Thesis topics from F-Secure

F-Secure is a leading Finnish company providing security and privacy solutions for consumers and corporations. They are interested in Product Analytics in order to leverage data to make their products better. Below is a list potential topics for MSc thesis. If you are interested in any of them, please contact Tomi Männistö.

Note that you would be doing your thesis for the company who would provide a tutor for you. Also, you would need to have a tutor at the department.

A/B testing practises – recommendations what and how to do

At FSC, we are doing A/B-testing, or in general testing, but it’s not yet used as widely as it could be. We would benefit from somebody first investigating on what testing we are doing, and then recommending testing practices that would be suitable for us, taking into account our product development practices.

Expected output:
Testing examples done together with our teams
Documentation describing how FSC should do testing (i.e., not (only) generic testing practices)

Planning & implementing product analytics for a new product

We will be launching new products, and analytics is taken into account, when launching them. However, here too, we could be doing more, and ideas as well as practical analytics work during the early phases of the product life-cycle, would be very beneficial and would add value working together with our product teams.

Expected output:
Analytics done and business relevant insights discovered together with our product team(s)
Relevant documentation explaining what was done as a case study

Analytics tooling selection

We are using several analytics tooling and expect to use even more of them in the future. However, which of the tools should we be using? Are we using all that we should?

Expected output:
Evaluation of different tooling, comparisons and recommendation for the tool set to use
Examples of using at least some of the tools
Architecture, i.e., how the tools are related to each other and the data storages

Data-driven culture – what is it? what does it mean in practise?

Very often people buy into data adding value, but too often the understanding of how to do it in practice is too shallow. We would benefit from somebody that is interested in organization culture, ways of working and habits to describe with concrete practical examples what does it mean to be data-driven. What kind of process/ways of working should there be, what kind of roles (not only analysts, but also the supporting roles), etc?

Expected output:
Documentation describing answers to the above questions and beyond
Comparisons to other companies or literature cases