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Amazon data science interview questions

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Re: Amazon data science interview questions

#71

Post by flamedoge »

pmiller2 wrote: post #46
gaius wrote: post #37

Earlier quoted context omitted.

Well yes. Before the trendy buzzword, machine learning was known simply as predictive statistics.

No idea why this was downvoted; I find this to be an accurate description. The difference between stats and ML is mostly one of terminology and perspective. I studied math in college and grad school, and there were several moments in Andrew Ng's online lectures where I thought "oh, I know this, but we didn't call it that, and I had no idea it was considered ML."

reminds me of the whole Bayesian perspective of ML

Re: Amazon data science interview questions

#72

Post by zaptheimpaler »

beebmam wrote: post #68

As an interviewer, I don't ask questions like these. By a very large margin, for most of the time you are actively developing as a full time employee at a large tech company, you're working on integrating systems, either through a build language or extending your software to use an already existing API. What are the most useful skills for these tasks? Being able to communicate effectively, feeling comfortable asking…

A company full of people great at communication but too little technical expertise will fail. I think it takes both types - technical gurus who can generate useful information and solve the harder problems, plus connectors/communicators who gather information.

Re: Amazon data science interview questions

#73

Post by deong »

cirgue wrote: post #55
hackernewsacct wrote: post #4

If you obtained a degree in Computer Science and specialized in Machine Learning are you suppose to be able to answer these questions? What job specialization is this aimed for? Almost strikes me more as a statistical based interview.

Machine learning systems that perform a job and make money are still overwhelmingly stats-based, and the knowledge required to understand, tune, and optimize any ml system, regardless of design, are based on statistics and always will be.

I'm not sure I wholly agree there. There's an awful lot of real work being done that makes real money based on deep learning, and deep learning comes from the CS "it works, what's the problem" side of the field as opposed to the statistics "I want formally grounded theory for everything" side.

One of the big pushes in Bayesian statistics recently has been to try to figure what the hell all these neural nets are actually doing. It's certainly not the case that the stats have been in the driving seat there.

Re: Amazon data science interview questions

#74

Post by dapreja »

ImSkeptical wrote: post #66
ben_jones wrote: post #63

Earlier quoted context omitted.

I've never understood how there is a "right way to think about something". Isn't it an attribute of humanity that we all think and approach problems differently ? Are companies really looking for one single type of individual to clone across all their engineering teams?

I believe it's not that there is a single right way to think about a problem, but there probably are wrong ways to think about the problem. For example, going through all the issues with the question and implicit underlying assumptions, talking about what you'd need to give a good answer, or the characteristics a good answer may have might not be exactly what the interviewer expected or was looking for - but such an…

The reality is, unless you're directly communicating with someone who understands the problem/question, they'll be expecting the textbook definition as is or whatever stackoverflow response has.

Re: Amazon data science interview questions

#75

Post by gaius »

zaptheimpaler wrote: post #72
beebmam wrote: post #68

As an interviewer, I don't ask questions like these. By a very large margin, for most of the time you are actively developing as a full time employee at a large tech company, you're working on integrating systems, either through a build language or extending your software to use an already existing API. What are the most useful skills for these tasks? Being able to communicate effectively, feeling comfortable asking…

A company full of people great at communication but too little technical expertise will fail. I think it takes both types - technical gurus who can generate useful information and solve the harder problems, plus connectors/communicators who gather information.

I get the feeling at many companies now, there are fewer truly interesting technical problems than there are engineers enthusiastic or desperate to exercise their hard-won CS knowledge. Most real-world work of commercial value requires skill of course, and experience, but is not especially algorithmic. I think the surge of interest in DS is that here people see an opportunity to do something mathematical day-to-day (but 10% or less of DS is the "sexy" stuff).

Re: Amazon data science interview questions

#76

Post by gaius »

Xcelerate wrote: post #62
rafinha wrote: post #36

I have mixed feelings concerning this methodology. At the same time I feel I'm a bit rust on the details of the field, I still believe best way to assess academic background is by sending a paper "offline" and asking the interviewee to explain it. IMO all these quick-answer questions asses is how prepared is the interviewee in answering quick-answer questions.

