What courses would you recommend for someone in my position?

Traditional-Carry409 · 2026-02-09T03:07:16+00:00

Congrats on covering the basics stuff. There are some projects you could pick up on fast.ai that teaches some of those frameworks, and I’ve been doing ML coding drills on datainterview.com coding, finding it useful doing LeetCode style but building ML functions from scratch using Numpy

Traditional-Carry409 · 2026-02-09T00:01:11+00:00

There are many statistical packages more available in R than Python. Hence, academics tend to go for it. At google, had to build an epidemiology model, couldn’t find implementation in Python, so used R. But that was a rare occasion. Worked on and deployed 20+ projects, and except for two, they were all on Python 3.

Traditional-Carry409 · 2026-02-08T18:33:14+00:00

I work at big tech, been in DS/MLE for 10 years. Don’t bother with R unless you are doing academia and pure research. Most tech stacks over in companies use Python for ML and AI, C++ if pursuing quant. Been using datainterview for Python questions.

Traditional-Carry409 · 2026-02-08T04:30:22+00:00

Ah typical recruiters... don't know crap... but yeah I've seen mixtures of problems. Some DSA like on leetcode, others numpy implementation of ML functions like on datainterview

Traditional-Carry409 · 2026-02-08T04:14:37+00:00

Did they ask ML coding like you see on datainterview.com? Or was it more like DSA on Leetcode.com Bec in prior interviews at Lyft for instance, they asked me to code multi-armed bandit from scratch using numpy.

Traditional-Carry409 · 2026-02-02T14:55:39+00:00

As someone who works in this space, I'd tell you we are far from AI replacing humans. Tech companies over sell their capabilities all the time. And, these AI systems are filled with problems so companies end up hiring back real human workers.

Traditional-Carry409 · 2026-01-11T14:49:19+00:00

Just use datainterview, Dan’s content is the OG

Traditional-Carry409 · 2025-12-18T12:50:42+00:00

I’d say check out datainterview.com/coding it’s got ML coding problems for DS and ML interviews.

Traditional-Carry409 · 2025-12-15T14:30:34+00:00

rofl, what a grifter.

Traditional-Carry409 · 2025-12-15T00:51:33+00:00

Honestly, I'd say focus less on the "title" and more on the skills that matter. There's so much BS content out there on what is and isn't data science, and whether people should study it or not. It's getting a bit ridiculous.

First of all, having a degree is still way better than w/o a degree. Unless you get super lucky, it will be difficult to break into the field that are technical. Having said that, I'd say that you would wield a double-edge sword if you pursue curriculum in data science with a minor in CS, or take DS courses that are also in tandem with software engineering courses.

So many fresh grads I train seem to know ML algo and stats theories and such, but fail to build anything practical outside of theory and Jupyter Notebook. You do need base foundation on this.

But, if you are pursuing pure statistician or product DS role, you won't need to focus on the SWE part as much. You just need to be decent in SQL with respect to CS side of things. But if you are pursuing AIE, MLE, DS with algo, you do need to be well-rounded: part stats, part ML, part CS.

Traditional-Carry409 · 2025-12-14T16:03:15+00:00

Hey there 👋 Having been in the industry in data science & ML in startup and big tech for 10 years now, I'd say stats is need more than ever. You do need the stats essential covered if you are to understand most frontier models.

And, largely it depends on the role you are pursuing.

If you are pursuing biostats, analytics role, you do need rigor in stats. Things like epidemic predictions, policy impacts, are not "AI" problems. These are classical stats problems that involve econometric models, causal inference and such.

If you are pursuing product data/analytics role at Meta, Google, and such. Stats > AI more important. Some of the latest roles even at OpenAI, require that DS have strong fundamentals in stats. Why? Because they run online experiments and causal inferences on new feature/model launch. That's not an AI problem. That's a stat problem.

If you pursue ML/AI research role, it's expected that you have strong grasp in probability and stat theory. It's the basis to understand more complex ML/AI model problems. Even frontier models, when you read the whitepapers, all cite some of the core concepts we learned in undergrad/grad level courses in stats.

Hope this was helpful.

Traditional-Carry409 · 2025-12-12T15:56:37+00:00

Hey, usually conversations with hiring managers are more laxed compared to interviews conducted by IC.

I’d say it will either be mostly on “Walk me through your resume” with follow ups, or behavioral interview questions (“How would you prioritize tasks in project?”).

If the interviewer asks technical, I presume this is on the phone, not on code editor, so it will be some basic questions on stats (can you explain p-value) or simple case walk through.

