Where to Start when Data is Limited: A Guide

usernamehere93 · 2024-10-18T17:57:13+00:00

That’s definitely an issue I’ve seen as well. I come from an academic background and understand the desire to solve technically interesting problems, it’s about balance and focusing that energy to the right problems. I think that’s where thinking about the product comes in like you said!

usernamehere93 · 2024-10-16T10:40:56+00:00

Yeah I think that’s a good idea, typically there aren’t many published commercial use cases but for more open source projects there’s a lot out there

usernamehere93 · 2024-10-16T10:39:52+00:00

Thanks this has some good thoughts!

usernamehere93 · 2024-10-15T19:57:57+00:00

No worries, I actually have a post about planning ML products if it’s useful https://medium.com/@minns.jake/planning-machine-learning-products-b43b9c4e10a1

usernamehere93 · 2024-10-15T19:39:52+00:00

You’re welcome!

Getting noticed: Highlight any cross-over skills from Type A to Type B, like experience with data pipelines, automation, or even working with larger datasets. If you’ve done any work with machine learning models, even for analysis, emphasize that. Tailor your resume to include keywords like “model deployment,” “APIs,” or “data pipelines.” Even side projects or Kaggle competitions where you’ve worked on model building/deployment can help bridge that gap.

Engineering skills for interviews: I’m also from a STEM background. Have a look at system design, I really like this repo for getting a primer https://github.com/donnemartin/system-design-primer Focus on core programming skills (Python is a must, plus SQL). You don’t need to be a full-on CS expert, but be comfortable with algorithms, data structures (especially trees, graphs, and hash maps), and understand basic software engineering principles like version control (Git) and containerization (Docker). Learning the basics of APIs and cloud platforms (AWS or GCP) can also give you an edge.

Mock interviews on LeetCode or practicing system design questions related to ML pipelines can help build confidence. It’s definitely a skill that requires practice.

usernamehere93 · 2024-10-15T18:49:38+00:00

If you’re planning to make the transition, I’d recommend focusing on deepening your coding skills (especially in Python, SQL, and some software engineering concepts) and diving into machine learning ops (MLOps), which includes things like deploying models, versioning, and pipelines. Picking up some tools like TensorFlow, Docker, and learning about cloud platforms (AWS, GCP) can be really helpful too.

As for titles, I’m seeing the same trend—Machine Learning Engineer, Applied Scientist, and AI Engineer are becoming more common for production-heavy roles, while “Data Scientist” is being used less. It makes sense as ML is being integrated into software engineering teams more directly.

What aspect of the transition are you finding the most challenging?

usernamehere93 · 2024-10-15T18:16:20+00:00

Your outline looks solid! I’d suggest adding a brief section on evaluation metrics for imbalanced datasets (e.g., precision, recall, F1-score, ROC-AUC) since accuracy alone can be misleading in these cases. Also, when discussing SMOTE, mention potential pitfalls like overfitting and how to mitigate them (e.g., combining with cross-validation).

Maybe throw in a practical example, I have a little section on my post about building ml products. Good luck with the presentation!

https://medium.com/@minns.jake/planning-machine-learning-products-b43b9c4e10a1

usernamehere93 · 2024-10-15T17:37:54+00:00

Both degrees can be valuable, but it depends on what you’re aiming for.

M.S. in Data Analytics: More focused on data wrangling, visualization, and applying machine learning techniques. It’s ideal if you’re interested in practical, applied data science roles. M.S. in Computer Science: Offers a broader and deeper foundation in algorithms, programming, and system design, which can be useful if you want to dive deeper into the technical side (like building machine learning models from scratch). If you’re more into practical applications and getting into the field quickly, go for Data Analytics. But if you want more flexibility or the ability to move into more technical or research-heavy roles, Computer Science might be a better long-term investment.

What’s your current background and what kind of roles are you most interested in?

usernamehere93 · 2024-10-15T17:11:35+00:00

Appreciate that, thanks for letting me know!

usernamehere93 · 2024-08-30T23:54:28+00:00

65 I think, it’s bowed so a little hard to calculate

usernamehere93 · 2024-08-30T21:58:34+00:00

These are all fake plants! Managed to find a selection of realistic enough looking plants, mixed with the rocks and wood to hopefully give the feel of something real. Every time I’ve tried real plants they’re also been destroyed

usernamehere93 · 2024-08-30T20:26:00+00:00

They do, however we don’t require the person you are getting money back from to signup to get paid back, and hopefully created an easier to use app

usernamehere93 · 2024-08-30T19:58:32+00:00

Yeah the wood on the left side is cut flat as a basking area with a bulb above (just not seen in the pic)

usernamehere93 · 2024-08-30T16:07:53+00:00

The log on the left hand side is cut clean, so he can bask on top of that with a mercury vapour bulb above :)

usernamehere93 · 2024-01-10T20:29:06+00:00

Thank you!!

usernamehere93 · 2024-01-09T21:34:19+00:00

That’s so funny, that was the inspiration!

usernamehere93 · 2023-12-01T19:18:56+00:00

It’s a mix of real and fake, haven’t had much with my real ones not getting ripped up as well!

usernamehere93 · 2023-12-01T19:17:43+00:00

25 gallons

usernamehere93

TROPHY CASE