In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of data science skills. They explore the various aspects of data science and what it truly means to be a data scientist. They touch upon the importance of understanding the scientific method and how it applies to data science. The conversation also delves into the misconception that data science is the sole focus of a data scientist’s work, highlighting the significant role of data engineering, data analysis, and programming skills. They emphasize that data science cannot exist in isolation and requires a strong foundation in other disciplines. The episode concludes with advice for individuals considering a career in data science, encouraging them to focus on their interests and strengths. Overall, the discussion sheds light on the complexity and interdisciplinary nature of data science, challenging common misconceptions about the field.
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Machine-Generated Transcript
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Christopher Penn 0:00
In this week’s In-Ear Insights, let’s talk about the limitations of data science skills, what you can and can’t do with data science.
So, Katie, when you hear this topic, what is it that that comes to mind? What are the things that you want to know, particularly someone who is not by trade a data scientist?
Katie Robbert 0:19
You know, it’s interesting, because when I hear limitations of data science skills, I immediately think soft skills.
And I’m guessing you’re thinking hard skills.
And so I guess it’s a conversation, I guess there’s a couple of conversations we can have is, you know, what should and should you not use data science for? You know, do you need to be a data scientist to set up your Google Analytics? Probably not? Do you need to be a data scientist to set up your large learning model? Probably.
And so understanding the skill sets of a data scientist, but sort of like, when I hear the question, I’m thinking beyond the standard data science skill sets of, you know, is the limitation of a data scientist, that typically they’re not a great communicator, or typically, a data scientist isn’t a leader, which I know is not true.
But you know, it’s not the rule.
So that’s sort of what I’m thinking.
But what do you think of when you hear that question?
Christopher Penn 1:15
I think we should first define what data science is.
Because it’s, it’s something that a lot of people, you hear the term thrown around a lot.
And it’s not necessarily being used accurately.
So data science, when you boil it all down, is the application of the scientific method to data.
So the scientific method is things like building hypothesis, constructing experiment, running a test, validating the results, and validating whether your hypothesis is true or not.
In that respect, it is just another form of science, which means that a lot of the things that we would attribute to data science actually aren’t.
Right.
So for example, working with Google Analytics, that’s marketing technology, doing doing data analysis within Google Analytics, is a data analyst, right? That is you’re literally analyzing data, setting up things like your website testing, and Google Optimize.
Now there, you’re starting to get more towards data science, right? You are starting to apply the scientific method.
But fundamentally, when you’re doing data science, you are applying the scientific method of that process to data in some fashion, maybe, for example, you got a great hit in the New York Times or the Wall Street Journal, in a public relations context.
And you want to know, did it matter? Right, and you would now pull out data science to do uplift modeling, which is effectively retroactive AB testing science to test did that create an impact or not? And so that is sort of the essence of data science, which means that a lot of the time when we’re talking about things like when you’re when you’re building a large language model, that’s not data science, that’s, that’s machine learning.
And that’s to a bigger term AI, but that is things like model training, pre training, data, cleaning, data preparation, and stuff like that.
But there’s no science in there in the in the scientific method sense.
Katie Robbert 3:18
I mean, that’s, I feel like you start to split hairs, then.
So then, is anyone truly a data scientist? Or is it that you’re sometimes a data scientist, sometimes you’re a data analyst,
Christopher Penn 3:34
that’s more like it is that 90% of the time, you’re actually a data engineer, right? 90% of the time, like, I have a whole bunch of crap data that’s dirty that I need to clean and refine and stuff to get ready to be used in a data science experiment.
But those processes and the things you do, yeah, 90% of your time, you are not doing data science, you are doing data cleansing analysis, maybe some exploratory data analysis, maybe doing some feature engineering.
You’re not doing the science part of data science.
Katie Robbert 4:10
But I thought exploratory data analysis fall under Data Science, because of the nature of the process.
Christopher Penn 4:16
The process, it’s exploratory it’s it’s not hypothesis testing.
It’s like what’s in the box.
What’s in the box is very different than saying, I believe this to be true.
Let’s test to see if this is true or not.
Katie Robbert 4:29
So if you’re not asking a question, if you’re not coming up with a hypothesis, but you’re doing everything else, you’re not a data scientist,
Christopher Penn 4:36
you’re not doing data science.
That’s correct.
And this, I think, is important with this topic of the limitations of data science skills, because, yeah, most of the time, you’re not doing data science.
Most of the time, you’re getting ready to do it.
Or you’re taking the outputs and the learnings from it and applying that.
For example.
You’re about to do an email marketing campaign.
You have a hypothesis.
To start, including emoji and the subject line will increase open rates, right? So what do you need to do you need to get gather your existing data, you need to come up with a testing condition like which emoji should I use this way? In? Which way should the arrow point on the clickable links, right? You construct your hypothesis.
If I include this yellow pointing hand on my subject line, my open rate will increase 4%.