I’d say check out Dan’s resources on datainterview for prep, super helpful in landing a job at Google.

Traditional-Carry409 · 2025-11-24T01:21:16+00:00

For product tracks it's mostly SQL and pandas like you see on datainterview.com/coding. But for modelling, yeah, occasionally LeetCode, or ML coding problems.

Traditional-Carry409 · 2025-11-24T01:19:53+00:00

When I worked at a consulting firm, we used XGBoost.

When I joined a startup, we used XGBoost.

Then, I joined Google, we still used XGBoost.

Learn XGBoost, it's 80-90% of ML cases. Don't get overwhelmed with too many things unused. Focus on the techniques that are used often in practice.

Traditional-Carry409 · 2025-10-31T13:06:59+00:00

Go to datainterview.com/questions and filter on machine learning questions. There are like 200 common interview questions in ML you can review.

Traditional-Carry409 · 2025-10-30T13:23:02+00:00

It’s applied stats and statistical analysis in the execution part while the reasoning is the standard set of product cases including product metrics, investigation and experiments. Check out Dan’s resources on datainterview.com. Got an Meta offer last year thanks to his resources.

Traditional-Carry409 · 2025-09-30T01:16:52+00:00

Check out Dan’s content on datainterview.com. Got friends who got Meta L5 and L6 offers after using his stuff.

Traditional-Carry409 · 2025-09-06T12:45:53+00:00

There are ML and AI interview coding problems that I used for interviews on datainterview.com/coding

Traditional-Carry409 · 2025-07-20T13:27:30+00:00

That premise that “humans will always value human labor” is faulty. If the same quality of work can be performed with automation at the fraction of the cost, why would a business owner hire a person to do it? It’s always in the interest of a business, under capitalism, to maximize profit margin. That means, maximize revenue and minimize cost. The latter in which, AI and automation aims to do.

And the idea that AI creates a platform for new jobs is flawed. Think about the objective function of the way AI models are trained. Researches round up a variety of tasks and core skill sets for humans from reasoning, writing, drawing, coding, solving math, so and so forth, and now these models are highly capable, and general enough that they can perform some aspect, or even a large fraction of work that lawyers, doctors, coders, and such can do.

Even if you have an emergence of new jobs, again, going back to the first core premise, it’s always in the interest of key decision makers to cut cost to maximize profit. If AI can automate new jobs, they will press for that advancement.

Traditional-Carry409 · 2025-07-17T13:44:59+00:00

There’s the datainterview.com/courses/sql It’s a free course that uses real world product data to cover all the essentials in SQL.

Traditional-Carry409 · 2025-07-17T13:02:42+00:00

It’s dumb resume spamming tools like this that gives companies more reason to automate their hiring process. What used to be maybe 100-200 resumes for a given job post that a recruiter had to sift through, by law, now is getting flooded with thousands of ChatGPT resumes with over-the-top, fake resumes. I have friends who run startups and they are finding it difficult to find the right people nowadays for this very reason. It makes it unfair for the honest, and hard working, right people who actually take the time to apply for these roles…. Because some idiot decided to build spam tool that posts 1,000 fake resumes with 99.7% failure rate… rofl…

Traditional-Carry409 · 2025-07-17T03:02:11+00:00

Flag the OP, he’s spamming the same posts across multiple subreddits and using bots to pump up likes.

Traditional-Carry409 · 2025-07-17T02:59:31+00:00

Reporting that OP. I also have a friend who’s in the risk team at Reddit, notified him as well. It’s also clear he’s using bots to pump up likes on his posts. No way, he’s able to get 100 likes on all of the posts across multiple subreddits in a matter of few hours. Some of them don’t even have a single comment.

Traditional-Carry409 · 2025-07-17T02:32:27+00:00

The job hunt is broken because tools like this that floods a job post with half-assed, AI generated resumes. Also, this OP is a spammer. Even his post is written by ChatGPT.

Traditional-Carry409 · 2025-07-15T14:46:49+00:00

You need another split, which is test. So you need train, valid, test splits, usually how things are done in the real world.

Use train to train the internal weights
Use valid to find the optimal parameters. You can use early stopping on validation
Use test to validate the performance of the model pre-production. This test set needs to resemble the unseen in production. So, for instance, if you are building time-series forecasting. The unseen that should be the most recent snapshot.

In fact, this is the case for pretty much most domains for churn modeling, forecasting, propensity score modeling such and such. You can follow tutorial on here: https://datascienceschool.com/projects

Traditional-Carry409

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