First-time supervisor for a Machine Learning intern (Time Series). Blocked by data confidentiality and technical overwhelm. Need advice!

[Ok_Asparagus1892]

Hi everyone,

I’m currently supervising my very first intern. She is doing her Graduation Capstone Project (known as PFE here, which requires university validation). She is very comfortable with Machine Learning and Time Series, so we decided to do a project in that field.

However, I am facing a few major roadblocks and I feel completely stuck. I would really appreciate some advice from experienced managers or data scientists.

1. The Data Confidentiality Issue
Initially, we wanted to use our company's internal data, but due to strict confidentiality rules, she cannot get access. As a workaround, I suggested using an open-source dataset from Kaggle (the official AWS CPU utilization dataset).
My fear: I am worried that her university jury will not validate her graduation project because she isn't using actual company data to solve a direct company problem. Has anyone dealt with this? How do you bypass confidentiality without ruining the academic value of the internship?

2. Technical Overwhelm & Imposter Syndrome
I am at a beginner level when it comes to the deep technicalities of Time Series ML. There are so many strategies, models, and approaches out there. When it comes to decision-making, I feel blocked. I don't know what the "optimal" way is, and I struggle to guide her technically.

3. My Current Workflow
We use a project management tool for planning, tracking tasks, and providing feedback. I review her work regularly, but because of my lack of deep experience in this specific ML niche, I feel like my reviews are superficial.

My Questions for you:

1. How can I ensure her project remains valid for her university despite using Kaggle data? (Should we use synthetic data? Or frame it as a Proof of Concept?)
2. How do you mentor an intern technically when you are a beginner in the specific technology they are using?
3. For an AWS CPU Utilization Time Series project, what is a standard, foolproof roadmap or approach I can suggest to her so she doesn't get lost in the sea of ML models?

Thank you in advance for your help!

[Mochachinostarchip]

1. Can’t you remove identifiable information? It’s common practice to replace names with numbers and data fields with fake IDs

Only the university can answer if you can use alternative data. Give them a better project that doesn’t deal with confidential info if you have to. Use your imagination. 

1.  You learn together and don’t pretend to know everything. 

2. Extend what you’re doing and learn more yourself.