You run the test, you validate? And then you’d say, is that true? We did this test? Does the pointing hand emoji, lift email open rates by 4%? Yes, or No? True or false? And then and that? That is 100%? Data science right there that that to hypothesis testing? Conclusion? And then you take that learning? Like, yes, it did.
Okay, from now on, all emails will have the yellow pointing hand on the subject lines, because we have proven through the scientific method, that this raises open rates 4%.
Katie Robbert 5:57
So I feel like and correct me if I’m wrong, but 99% of the time, Chris, you’re not a data scientist? That’s correct.
Okay, but yet you’re the chief data scientist,
Christopher Penn 6:09
that’s correct percent of the time.
That’s correct.
Katie Robbert 6:12
So what are you 99% of the time a data analyst and a data engineer a night
Christopher Penn 6:17
I would say 99% of my job is data engineering.
It is data engineering and data analysis, because it is collecting the data, it is processing the data, it is building the infrastructure to manage the data, you know, that part is a huge part of the job.
You cannot do data science without those prerequisites, though.
That’s that’s the sticking point is you can’t have data science without data engineering, and data analysis.
Because otherwise, you are you’re you’re a chef without a kitchen and a chef without ingredients.
Katie Robbert 6:53
So let’s say on the flip side of that, I run an AB test on my website.
Am I a data scientist?
Christopher Penn 7:05
If you’re using data to, to draw conclusions, and there’s a hypothesis involved? That is based in data? Yes.
Katie Robbert 7:15
So a few months back, I ran an A B test on our website, with the hypothesis that switching out the link from going through our blog to go into our services would result in higher conversions.
And that I looked at the data and made a decision.
That’s data science.
Christopher Penn 7:37
Yeah.
Did you collect the data in advance to see what was happening? Yeah.
Did you clean it? Prepare it? Yeah.
Did you explore the initial data to look for patterns that might have indicated Yes, this is worth testing? Yes.
Did you do any feature engineering in the dataset? As you were, you’re trying to figure out what to test? Like, day of week, week of month, etc?
Katie Robbert 8:02
I didn’t need to the data was in good shape.
Christopher Penn 8:05
Okay.
Did you have a statistical model from the test? Yes.
Okay.
Did you evaluate that model? Did you have hypothesis? Yes.
Did you prove that the hypothesis is true or false with the data? Yes.
You’re a data scientist.
Katie Robbert 8:23
So how can somebody be well, I guess, I mean, I know how like science works.
I’m not truly an idiot.
But you know, in a marketing organization, how can somebody be a full time all the time data scientists,
Christopher Penn 8:39
because your, your primary responsibility would be to generate things that data science creates that the organization can use, right? Even if 99% of the tasks you’re performing, and not data science tasks, you are still accountable for what data scientists are supposed to give out, which is data driven insights, right? Put this emoji in your subject line.
Now you can arbitrarily just say that, yeah, we’re gonna put poop emoji in all of our subject lines, but you have nothing to back that up.
Right? They have no, no way to say this is statistically a reliable outcome.
You have no way to say we should use three hashtags in our tweets, like, how do you know that? You can but if you were to then take 8 million tweets, and do the statistical analysis, count the number of hashtags, build a model, validated, say, yep, three hashtags, is it now you’re using the output of data science to drive your marketing when we talk about data driven marketing, we are really talking about data science driven marketing, where you have empirical proof you have proven that what you’re saying is true.
It’s show me the receipts is essentially what is
Katie Robbert 9:53
interesting.
So then why is the term the job title? Data Science, like, why does it carry so much weight? When it’s really such a small part of the job?
Christopher Penn 10:08
Because you can get paid more?
Katie Robbert 10:11
I mean, I understand that part of it.
But like why data science? Why not data engineer? Why not data analysts? Like, is it that term science that makes it feel heavier?
Christopher Penn 10:25
It sounds more expensive.
Katie Robbert 10:29
Marketers are the worst.
Christopher Penn 10:32
But if you think about it, think about it’s true because in, in corporate culture, we have devalued analysis so much, we have made it such a junior task when it really isn’t that someone who has proficiency above and beyond what you know, basics, needs a title that communicates there beyond the basics.
And yeah, in like large corporations just have like data analyst, one, data analysts, two data analysts, three senior data analyst, whatever.
But at the end of the day, that mentally and heuristically in the minds of people who are not in those roles, someone who says the title of data scientist sounds more important than someone who has the title of a data analyst, even though if you’re a data scientist, and you don’t have an analyst or analysts capabilities, you are hosed.
Katie Robbert 11:23
And I think that goes back to the question of what are the limitations of the data science skill set.
And I think that’s it like data science on its own.
Can’t do a whole lot unless you have all of those other skill sets as a foundation.
And I think that’s really, you know, getting to the heart of the conversation.
Christopher Penn 11:48
You know, four categories, this four things you need, you need business knowledge, like, what does this company do? Right, you need statistical mathematical skills, you need data engineering skills, and a lot of cases, you need programming skills.
