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Data Science: Reality Doesn't Meet Expectations

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Re: Data Science: Reality Doesn't Meet Expectations

#41

Post by [deleted] »

beckingz wrote: post #31
dtjones wrote: post #10

Earlier quoted context omitted.

I've been working in data roles for 10 years and hold a masters in ML. I've hired and managed each of the roles you mentioned. I think of the responsibilities of each of those roles as: -ML Engineers as building software infrastructure to scale machine learning inference and training. -Data engineers focusing on data infrastructure and pipelining into either model inference, training, or other business intelligence p…

How hard could it be to find one person who can do all that?

[deleted]

Re: Data Science: Reality Doesn't Meet Expectations

#42

Post by resolaibohp »

This article is pretty spot on. As someone who has worked in data science/analytics for over 6 years I have found that the field is filled with hype, managers who are not sure what data science actually is, and an absurdly wide amount of skills jobs expect you to be able to do well.

Apply for and interviewing for data science jobs is a total nightmare. You are competing against 100s or even 1000s of applicants for every job posting because someone said it was one of the sexiest careers of the 21st century. Further exacerbating this, Everyone believes that data is the new oil, and large profit multipliers are just waiting to be discovered in this virgin data that companies are sitting on. All that is missing is someone to run some neural network, or deep learning algo on it to discover the insights that nobody else can see.

The reality is that there is an army of people who know how to run these algos. MOOC's, blogs, youtube, etc have been teaching everyone how to use these python/R packages for years. The lucky few who get that coveted data science job can't wait to apply these libraries to the virgin data only to find that they have to do all kinda of data manipulating to make the algos even work, which takes days and weeks of mundane work. Finally they find out the data is so lacking that their deep learning model does very little in providing actual business value. It is overly complicated, computationally expensive, and in the back of your mind know you can get the same results using some simple logic.

Managers who don't understand data science fundamentals learn from the news and have their data scientist implement those buzz words so they can look good in front of their bosses.

I think there is a place for data scientists who understand the fundamentals of the models out there, and know when you should not use them. Data science is also increasingly a subset of software engineering and a good data science in a tech company should be able to code well. I also think that there is not some huge unmet demand for data scientists. Just a huge amount of hype and managers wanting to look good by saying they managed a data science team.

Re: Data Science: Reality Doesn't Meet Expectations

#43

Post by danmostudco »

I stood up a data science operation at my company over the last few years, and have noticed a key difference in data-science projects that have been successful and those that have failed. It hits on a number of points brought up in the article, namely where does data science "fit" in an organization delivering software and how is the value realized by the business.

The worst cases I have seen is when executives take a problem and ask data scientists to "do some of that data science" on the problem, looking for trends, patterns, automating workflows, making recommendations, etc. This is high-level pie in the sky stuff that works well in pitch meetings and client meetings, but when it comes down to brass tacks this leaves very little vision of what is trying to be achieved and even less on a viable execution path.

More successful deployments have had a few items in common

1. A reasonably solid understanding of what the data could and couldn't do. What can we actually expect our data to achieve? What does it do well? What does it do poorly? Will we need to add other data sets? Propagate new data? How will we get or generate that data?

2. The business case or user problem was understood up front. In our most successful project, we saw users continuously miscategorized items on input and built a model to make recommendations. It greatly improved the efficacy of our ingested user data.

3. Break it into small chunks and wins. Promising a mega-model that will do all the things is never a good way to deliver aspirational data goals. Little model wins were celebrated regularly and we found homes and utility for those wins in our codebase along the way.

4. Make is accessible to other members of the company. We always ensure our models have an API that can be accessed by any other services in our ecosystem, so other feature teams can tap into data science work. There's a big difference between "I can run this model on my computer, let me output the results" and "this model can be called anywhere at any time."

While not exhaustive, a few solid fundamentals like the above I think align data science capabilities to business objectives and let the organization get "smarter" as time goes on as to what is possible and not possible.

Re: Data Science: Reality Doesn't Meet Expectations

#44

Post by [deleted] »

beckingz wrote: post #31
dtjones wrote: post #10

Earlier quoted context omitted.

I've been working in data roles for 10 years and hold a masters in ML. I've hired and managed each of the roles you mentioned. I think of the responsibilities of each of those roles as: -ML Engineers as building software infrastructure to scale machine learning inference and training. -Data engineers focusing on data infrastructure and pipelining into either model inference, training, or other business intelligence p…

How hard could it be to find one person who can do all that?

