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The State of Data Science and Machine Learning

kaggle.com

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Re: The State of Data Science and Machine Learning

#11

Post by huac »

antgoldbloom wrote: post #4

For interest, the raw data is published here: https://www.kaggle.com/kaggle/kaggle-survey-2017 And some early analysis from our community here: https://www.kaggle.com/crawford/analyzing-the-analyzers Some things that jumped out at me: 1. more people learn data science and ML from MOOCs than university courses 2. Tensorflow the tech people most want to learn in the next year 3. 40% of people survey spend >1-2 hours pe…

> 1. more people learn data science and ML from MOOCs than university courses

More people, out of the subset of people on Kaggle. Lot of selection bias there!

Re: The State of Data Science and Machine Learning

#12

Post by apohn »

antgoldbloom wrote: post #4

For interest, the raw data is published here: https://www.kaggle.com/kaggle/kaggle-survey-2017 And some early analysis from our community here: https://www.kaggle.com/crawford/analyzing-the-analyzers Some things that jumped out at me: 1. more people learn data science and ML from MOOCs than university courses 2. Tensorflow the tech people most want to learn in the next year 3. 40% of people survey spend >1-2 hours pe…

>40% of people survey spend >1-2 hours per week searching for another job. Surprising given all companies complain about the difficulty in finding data scientists/machine learners.

I've hired data scientists in the past. One thing I found is that a lot of interviewees want to talk about all the algorithms (e.g. Gradient Boosting) they've used and are not able to describe how they thought through the problems before they applied the algorithms. It's easier to find somebody who downloaded some mostly clean data, then copy/pasted some code than a person actually thinks through the quantification of a problem. There are a lot of buzzword artists out there.

This is important because in a lot of organizations the business problems have not yet been quantified in a way that lends itself to getting meaningful and valid results from an algorithm. The Data Scientist has to be able to work with others to quantify a problem. Or at a minimum, recognize that there are issues with the current way the problem is quantified and think of ways to improve it. It's much easier to teach somebody to run a data algorithm than it is to actually understand a business problem.

There are issues with people doing the hiring as well. In my last job (not a software company), the VP of the group had pushed to get headcount for a data science team and was fearful of making the wrong hire because he didn't want to say "We hired a data scientist at 2X-3X the cost of a Business Analyst and that was a bad hire." The end result was a massive amount of paralysis, an insanely long and convoluted job description, and complaints about the hiring pipeline.

Re: The State of Data Science and Machine Learning

#13

Post by kmax12 »

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together.

In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting data ready for the algorithms. While there is a question that lets respondents pick which of 15 different modeling algorithms they use, there's nothing that talks about what technologies people use to deal with "dirty data", which is agreed to be the biggest challenge for data scientists. I think more formal study of data preparation and feature engineering is too frequently ignored in the industry.

Re: The State of Data Science and Machine Learning

#14

Post by sidlls »

antgoldbloom wrote: post #4

For interest, the raw data is published here: https://www.kaggle.com/kaggle/kaggle-survey-2017 And some early analysis from our community here: https://www.kaggle.com/crawford/analyzing-the-analyzers Some things that jumped out at me: 1. more people learn data science and ML from MOOCs than university courses 2. Tensorflow the tech people most want to learn in the next year 3. 40% of people survey spend >1-2 hours pe…

All of these things coupled with the top complaints data make a lot of sense if one views the items you list as identifying a set of people who are mostly self-taught, have a perhaps insufficiently broad and deep quantitative background, and are looking to merely catch the "data science wave".

Re: The State of Data Science and Machine Learning

#15

Post by benhamner »

kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

Completely agree. Data quality issues was a big part of our motivation with Kaggle Datasets (an open data platform where the quality of the dataset improves as more people use it) and Kaggle Kernels (a reproducible data science workbench that combines versioned data, code, and compute environments to create reproducible results).

Two examples of this: Kaggle Datasets supports wiki-like editing of metadata (file and column descriptions) and makes it easy to see, fork, and build on all the analytics created on the data so far.

We're just getting started with each of these products: we want Kaggle Datasets to support a fully collaborative model around working with all your data in the future, and Kaggle Kernels to support every analytics and machine learning usecase.

Re: The State of Data Science and Machine Learning

#16

Post by VHRanger »

kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

> There is so much focus on the machine learning algorithms rather than getting data ready for the algorithms.

Generally, once a problem at work has come to the point of being a "kaggle problem", it's trivially easy. The main problem is unstructured data, with infinite ways of specifying similar ways to measure the same attribute, and lots of leeway to build an unmaintainable data pipeline between the data generation process and the model at the end.

