BEX5403 · Start-up simulation: Data-driven decision making for success

Overview

In the dynamic landscape of modern entrepreneurship, data has risen to become an indispensable asset for startups. Your ability to grasp, dissect, and extract actionable insights from data can be the ultimate determinant of a startup's prosperity or downfall. This post-graduate unit presents an unparalleled opportunity for you to equip yourself with essential tools and knowledge required to harness the potential of data. By leveraging technology and simulation, you will uncover the means to propel startup growth and ensure that more effective decisions can be made in real time through data-driven strategies. Throughout an intensive 12-week journey, you will immerse yourself in the domain of data-driven decisions and their pivotal role in enhancing startup performance. You will delve into how data-driven insights lay the foundation for well-informed decision-making, bridging the gap between uncertainty and action. Armed with contemporary techniques, cutting-edge tools, and proven methodologies, you will gain the capability to steer startups toward a trajectory of success via real world simulation. This unit takes you deep into the realm of data for start-ups, its significance in startup performance, and empowers you to master the art of crafting data-driven decisions capable of setting startups on the course to success.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. understand the role and the importance of data in start-ups
2. be equipped with foundational data decision making tools
3. become competent in tracking and measuring start-up growth & success
4. be able to perform predictive analytics for proactive decision making
5. carry out ethical data handling and decision making
6. effectively communicate a start-up's data story with stakeholders.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.