Comment by sixdimensional | Hacker News Reader

Comment by sixdimensional | Hacker News Reader

"The only difference between screwing around and science is writing it down" - Adam Savage.

In all seriousness, why can't data science simply be about applying the scientific method in the realm of data analysis? It doesn't need to be conflated with machine learning, BI, SQL, etc. It can just be about approaching data analysis with scientific rigor.

My opinion is that the term data science evolved when we started needing cross-functional people who are a blend of:

- domain experts;

- numerical/quantitative specialists (such as statisticians, mathematicians, physicists, STEM people);

- business analysts, business intelligence; and,

- those who traditionally deal with data management, platforms and tools.

That confluence of people was needed amidst the related trends:

- increased government funding for STEM education and brain research;

- marketing from companies such as IBM ("Watson"), the democratization of data and increase in the use of data in daily life;

- the big data wave, subsequent interest in "internet of things" and "digital transformation";

- renewed interest in machine learning and AI (recurrent neural networks and other breakthroughs);

- and others of course..

We needed to apply more discipline to data analysis - thus data science was born. A formalizing of what many were already doing, to capture the need and changing paradigm. Or so I like to believe.

> why can't data science simply be about applying the scientific method in the realm of data analysis?

Because then someone with a business school education (and zero formal statistical training) wouldn't be able to do it. I joke about waiting for finance's Excel models to be rebranded as AI, as I've already seen a handful of hedge funds rebrand their analysts as data scientists.

A large number of hedge fund analysts are data scientists. Just because computational finance models used a tool that abstracted away a large portion of the programming doesn't mean that they weren't using applied statistics and domain specific modeling to solve problems.

Let's take a simple example, linear regression. You can fit a linear model with w few button clicks in Excel and then look at R² to check if it's high enough and call it a day. Whereas in R you'd be able to easily check a bunch of other metrics to assess your model (ANOVA, plot the residuals, etc). When there's a lot at stake, you want to make the decision yourself and not just rely on the output of a black box.

All of the things you're taking about are possible to do with Excel. Of course I prefer R, but at some level we all abstract away algorithmic responsibility and return granularity.

You can also easily obtain summary statistics and ANOVAs, etc, in Excel with a few clicks.

> You can also easily obtain summary statistics and ANOVAs, etc, in Excel with a few clicks

To be clear, my original comment was not criticizing Excel. It pokes fun at analysts who couldn't tell you a Type I error from a Type II being branded as "data analysts" because they build DCF models.

I think a relevant analogy from back in the day is being able to use frontpage and then calling yourself a web developer.

Certainly it has good features to create what you want, and you could edit source (if you can read it!)...but it's used by people who have a elementary knowledge of web development.

> a large number of hedge fund analysts are data scientists

Maybe I'm being a curmudgeon. In my book if you can't build the statistical tool you're using, you don't understand it. So if, in Excel, you can fit a regression from "scratch", (i.e. not using any built-in regression functions) and use the built-in functions for convenience, that's fine. If you can't, you're a regular financial analyst. (Nothing wrong with that. I was one once.)

This is important because being able to build it means being able to tweak it. Excel's tools have quirks and make built-in assumptions about your data. If those assumptions don't hold, you should be able to tweak (or change) your approach. Being limited to built-in models removes that flexibility. It also implies you don't know when you're crossing between "my tool works" and "my tool is outputting garbage."

Hadley Whickam also stressed the importance of readability and reproducibility of code vs software like Excel. A sequence of manipulations and button presses doesn't leave a clear history for others in your team to understand or quickly test and reproduce your work to validate, or apply to be data sets.

For work to be "science", it really ought to be transparent and reproducible within your community (eg, team within your firm).

This is an important point too. I'm still amused by the story of the "Growth in a Time of Debt" paper. Two famous Harvard economist made an Excel mistake (among other things) that completely changed the conclusion of their study and ultimately affected real-world economic policies.

Here is a summary of the story: https://www.nytimes.com/2013/04/19/opinion/krugman-the-excel...

>So if, in Excel, you can fit a regression from "scratch", (i.e. not using any built-in regression functions) and use the built-in functions for convenience, that's fine.

I suppose it depends on what you mean by "can". Much like whiteboard interviewing, I bet I can't sit down today and implement a regression from scratch, because I'm out of practice. I have had no need to retain that information; there are tools that do it for me.

Could I do it quickly with a stats 101 textbook in my lap (or 10 minutes of Googling)? Absolutely.

I don’t think most “data scientists” can do b = (X′X)^(−1) X′ Y right off the top of their heads either.

> not using any built-in regression functions

This is absolute pure silliness. So the guy down the street that can't manufacture a new carburetor isn't a mechanic? The doctor that can't create penicillin in his personal office isn't a medical professional?

More often than not, the people that create new systems are not the same people that take these concepts and apply them to practical business problem. It takes many kinds of people to help a company grow.

> why can't data science simply be about applying the scientific method in the realm of data analysis?

