The Lighting area working within our Unity Editor tribe is hoping to better understand how Creators use their products and features, and have come to you for help. Your task is to help the team validate or refine their questions of interest, explore relevant datasets at our disposal, ask clarifying questions as you progress, and report the main findings and insights back to them at the end. Your primary contacts are the Product Manager (PM) and the Engineering Manager (EM) of Lighting. Both have related, but different questions that are listed below.
Background: What are Lightmaps and Baked Lights in the context of Unity?
- YouTube Tutorial: How to build Lightmaps in Unity 2020.1
- Official Unity Editor documentation: Light Mode: Baked
Questions (sent to you via Slack by the PM)
- How many Creators are using our Lighting tools?
- What user insights do we have on them?
- How much time do they generally spend on our Lighting tools?
- Are they satisfied with our current offerings?
Questions (sent to you via Slack by the EM)
- Do Creators experience any errors when using our Lighting tools?
- How many Creators use “bakeBackend”?
- Which version of “bakeBackend” is used the most?
- How many Creators use the Menu button in the Unity Editor to find our Lighting tools?
- What is the preferred Lightmap size?
Step 1:
Explore and describe the information contained in the sample dataset made available for download here in simple phrases, including any limitations you wish to highlight. Supporting material is provided in the documentation folder.
Step 2:
Visualise and describe your main findings to the PM.
Step 3:
Visualise and describe your main findings to the EM.
Create a local copy of this repository on your laptop and link a personal private GitHub repository to it, adding view access for {emil.jorgensen, deniz.cavas, kiarash.keshmiri}@unity3d.com. You are expected to answer the assignment using one or several Jupyter Notebooks with a suitable combination of headlines, text, code, tables and graphs. Notebooks (.ipynb) should be stored in a dedicated notebooks folder, and any executive findings you wish to highlight can be pasted into a slideshow (.gslides, .pptx) in the current executive slides folder. Lengthy SQL statements (if used) can optionally be placed in the dedicated folder for that.
Storing static datasets on GitHub is not considered best practice. Please avoid uploading the sample dataset (.json) to your personal linked GitHub repository, e.g. by creating and invoking an appropriate .gitignore file.
Expected Tooling: Git, GitHub, Python, and potentially SQL.
You are expected to dedicate the equivalent of a full evening to answering the assignment (i.e. 3-4h), not including time spent setting up your laptop.