one week prior to the deadline.

5. Extenuating Circumstances

If you have personal problems that interfere with your studies, you can apply for extra time to complete coursework without a mark penalty. You have to provide appropriate documentary evidence.

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CST 3130 – Advanced Web Development with Big Data – Coursework 2

More information here: https://unihub.mdx.ac.uk/your-study/assessment-and-regulations/extenuating-circumstances.

You must let the module leader know if you have been granted an extension.

6. Late Submission

We are very unlikely to give extensions to coursework and very unlikely to accept excuses. So we strongly recommend that you hand coursework in on time.

Contact the module leader before the deadline if you run into problems. Zero marks are likely to be awarded for late coursework.

7. Plagiarism

Plagiarism is a serious academic offence. Students that submit identical projects will be reported to the university. If they are found guilty, they will have to resubmit their work, their marks could be capped or they could fail the module.

We recognize that there is often a blurry line between copying and collaboration. People work together and help each other to solve problems and apply the solutions to their own projects. We strongly encourage this kind of collaboration. But it is not acceptable for students to collaborate on a project which they submit as individual work. To penalize this, the mark for near-identical projects will be divided between the projects. So suppose a project gets a mark of 60% and near-identical versions are handed in by 3 people. Each person will get 20%, instead of 60%. This only applies to the marks for the parts of the project that are identical.

We are not going to police this and make detailed investigations. So if you allow your project to be copied, you will be as liable for plagiarism as the person who submits it as their own work. Both the original and the copy will receive zero or reduced marks.

Links to the relevant University regulations and additional support resources can be found here:

•Academic Integrity Awareness Course: https://mdx.mrooms.net/mod/lesson/view.php?id=877307. (You will have to log into to MyUniHub and then MyLearning to access the course.)

•Section F: Infringement of Assessment Regulations/Academic Misconduct: https://www.mdx.ac.uk/about-us/policies/university-regulations.

•Referencing & Plagiarism: Suspected of plagiarism?: http://libguides.mdx.ac.uk/c.php?g=322119&p=2155601.

•Referencing and avoiding plagiarism: http://unihub.mdx.ac.uk/your-study/learning-enhancement-team/online-resources/referencing-and-avoiding-plagiarism.

The MDXSU Advice Service offers free and independent support face-to-face in making an appeal, complaint or responding to any allegations of academic or non-academic misconduct. https://www.mdxsu.com/advice.

8. Assessment Methods

8.1 Project Proposal

We will read your project proposal and give you feedback online and in the labs.

8.2 Final Submission

We will look at the code, read your report and view up to 5 minutes of your video demonstration. Your video demonstrations should not be significantly longer than 5 minutes. Zero marks will be awarded for a final submission

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CST 3130 – Advanced Web Development with Big Data – Coursework 2

of Coursework 2 without a video demonstration. I strongly recommend that you watch the talk on recording video demonstrations on the course website.

The project will be given a mark out of 100. This will be scaled down to a mark between 0 and 50 that corresponds to 50% of the overall mark for the module.

9. Assessment Criteria

Feature Deadline Marks

Project proposal. 16:00 1 mark. Brief description of proposed website.
Must include: 22/1/21 1 mark. List of source(s) of numerical data.

• A short description of the proposed 1 mark. List of source(s) of data for sentiment
website. analysis.
• List of web services that will be 1 marks. Mock-ups of front end of website showing
accessed to obtain data for the proposed data visualization. These can be pen and
machine learning and sentiment paper, Word, Inkscape etc.
analysis. 2 marks. Proposal quality. Is it clearly written? Are the
• Mock-ups of front end of website
data sources sensible? Do the wireframes clearly show
showing proposed data visualization.
the website design?
• Screenshot and URL of static website

hosted on the cloud.

Static website hosted on cloud. A static 16:00 2 marks. Static ‘Hello World’ HTML page hosted on
HTML page containing at least one image 22/1/21 cloud.
hosted on the cloud. 2 marks. Static website contains an image that is also

hosted on the cloud.

Cloud storage of data from third party 16:00 2.5 marks. Download of numerical data from third
web service(s). The code for uploading 16/4/21 party web service(s).
the data must be written in TypeScript – 2.5 marks. Download of text data for sentiment
there are no marks for using Java or
analysis from third party web service(s).
ordinary JavaScript. Aim for at least 1000
4 marks. Storage of data from third party web
data points.
service(s) in cloud database.

Machine learning. Application of 16:00 5 marks. Use of machine learning to generate correct
machine learning to numerical data 16/4/21 predictions about synthetic data.
stored in the cloud. 5 marks. Use of machine learning to generate

predictions about numerical data from third party
web service(s).

Sentiment analysis. Application of 16:00 10 marks. Sentiment analysis of text data stored in the
sentiment analysis to text data stored in 16/4/21 cloud.
the cloud.

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CST 3130 – Advanced Web Development with Big Data – Coursework 2

Serverless. Back end of website runs in 16:00 5 marks. Back end of website runs in cloud with
the cloud using serverless technology. 16/4/21 serverless technology.
5 marks. Events connect Lambda functions together
for data processing. For example, database updates
trigger lambda functions, which update the sentiment
analysis and push new data to the client. 2.5 marks
per database trigger used for data processing or
sending new data to client.

Cloud hosting of website. All of the front 16:00 5 marks. Website entirely hosted on the cloud, for
end files for the final website are hosted 16/4/21 example, using Amazon S3.
on the cloud. Website can be accessed
through a public URL.

Data visualization. Visualization of data 16:00 2.5 marks. Visualization of third party numerical data
on website using graphs and other 16/4/21 stored in the cloud.
appropriate techniques. 2.5 marks. Visualization of synthetic data.

2.5 marks. Visualization of predictions about third
party numerical data. These predictions must have
been generated by machine learning.
2.5 marks. Visualization of predictions about synthetic
data. These predictions must have been generated by
machine learning.
5 marks. Display of the results of sentiment analysis.

Website design. Design of the front end 16:00 5 marks. How close is the design of the website to
of the website. 16/4/21 professional quality?

Data quality. How much data is shown? 16:00 5 marks. Amount of data and quality of data
How good is the data visualization? 16/4/21 visualization.

WebSockets. The WebSocket back end 16:00 5 marks. WebSockets send data to a single client
must be running in the cloud, for 16/4/21 when it connects.
example, using AWS API Gateway. 5 marks. WebSockets broadcast new data to all

connected clients when the database changes.

Code quality. For example, comments, 16:00 2.5 marks. Typescript code quality.
layout, organization, etc. 16/4/21 2.5 marks. JavaScript code quality.

Project report. Briefly describes the 16:00 2 marks. Screenshot(s) of the front end of website and
project and includes high resolution 16/4/21 the data visualization. The pictures of the data
screenshots of the data visualization. visualization must be high resolution so that we can
Do not include screenshots of code. check your predictions about the synthetic data.

2 marks. Architecture diagram showing the
relationships between Lambda functions, API
Gateway, database, etc. This must be accurate.
2 marks. Content of report. Does it clearly describe
the project? You should explain the machine learning
and sentiment analysis, describe how your lambda

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CST 3130 – Advanced Web Development with Big Data – Coursework 2

functions work and document your database.

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