Startup Growth Engineering (H) COMPSCI4087
- Academic Session: 2024-25
- School: School of Computing Science
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
Short Description
Start-up Growth Engineering comprises the techniques and best practices applied by the world's most successful tech companies to exponentially grow from the start-up phase into large-scale organisations, with millions of users. These techniques have been distilled from best practices employed across start-ups in the Silicon Valley and elsewhere. Start-up Growth Engineering moves beyond traditional product development and marketing activities into an integrated, scientific approach to introducing a new product idea into a market and driving viral user growth.
The Start-up Growth Engineering course combines theory with a large number of practical examples taken from well-known organisations. It equips students with the skills to employ their software engineering and product development skills more effectively, in a real-world environment.
Timetable
Two one-hour lectures and one one-hour laboratory session per week.
Requirements of Entry
Professional Software Development (H) (or equivalent)
Team Project (H) (or equivalent)
This course is available to Honours and MSci students.
Excluded Courses
None
Co-requisites
None
Assessment
Examination 50%, assessed practical exercise 50%
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses
Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
The coursework cannot be redone because the nature of the coursework is such that it takes a significant number of days to produce it and this effort is infeasible for supporting the re-doing of such coursework over the summer.
Course Aims
To familiarise students with the fundamental techniques used by tech companies to drive exponential user growth, including user retention techniques, the different types of compounding growth mechanisms, and optimization techniques.
To equip students with the analytical and strategy skills necessary to analyse real-world start-up growth trajectories and construct predictive and optimisation models to drive user growth.
To enable students to apply these techniques in team situations and to gain an understanding of the typical Growth Engineering team structures and processes employed in Silicon Valley and other leading start-up incubation areas.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. explain and apply the techniques used to create viral user growth in tech companies;
2. create a growth strategy for a start-up, designed to drive exponential growth.
3. explain the key metrics involved in driving start-up exponential growth, and how to optimise those metrics;
4. qualitatively and quantitatively predict the growth performance of a product or start-up based on current growth metrics
5. analyse and explain why some start-ups grow into very large-scale organisations, while others with similar products fail;
6. explain how growth teams are configured inside leading start-ups and the development models used by them.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.