That's what a statistician do.

I've seen these ML and Datascience people. And the majority the time how they tackle data is radically different from statistician and is more of an art than a science compare to what statistician does.

But this could be my bias opinion and just some small data sample from personal experiences.

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Actually my last day of internship I've met a few statistician interns some of them are from Cal (UCBerkely) and they came to the same conclusion (we have a lot of complaints). The ML/DS group is really just doing black magic (nicest way of putting it). I wish statistic is better at marketing. Oh well.

The amazing thing is that the black magic gets results (for certain categories of problems).

I've seen them do random forest on temporal data.

They tried to fix it with PCA.

What do you mean by "on temporal data"?. For example, no feature extraction was performed? This sounds pretty amateurish, and completely below the understanding levels of the many machine learning papers that I've read - which is admittedly a small percentage of what has been published.

I've seen "them" test in production. Every field has varying levels of skill.

In my humble experience as a data scientist at a big tech company, a big differentiating factor is familiarity with the R or Pydata stack. It's not just its own language and library, it's a more general idiomatic way toward approaching and solving problems from a "software first" perspective.

I sometimes work with people who are formally trained in stats at a much higher level than myself. But with my lesser ability combined with my capability to write production grade Python code using the pydata stack, my code and solutions are more likely to make it in.

I think we're talking about different roles.

I'm coming at it from just a modeler role and it seems like you are stating more of a full stack within the data science. I took OP remark as if it was just within data analyst in term of analysing the data and not including turning it into some kind of application.

As for Python, time series isn't as good in python. I do agree that Python production is good. But I don't believe Python have better analysis packages. I'm in the camp of using programing language for their strength and using Apache Thrift or whatever to tie everything in.

> As for Python, time series isn't as good in python

Time series isn't as good

Linear regression isn't as good

Mixed models are awful

Good luck fitting a spline without diving into scipy and doing it as an interpolation

R packages are almost always accompanied with a rigorous paper and a great vignette, whereas in Python, there are almost no talks of the implementation and just documentation about how you can use the library.

Every model we develop has assumptions and shortcomings, R offers the tools to diagnose and examine that, whereas in Python, I get the feeling that it's more like "some smart guys figured out this formula, here's an implementation of it".

I agreed, I didn't want come off as a disgruntle statistician and list the weakness of Python.

But you're right. I find most Springer, CRC, etc.. books are in R with accompanying R packages. Shumway & Stoffer Time series book comes with the R astra package. Andrew Gelman Bayesian create Stan and their target was R first (rstan). The Sanford people who created lasso and elasticnet created glmnet. The thought that the creators or experts of these subject wrote a book or research paper and then also publish R package to accompany it is reassuring.

When it comes to just data analysis, it seem like R is pretty good or better than Python. I think Python is much better for ML stuff such as deep learning and also it seems like Python have better NLP support. Arguably Stanford is very good with NLP and they publish their tool in Java.

How good ML and Datascience people that you have met were at statistics and in math in general?

In social sciences (speaking as an economist), the goal of statistics was always testing theories and hypotheses against empirical evidence. Once you have a solid theory, then you have an understanding of the subject matter, which allows you to make predictions and counterfactual hypotheses. So the primary goal was understanding the data generating process, finding causal relationships between variables, and so on. The goal was specifically not making predictions. One of the first things I learnt was that a bad model can make good predictions (for a while). The ML crowd appears to be taking the opposite approach, focusing on making predictions and disregarding everything else. I suppose it has its uses, but I wouldn't call it science.

>> why can't data science simply be about applying the scientific method in the realm of data analysis? >That's what a statistician do.

Mmmh.

Run that experiment for me next time you meet a statistician:

    - ask him if he can apply Chi-squared to a decision problem

    - ask him if he can *explain* how and why Chi-squared works.

In my experience, all statisticians can do the first, almost none can do the second.

Learning how to use a screwdriver to screw screws without understanding notions of torque and moment doesn't mean you're applying the scientific method.

> In my experience, all statisticians can do the first, almost none can do the second.

I think a phd statistician can do this.

Master statistic does not touch upon field and measure theory in statistical inference so many questions get unanswered. But I suspect you may be correct.

I view chisq as a statistical distance for most of my encounter and learning.

I would argue that they should be able to understand it to the level that they can at least defend Chi-squared as a tool for the problem at hand. Then, they should be able to evaluate whether or not it works correctly.

If a medical researcher is testing a new radio-therapy treatment, but can't mathematically model every fission problem you can throw at them, they're still applying the scientific method.

This, and your other responses in this thread, come off as rather disappointing to me, as someone who considers the work they do as "data science". Machine learning as a method clearly has quite a bit to contribute to the business world based on revenue alone. My argument could rest here.

But it's also disappointing the way that you belittle all machine learning practitioners, even those with academic credentials, for their work not being worthy of serious consideration. This also sounds a little defensive and projective, and I can't imagine it's easy seeing the forest for the trees with your head in the clouds.