Re: The State of Data Science and Machine Learning

#17

Post by zebrafish »

apohn wrote: post #12
antgoldbloom wrote: post #4

For interest, the raw data is published here: https://www.kaggle.com/kaggle/kaggle-survey-2017 And some early analysis from our community here: https://www.kaggle.com/crawford/analyzing-the-analyzers Some things that jumped out at me: 1. more people learn data science and ML from MOOCs than university courses 2. Tensorflow the tech people most want to learn in the next year 3. 40% of people survey spend >1-2 hours pe…

>40% of people survey spend >1-2 hours per week searching for another job. Surprising given all companies complain about the difficulty in finding data scientists/machine learners. I've hired data scientists in the past. One thing I found is that a lot of interviewees want to talk about all the algorithms (e.g. Gradient Boosting) they've used and are not able to describe how they thought through the problems before t…

I agree with your assessment that a lot of times the business problems have been put into a form that lends itself to exploitation by machine learning. Sometimes a company has a lot of data that's actually useless.

Most of the time, I've found that business people do not understand the value of data. Often I hear, "we have this data set, let's unleash the data scientist on this to tell us something." or "we have this data set but what are the so-whats here?".

I spend a lot of my time explaining that there must first be a business objective, a key question, or hypothesis that can then be understood through data. I cannot take a haystack and find the needle that is interesting to you. And if I do find that needle, many times there are no resulting changes made to our strategy.

I think we're still in a place where the value in a data scientist is not that she knows how to write: fit

The value exists when she can take a problem from the business, understand how to find a solution with data, and then convey that back to the business in a meaningful way that allows them to easily understand how they can make changes to positively impact the bottom line.

Re: The State of Data Science and Machine Learning

#18

Post by sidlls »

VHRanger wrote: post #16
kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

> There is so much focus on the machine learning algorithms rather than getting data ready for the algorithms. Generally, once a problem at work has come to the point of being a "kaggle problem", it's trivially easy. The main problem is unstructured data, with infinite ways of specifying similar ways to measure the same attribute, and lots of leeway to build an unmaintainable data pipeline between the data generation…

I disagree that a "kaggle problem" style problem is trivially easy, but I strongly agree with the sentiment that dealing with unstructured data is often a much bigger, deeper, and broader problem than the choice of a particular algorithm or ensemble of them.

The ability to efficiently and effectively derive insights from such data is scarce.

Re: The State of Data Science and Machine Learning

#19

Post by benhamner »

VHRanger wrote: post #16
kmax12 wrote: post #13

In "What barriers are faced at work?", I really wish they broke down the "dirty data" response into more categories. In particular, I'd love to know if people are dealing with data quality issues, feature engineering issues, or something else all together. In my opinion, this is representative of the problems with data science tools today. There is so much focus on the machine learning algorithms rather than getting…

> There is so much focus on the machine learning algorithms rather than getting data ready for the algorithms. Generally, once a problem at work has come to the point of being a "kaggle problem", it's trivially easy. The main problem is unstructured data, with infinite ways of specifying similar ways to measure the same attribute, and lots of leeway to build an unmaintainable data pipeline between the data generation…

All Kaggle problems aren't created equal. Some look like a train matrix, a single target, and a test matrix.

Others are far more complex and start with much messier data and/or complex formulations.

Examples:

- www.kaggle.com/c/nips-2017-non-targeted-adversarial-attack/ - www.kaggle.com/c/the-allen-ai-science-challenge

Re: The State of Data Science and Machine Learning

#20

Post by VHRanger »

sidlls wrote: post #18
VHRanger wrote: post #16

Earlier quoted context omitted.

> There is so much focus on the machine learning algorithms rather than getting data ready for the algorithms. Generally, once a problem at work has come to the point of being a "kaggle problem", it's trivially easy. The main problem is unstructured data, with infinite ways of specifying similar ways to measure the same attribute, and lots of leeway to build an unmaintainable data pipeline between the data generation…

I disagree that a "kaggle problem" style problem is trivially easy, but I strongly agree with the sentiment that dealing with unstructured data is often a much bigger, deeper, and broader problem than the choice of a particular algorithm or ensemble of them. The ability to efficiently and effectively derive insights from such data is scarce.

Right, by "kaggle problem" I mean the general case where we roughly know what we're going to want to have on the right hand side of the model we're going to run (plus or minus some feature engineering, model choice and other hyperparameter specification, etc.)
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