Data Visualization Website
• 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
• 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 must 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
• WebSocket’s will push new data items to subscribed clients.
• The coursework should be based on Amazon Web Services (AWS).
The final submission should look like:
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
• 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.
Word would be ideal file formal for this.
2. Source code. Your source code zip file 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! Zip up your source code and
submit the zip file on the course website.
3. Machine learning files.
Zip up all the files that you used for training and testing the machine learning.
The file should be a Zip file.