Data Driven Product and Service Design

With the continuous development of the Internet to meet the growth of smart cities and the upcoming 4th industrial revolution, technologies such as IoT (Internet of Things), cloud computing, artificial intelligence, blockchain, immersive technology, and big data are increasingly being integrated into the fabric of our lives and works. These technologies pose tremendous challenges to industries and organisations in developing data driven products and services. How to adopt them for digital transformation of the business to keep up with the market to meet needs and demands is becoming a top priority for management.

Data driven product is built on product co-ownership, in which the business and users share product usage information throughout the product life cycle to create values for both sides. The data product will continuously generate data about its users and its use. These data products start with a set of assumptions about their users. Through user interactions, the assumptions will be verified and validated, resulting in improvement in product features to serve better user experience.

As smart, connected, and data driven products and services become more prevalent in this coming decade, knowing the sources of data and acquiring them programmatically for further cleaning, filtering, aggregation, modelling, evaluation, visualisation, and on-demand interaction are crucial for achieving high productivity, business excellence, and customer satisfaction. This course aims to help students understand the context of this development and equip them with the required research, design, coding and testing skills for pursuing a career in the field.

Expected Learning Outcome
After taking the course, students will be able to:
1) Conduct UX/UI research to define product requirements in the context of product management life cycle.
2) Transform requirements into design prototype using Figma.
3) Master fundamental front-end (HTML/CSS/JS) and back-end (Python/SQL) coding skills.
4) Perform web scraping in ParseHub and Beautiful Soup and data cleaning in Open Refine and Pandas.
5) Perform usability studies and A/B tests using Google Analytics, Google Optimize, and Google Tag Manager for evaluating user adoption.