[deleted]

Re: Data Science: Reality Doesn't Meet Expectations

#45

Post by smeeth »

rafiki6 wrote: post #40
smeeth wrote: post #37

Earlier quoted context omitted.

I always thought the non-specificity of the term Data Science was a strange criticism for those in the tech industry to make. How many types of SWE are there? Front-end, back-end, full-stack, devops, security, QA... I agree wholeheartedly with your recommendation. Like any other job, each company has different needs and expectations and if you want something else out of the role you'd best avoid that company.

Frankly I have the same criticism of those who use the term software engineer. Engineering is a pretty established profession with a set of standards, ethics and practices. Most of us who work in software are not engineers. We are developers. Similarly, a scientist is one who follows the scientific method to do research. So by that logic a data scientist should be a person who uses the scientific method to do researc…

I'd be best described as an ML Researcher/Engineer and I'm not in the private sector so take my opinion with a grain of salt, but my understanding is that many DS roles require application of the scientific method.

A lot of DS can be boiled down to some sort of statistical testing or inference (A/B test email marketing for example) or applied ml (classification, regression). I'd argue thats science (if done right).

Data Analysis, the plot a few charts and put it in a slide deck kind? Totally agree with you. Definitely not science.

Re: Data Science: Reality Doesn't Meet Expectations

#47

Post by semi-extrinsic »

hectormalot wrote: post #5

> Moreover, you may quickly realize much of this work is repetitive and while time-consuming, is “easy”. In fact, most analyses involve a great deal of time to understand the data, clean it and organize it. You may spend a minimal amount of time doing the “fun” parts that data scientists think of: complex statistics, machine learning and experimentation with tangible results. This. Universities and online challenges…

There was a remark in the old school Linear Algebra book we had in university (Edwards & Penney) that stuck with me, to the effect (probably I recall the details wrong) that one of the authors were once involved in data analysis of water samples collected from a bunch of rivers by 15 engineers, and it turned out no 6 of these engineers' measurements were internally consistent. The moral of the story was that real world data is messy, you need to learn least squares and related methods to make sense of the data.

Now with "data science" you've taken a step further, and instead of applying the math to lab reports on meticulously filled out forms, you're going to aggregate all the messy sources you can get your hands on. Of course your headaches will multiply.

Re: Data Science: Reality Doesn't Meet Expectations

#48

Post by jtanderson »

kqr wrote: post #33
hectormalot wrote: post #5

> Moreover, you may quickly realize much of this work is repetitive and while time-consuming, is “easy”. In fact, most analyses involve a great deal of time to understand the data, clean it and organize it. You may spend a minimal amount of time doing the “fun” parts that data scientists think of: complex statistics, machine learning and experimentation with tangible results. This. Universities and online challenges…

I took a data visualisation class in uni that handled this really cleverly. The second assignment sounded very easy. The teacher provided links to the sources where we could find data. Most people figured that with such a simple assignment (not significantly harder than the first one, which was also easy-ish) they could put off doing it until the last moment. Most people failed. This real world data needed hours upon…

I’m currently preparing a data visualization course to be taught this fall, and I would love to hear more about this! If you’d be willing to share some of those resources or the contact information for your professor, I’d really appreciate it. You can find contact info at the link in my profile :)

Re: Data Science: Reality Doesn't Meet Expectations

#49

Post by simonkafan »

My feeling is that a lot of companies think: "We need a data scientist because all the big players also have one!"

In fact, they actually don't need a data scientist. At best they need someone who cleans data, creates pie charts or even worse, they relabel the database admin job as "Data scientist".

Re: Data Science: Reality Doesn't Meet Expectations

#50

Post by mr_toad »

rafiki6 wrote: post #40
smeeth wrote: post #37

Earlier quoted context omitted.

I always thought the non-specificity of the term Data Science was a strange criticism for those in the tech industry to make. How many types of SWE are there? Front-end, back-end, full-stack, devops, security, QA... I agree wholeheartedly with your recommendation. Like any other job, each company has different needs and expectations and if you want something else out of the role you'd best avoid that company.

Frankly I have the same criticism of those who use the term software engineer. Engineering is a pretty established profession with a set of standards, ethics and practices. Most of us who work in software are not engineers. We are developers. Similarly, a scientist is one who follows the scientific method to do research. So by that logic a data scientist should be a person who uses the scientific method to do researc…

> the scientific method to do research on data

Exploratory data analysis is often overlooked and underrated.

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