In this course, students will learn the process for how to design, build, test, deploy, maintain, and monitor scalable and robust data products using the Data Product Life Cycle (DPLC). Students will gain hands-on experience working with datasets and use cases, collaborating in teams, and applying agile methodologies to deliver data products that meet the needs of real world stakeholders. The course will cover the entire DPLC process, including experimentation and productization, with a focus on reliability, fault tolerance, scalability, deployment, and meeting regulatory requirements. The course will prepare students for careers in data & digital technology, equipping them with the knowledge and skills required to work in cross-functional teams and navigate complex regulatory requirements.

1 hour lecture per week and 2 hours of practical labs per week

NA

Machine Learning H (or equivalent) is recommended but not required

■ 10% Weekly in class quiz to cover knowledge.

■ 50% Repeated single slide presentation on different aspects of the product journey based on weekly topics. *** Each person delivers one slide to be assessed on (developed slide collaboratively) *** 

■ Development of a hypothesis driven experiment

■ Presentation of the rationale for the exploring the hypothesis of business benefits (quality improvement, risk reduction, cost savings/avoidance, increased revenue/margin, customer satisfaction)

■ Link to importance of non-functional criteria

■ Assess both vision/roadmap and execution towards this in implementation and link to non-functional considerations (fault tolerance, data privacy, security.)

■ 40% On the product itself. 

■ Assume a dashboard to be developed

■ Power BI/Tableaux/Cloud deployment

To ensure the students learn about building large scale collaborative data products in an enterprise environment. The course aims to focus on machine learning and large language model development at scale.

By the end of this course students will be able to:

1. Articulate the phases, workflow and key outputs of the data product life-cycle

2. Identify when and how to experiment to prove or disprove a hypothesis

3. Explain how to productionise a successful hypothesis as part of a team across the enterprise

4. Identify, describe and perform the key roles in the Data Product Life-cycle / Data Product Engineering

5. Justify data product design choices aligned with business and regulatory requirements