CETM46 – Data Science Product Development - Computer Science Assignment Help

Assignment Task :

Assignment 1 of the summative value of the module
Extract from module descriptor – The module will be assessed by two coursework assignments, which will cover related learning outcomes of the module respectively. In this second assignment (2 of 2), students will develop a data science product prototype and write up a report about the design, development and management of the prototype development project.

LEARNING OUTCOMES ASSESSED
Knowledge
1. Utilisation of the state of art of data science methodologies and software tools for data analysis applications development
2. Critical understanding of modern data science systems and their ecosphere Skills
3. Ability to design and develop data science systems using various data repositories and data models
4. Developing data science products with modern data systems, visualisation technologies, software tools and their ecosphere
 

ASSIGNMENT INTRODUCTION
As a data scientist at a data science start-up company, you are tasked to design and develop a bespoke data science product for a specific application domain as part of an individual R&D project. The end users of your data science product have no or very limited knowledge of data science technologies, but expect to install, deploy, and use your data science product for their company or organisation in an easy and user friendly style.

Section 1 Product Design

• Critically discuss the design of data science product, including:
•  Data source and theme selection and specification
•  Application domain/end user’s requirements analysis
•  Product functional and non-functional requirements specifications
•  Product software architecture design
•  Product use case specifications

Section 2 Product Development

• Critically discuss the development of the data science product, including:
•  The selection of appropriate software tools/platforms and hardware methodologies (e.g., Mobile/BYOD, VR/AR)
•  Product development software engineering methodology (e.g., Waterfall, Agile/Rapid Prototyping)
•  System testing method
•  User evaluation plan and methods
   

Section 3 Project Management

• Critically discuss the project management the data science product, including:
•  Time management with Gantt Chart
•  Risk assessment on personal information protection and data security/governance
•  Quality control on software development
•  Basic Customer/User relationship management
•  Basic Product marketing strategy

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