Trying to walk on a rope is pretty hard balancing act.

I do want to give a personal view and constructive criticism but any intentional and direct attack toward another domain is not my intention.

I think statistic is very well equipped to do just data analysis. The discipline have many weaknesses I do acknowledge that but I don't believe data analysis is one of it when it is the core tenet of what statistic is.

I believe data science is too new and is still trying to find it's standing. Also it seems like a jack of all trade and a master of none discipline. I don't believe the discipline can be a master of everything including data analysis with all other things it's trying to incorporate.

My critique is that for the original post is that there is already a field for data analysis. Just for it, for a century now, it's named statistic.

ML/DS is a new breed that is more than just data analysis. Because of this they can do many things but I don't believe they are an expert at any one thing. Which is fine. But I do understand that this discussion is sensitive since people within the DS/ML since that is how they make money and earn a living.

if you really understand ML/DS, then you would know that ML is founded on all the same concepts.

> "... they tackle data is radically different from statistician ..."

the short answer is, before we had limited data, compute, and the problems businesses needs increased substantially.

now we can do better things, and if you cant do more than what companies were doing in the 1950s, then your value proposition is significantly less than someone that's using industry practices established in 2017 / early 2018.

> "I wish statistic is better at marketing. Oh well."

as a data scientist, I have to know stats, ML, big data tools (spark/splunk/hdfs), programming, and domain specific knowledge. some data scientists are just rebranded statisticians, which is fine. the role of a data scientist varies to the needs of the org they are in. I mostly do anomaly detection and classification, while others may focus solely on AB testing.

> > "> why can't data science simply be about applying the scientific method in the realm of data analysis?"

the statement doesnt make sense to me. if you are doing data analysis then by default you are doing observations and measurement on empirical data. As far as experimentation goes, almost no one has the resources or the standing to do that. Some do, like those AB testers I mentioned before.

> "Actually my last day of internship I've met a few statistician interns some of them are from Cal (UCBerkely) and they came to the same conclusion (we have a lot of complaints). The ML/DS group is really just doing black magic (nicest way of putting it)"

Then they don't appreciate ML and the problems they are trying to tackle. It reminds me of a stats PHD co-worker that was upset about the number of parameters in an image classification problem we had. they didn't think a model could be ran that had more parameters than observations. I was like we are not running linear models ... As an experiment, go to kaggle and try the spooky author classification competition. See how far you can get with basic stats and then see how much further you can get with ML. hopefully it will give you an appreciation for some of the ML tools available.

It's not black magic, like many other domains; its just a dense subject that takes a lot of time to understand

Yeah, most of this thread has kinda garbage responses.

Even if you did a CS degree and then did a BMath with a statistics major you still wouldn't have all the skills you would need, though you'd have a great place to start off from. There is an art in kinda guiding / interpreting the mathematics that's hard to teach and most people in the field kinda pick it up through experimentation. I'm sure it will get more formalized at some point, but I don't think the formalization will be all mathematical or scientific; I think much of it will be making explicit how to think about certain domains or a system of process that are useful.

Also I think there is a big difference between Data Engineers, Data Analysts, Data Scientists, and AI researchers.

Depending on the field, an AI researcher deals more with idealized forms and is much stronger on the mathematics of machine learning.

Data engineers are usually stronger on the CS topics that deal with scale.

Data analysts, even the very best ones, tend to be weaker on the CS, the math, but tend to be the strongest at quickly understanding data and making it intelligible to decision makers. I'm not knocking it—I was an analyst at one point—it's just the truth that some positions take more skill than others and most great data analysts tend to treat it as a 3 or 5 year stop before levelling up to management or a more technical role.

Data Scientists generally have the skills of a data analyst (though they have trouble dumbing things down at times if they came here from something other than an analyst position) with some of the skills from the other two.

The way some application code software developers dismiss a whole sub-discipline is kinda embarrassing.

Is business intelligence considered data science? I've never really understood. Thanks.

I've written about this problem before as well[1] - data science seems to be defined uniquely narrowly (and confusingly) compared to other fields.

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"Data science" is the most natural name for this field. Though fields like "information science" and "political science" are broad, "data science," as it is popularly defined, is uniquely narrow. This is problematic because general fields typically serve as a roadmap for all subfields - a cursory glance at what they are and how they relate. "Data science" today does not provide this road map.

[1] https://alexpetralia.com/posts/2016/6/22/reclaiming-the-term...

Seems like "Data Scientist" as a job title is too generic.

It's like saying "Web Designer" nowadays, when in reality we have a variety of job specializations like UX strategist (ux only), graphics designer (photoshop), UI developer (html/css only), front-end developer (html/css/js), etc.

> In all seriousness, why can't data science simply be about applying the scientific method in the realm of data analysis?

I suppose someone who isn't very smart is staffing data analysts with little understanding of science. Most would assume that if you are getting paid to analyze all this valuable data, you would have some grounding in the scientific method.

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