Does it cost $96k to become a data scientist?

+ an AI advert that actually made me lol, and insights on running an AI startup with only local LLMs

Image by Jezael Melgoza on Unsplash

This week, I’ve been thinking a lot about an excellent Towards Data Science article by Spotify data scientist Khouloud El Alami, I Spent $96k To Become a Data Scientist. Here Are 5 Crucial Lessons All Beginners Must Know.

The article resonated with me because I also spent a lot of money on an expensive data science master’s degree, and I’ve never really known how to talk about the topic of money in DS.

If you want to do the degree I did at Oxford University, it will cost you £27,260 (Home students) / £33,970 (Overseas students). 

That’s enough to buy:

• 🍫 120,000 Freddos (it would have been a lot more before the Cost of Living Crisis!)

• 🅿️ A single car parking space in outer London

• 🎂 16 personalised birthday video messages from Caitlin Jenner via Cameo

• 🎮 1 Nvidia H100 (but good luck finding somewhere that has any in stock)

When I did the course 3 years ago, it was cheaper, and I also had a scholarship which discounted this. But - especially when you factor in the lost earnings from a year of not working in paid employment - overall it was still a huge investment. And honestly, I feel really conflicted about this. 

Is it worth it?

Part of me wants to believe that the high costs are justifiable.

AI professions are in demand and well-remunerated, so the return on investment is good in the long run. You also get a lot of immaterial yet invaluable things out of doing a degree (e.g., friendships, fun, connections, intellectual satisfaction). 

But on the other hand, these courses are just downright unaffordable for many. And I hate the idea that we’re gatekeeping a whole profession behind a paywall.

It’s also a risk - if you do manage to get a DS/AI job at the end of it, you’re set. But if you don’t, then it feels a lot less worthwhile.

Is an AI/Data Science master’s degree even necessary?

In a way, no, it’s not. 

Without a doubt, you could self-teach the content in any data science master’s.

By relying on free resources (+ maybe £1000 on textbooks and courses? I’m not sure) you’d 100% be able to do this. And you’ll probably learn faster because you can just focus on what you want to learn, and won’t have to do compulsory (but less industry-relevant) courses. 

But, on the other hand, I do believe that a master’s degree can be the right choice for some people.

For example, in my case (coming from a completely non-Maths/non-CompSci background), I really felt I needed a prestigious formal qualification. I might have been wrong, but that was how I felt based on the info available to me at the time and the research I did.

But I’ve also met Data Scientists who got into the industry without paying for expensive masters courses, through a combination of self-teaching Python/SQL and getting onto entry-level grad schemes/internships which train you up from scratch. 

Ultimately, I think it’s impossible (and dangerous) to give general advice on paid education which applies to everyone.

Because of the nuances of this topic, I’ve actually written about this before, in 8 Things You Must Consider Before Committing to a Data Science Master’s Degree.

The article is normally paywalled (ironically), but you can view it all for free using that link. And, if you’re wrestling with a decision like this, feel free to send me a message on X and I’ll do my best to help. And let me know what you think in the poll below:

5 things

Anyway, ramblings over! This newsletter is called AI in Five for a reason, after all! 

Here are 5 things to be aware of in AI this week:

1. Cognition AI unveiled Devin, “the world’s first AI software engineer” - I enjoyed this discussion on Reddit r/Machine Learning about how Devin might impact AI/Data professions.

2. Local AI podcast episode of The Bootstrapped Founder - I’d always assumed that startups which utilise LLMs are dependent on APIs (either for accessing models like OpenAI’s, or for accessing their own models (e.g., a fine-tuned Llama2 model) which they’d deployed to servers somewhere). Earlier this week, I listened to Arvid Kahl’s 27-minute episode on Local AI, where he touches on a different model: running an AI startup with local models which are never deployed to production. I found it super eye-opening.

3. How Netflix does ML - Great article from the Netflix ML team, with insights into their MLOps functions.

4. Free SQL course for Data Science - I’ve just finished writing a free SQL course over on my website the-sql-gym.com/courses/sql-zero-to-hero. It’s free, beginner-friendly, and includes a bunch of practice exercises you can do in your browser. Check it out!

And finally, here’s the advert I saw (and loved) on GenAI and the future of work:

Have a great week!

Matt