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Comment by tfgg | Hacker News Reader

Based on recent (successful) job interviewing, I'd recommend people looking for a job in data science/ML to do a statistical learning course such as the Hastie and Tibshirani Stanford one [1] as a higher priority over ML/deep learning courses. It gives you a base level of knowledge in the field, and even for jobs that do deep learning, most of the technical questions will be about making sure you know the classical concepts really well.

[1] https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...

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http://statweb.stanford.edu/~tibs/ElemStatLearn/

Everyone keeps linking ESL, but really ISLR is much easier to understand, provides more important clarifying context, and covers more or less the same information. ESL is more like a reference and prototype for ILSR

URL?

http://www-bcf.usc.edu/~gareth/ISL/ full text download link: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing....

http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.p... is the download link for the newest printing.

http://www-bcf.usc.edu/~gareth/ISL/

[edited] I would hire you tomorrow if you come from a quantitative background, know at least ISLR inside and out, and can communicate in a professional manner.

ESL or ISLR?

Excellent.

Excellent. I just started reading it. Shall we catch up in about 2 months?

Sure.

I'm currently a software developer - feel free to reach out at jonschoning at gmail dotcom

Thanks for posting this

How difficult is it to lend AI engineer/Data engineer (fresh grad) position for someone without masters/Ph.D? What do you recommend to person like this?

Unless your experience is exceptional and you are acing interviews left and right, I'd recommend getting a Masters at minimum. The unfortunate reality is that few will take you seriously without at least one advanced credential above a BSc.

While I disagree with stuxnet79 about needing an advanced degree, you'll likely not find much without some kind of experience. In lieu an MS or PhD, you may want to start in entry-level development at a shop that also has some machine learning, big data analysis group and work your way in their over the course of a few years. After a few years you may be in a better position, experience-wise, than many M.S. grads i've seen.

Goodluck

To add some evidence - I co-chair PyDataLondon (3,000 members, UK's largest Python u/g, UK's most active data science group). I survey our members, our monthly attending group are 40% PhD, 40% MSc, 20% other, few have 5-10 yrs industry experience, the majority have 2-4 years. I'd argue that you need at least a relevant MSc + a couple of years experience to begin to talk of being a data scientist/AI engineer. Coming through data engineering in support of data science is a great route to get practical experience where there's a lot of job demand, at least in London.

Thanks for input. I have a BS in CS. I took couple of AI/ML centric courses in my undergrad. I worked on couple of ML centric open source projects, one of them featured on the front page of HN. And I've good stats on kaggle also. I'm applying for a job in top ML firm. I'm fresh grad. Should I apply for Software engineer or research engineer/Data scientist? Is my experience enough for research engineer/Data scientist?

Thanks!, I have a BS in CS. I took couple of AI/ML centric courses in my undergrad. I worked on couple of ML centric open source projects, one of them featured on the front page of HN. And I've good stats on kaggle also. I'm applying for a job in top ML firm. I'm fresh grad. Should I apply for Software engineer or research engineer/Data scientist? Is my experience enough for research engineer/Data scientist?

Its hard to say as I don't know your complete background or the level of the role at this firm. If they're at top ML firm and you're applying for a research engineer/Data scientist role then you're probably competing with a hefty bunch of experienced candidates (many of which i'm sure are on Kaggle too). If you're still lacking a background with professional experience then i'd suggest starting at a lesser role to get yourself in the door.

Thanks for help. That makes sense. I guess I'm gonna apply for software engineering role.

With no experience you will need at least a Masters. Last I checked half of data scientists in the industry held a PhD and the other half held a Masters degree. You can get away without it if you somehow have significant experience in the field (experience always trumps everything else), but that's pretty rare.

Sorry, didn't see this until now.

It's hard for me to say without knowing which firm or reading the job description, but to me research engineer implies a PhD level of knowledge. You won't get that from some open source projects and kaggle competitions.

CS109: Introduction to Probability for Computer Scientists http://web.stanford.edu/class/cs109/

also a great resource.

I'd be really interested in your background, the kind of projects you did and the kind of positions you applied for (I'm trying to switch to ML/data science)

That's nice to hear! I'm doing my masters thesis on statistical learning and I often think how un-glamour this field now is. No bayesianism, less engineering, but having probabilistic guarantees on your out of sample results as well as sample complexity, no matter the underlying distribution, can be quite beneficial.

If you have time, I'd also read something like David Mackay's information theory textbook [1] for more of a Bayesian perspective. Interviewers did seem to appreciate having multiple perspectives and interpretations of basic results, though less practical.

[1] http://www.inference.phy.cam.ac.uk/mackay/itila/

Given you're making recommendations on the topic of statistics, exactly how many companies did you talk to reach the recommendations you're providing and what if any bias was there in your job search?

Good points. I'm coming from a computational physics research background and applied for a few data science positions at startups, a large social network and a private ML research group, so not that many overall, beware of the small sample size.

The smaller startups seemed to want more "data engineering" experience.

What do you mean by "what if any bias was there in your job search"?

having probabilistic guarantees on your out of sample results as well as sample complexity, no matter the underlying distribution

What technique are you referring to?

Using concentration inequalities on Lipschitz convex learning algorithms to derive generalizing bounds. The seminal papers for this would be Stability and Generalization by Bousquet and Elisseef (2002), or those by Shalev Schwartz.

Tagging this thread for future use.

My wife is a PhD Data Scientist, and I'd like to at least have a basic level of understanding theory, processes and tools used in her field.

Protip: https://news.ycombinator.com/saved?id=balls187&comments=t

Thank you!

You're very welcome!

I can't see this link, what is it?

It's a link to his personal saved comments on HN. You can't see it because it's for his username.

Yours would be https://news.ycombinator.com/saved?id=catilac&comments=t (access it by clicking on your username on the top right then "saved comments")

ditto (except the wife part)

Are the lectures for that available outside of the regularly scheduled course offerings? I'm interested in coming at ML from the statistical direction.

http://www.r-bloggers.com/in-depth-introduction-to-machine-l...

I see in the link an empty course info page. Is it an online course that requires a login?

Sorry, I think this works: https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...

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