Agreed. I have horrible short term memory but these questions are trivial as a "homework" type assignment. Plus, for my current data scientist role, the interview consisted of me bringing in a project I had worked on (actual code) and demonstrating how it worked. I much prefer that tactic. I think it gives you greater insight into the candidate.

bringing in a project I had worked on (actual code

I don't think commercial confidentiality makes that a viable generic method...

Re: Amazon data science interview questions

#77

Post by metaobject »

gaius wrote: post #75
zaptheimpaler wrote: post #72

Earlier quoted context omitted.

A company full of people great at communication but too little technical expertise will fail. I think it takes both types - technical gurus who can generate useful information and solve the harder problems, plus connectors/communicators who gather information.

I get the feeling at many companies now, there are fewer truly interesting technical problems than there are engineers enthusiastic or desperate to exercise their hard-won CS knowledge. Most real-world work of commercial value requires skill of course, and experience, but is not especially algorithmic. I think the surge of interest in DS is that here people see an opportunity to do something mathematical day-to-day (…

Much of the work I do that could be considered "data science" involves trying to write generic visualization tools that allows us to plot various datasets and the results from models we've built (a lot of 2D/3D/4D weather/atmospheric data).

Re: Amazon data science interview questions

#78

Post by mamon »

stale2002 wrote: post #39
chronic6h2 wrote: post #31

Earlier quoted context omitted.

As long as these riddles keep the web devs and infra engineers away from machine learning, I'm all for it. We must preserve the elite reputation associated with machine learning; not watered down by avergae engineers looking for a career change.

People have been doing statistics for decades, which is mostly all that machine learning is. Coming up with a fancy new buzz word for stuff that has been for decades, doesn't make something "elite".

You know, statistics is a branch of mathematics, and mathematical education itself can be considered "elite", if for no other reason then for the irrational fear it induces in most of people.

Most of high school graduates decides to pursue more "practical" topics like CS, leaving mathematics to those chosen few who dared to study it :)

Re: Amazon data science interview questions

#79

Post by ju-st »

soVeryTired wrote: post #43

My quick and dirty answers are below. I'm thinking of moving jobs within the next year so I could use the practice. Can anyone do better? > How do you treat colinearity? Throw away the redundant part of the data > How will you deal with unbalanced data where the ratio of negative and positive is huge? This is very problem-dependent, but it's got the potential to wreak havoc with your learning algorithms. You might ge…

2 things I would like to add to the already very good answers in this thread:

> How do you treat colinearity?

Look at feature interactions when using decision tree based models to discover if some interactions between seemingly similar features could improve the performance of the model.

> How will you deal with unbalanced data where the ratio of negative and positive is huge?

Don't use accuracy or other "averaging" evaluation metrics. When looking at unbalanced data you are likely interested in the minority class so I would use metrics that only look at this class for performance evaluation.

Re: Amazon data science interview questions

#80

Post by alexcnwy »

RA_Fisher wrote: post #24
prestonh wrote: post #5

Can't say I could answer all these questions, but I think they're really great and motivate me to spend more time studying statistics. With the rise in data science and machine learning I've noticed a lot of resources devoted to teaching people how to create and train models for a given problem, but less resources on how to interpret those models and on statistical inference in general.

It's quite rare to find folks in the industry with formal education in statistics. The folks that have it can run circles around everyone else, though. Data Science and Analytics and Machine Learning all use statistical theory when boiled down.

I find it curious that machine learning is a branch of computer science given how much of it is actually statistical learning theory.

The go-to graduate level machine learning text "The Elements of Statistical Learning" was written by 2 statistics professors (one, Prof. Hastie, a fellow South African! :)

Granted, neural networks are often taught as a bolt-on lecture in statistics course machine learning modules but topics like regularization and a rigorous study of overfitting were born in stats departments.

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