So those four sets of skills comprise what is needed to make data science work.
Katie Robbert 12:10
And if you don’t have one of those, then you’re not going to be an effective data scientist,
Christopher Penn 12:15
you will be less effective.
That’s right.
So you have to have them or you have to have a team of people that has those skills.
Katie Robbert 12:21
So, you know, going back to that very simple example, I ran an A B test on our website.
I’m not a data scientist, but I ran a scientific test.
And so I would not call myself a data scientist, because I am not majority of the time the analyst or the engineer, I am the standing cmo who was curious to see if I made this change, would it affect it positively? Positively or negatively?
Christopher Penn 12:53
Right.
And I would argue that by having by having that more tight definition, that is if you’re using science on data, you’re doing data science, that also opens it up.
So that you it’s kind of in a good way takes away some of the mystery around the job title.
Again, if you’re it’s like, are you an analyst? If using Google Analytics, well, it may not be your full time job title, but you are still doing analytics, right? You are still doing analysis? If you are looking at Google Analytics go how many people visited the website, go in and look at it right.
That is that is still analysis.
And part of what our hope is, is that people feel less intimidated by, by that will say, yeah, if you are doing the thing, and you’re getting the answer that’s necessary to help your business, you are doing analytics, you don’t need a formal certification, you don’t need a fancy job title, to be able to do stuff.
And the same is true of data science, you know, if you can run an A B test, following the steps of collecting cleaning, gathering exploratory data analysis, feature engineering model, and hypothesis construction and proof.
If you’re doing that, regardless of your job title, you are doing data science, and that’s a good thing.
Katie Robbert 14:10
So it sounds like the limitation is data science, on its own by itself is not enough of a well rounded skill set data science needs other disciplines to support it.
And that becomes a limitation.
So you know, if I’m, you know, in an organization, I’m like, You know what, we need a data science team.
I can find a data scientist, but if that data scientist can’t code or can’t prepare data, then I still need to hire like five or six other people who can do those things.
And that sounds like that’s the limitation of data science.
It does.
It can’t exist on its own.
It needs all of these other pillars to hold it up.
Christopher Penn 14:55
It does think about this.
Recently, you asked me to write down kind of, you know, the software skills and the job skills that I use on a regular basis in my job, right? And, yes, one of them is very much in the data science wheelhouse the our programming language, right.
But that document is like 16 pages long, most of which has nothing to do with data science, you know, administration of BigQuery, Google Cloud platform where the data comes from at Google Analytics, which is where the data comes from the Mautic, marketing operations system and server administration, where the data comes from.
So most of that list is really on the data engineering side and just straight up it.
Not data science, not because we don’t do data science, but because so there’s so much data science hinges on that gigantic foundation of other skills, other techniques, other technologies, other people that yet, like you said, data science, and a data scientist, is a sort of an umbrella skill set over a huge Foundation.
And if you don’t have that foundation, if your foundations unstable, you’re not gonna be able to do the thing.
Katie Robbert 16:09
So what advice would you give to someone who, you know, let’s say it comes to you and say, You know what, I think I’m gonna go back to school to be a data scientist.
What advice would you give me in terms of where I should focus my time?
Christopher Penn 16:22
Well, first, I’d ask you why?
Katie Robbert 16:24
Because I heard it’s a really important skill set, it’s the job of the future, it’s going to give me lots of money and nobody knows what it is.
So I’m going to be a data scientist.
Christopher Penn 16:35
There are easier ways to make a lot of money.
Katie Robbert 16:38
Okay, but answer this particular question.
Christopher Penn 16:43
Should you do that? Like, should you should you go back to school be a data scientist? Do you love working? Well, first, are you a very curious person?
Katie Robbert 16:55
Yes.
I have a lot of questions.
Christopher Penn 16:58
Okay.
Do you love working with data? Do you enjoy spending time in spreadsheets? Do you enjoy comparing your sets of numbers to each other? Yes.
Okay.
Do you have a head for business?
Katie Robbert 17:17
And I hope so.
Christopher Penn 17:20
Okay, can you program poorly? Have you? Did you enjoy it when you tried it?
Katie Robbert 17:28
I understand.
It’s a set of logic, and I’m really good at logic.
Christopher Penn 17:31
Did you enjoy it?
Katie Robbert 17:34
I want to make it my full time career.
Christopher Penn 17:38
Okay.
Have you ever done any work with databases? Yes.
In terms of building them and operating them?
Katie Robbert 17:45
Yes.
Christopher Penn 17:46
Did you enjoy that? No,
Katie Robbert 17:48
I did not.
Christopher Penn 17:50
So you’re two out of four on the skills of a data scientist.
So I would say no, you probably should not pursue this as a as a career.
I would say it’s three out of four or better.
Katie Robbert 18:02
And I think that that’s a really good quick gut check.
For people who say I want to go into data science, because there is that misunderstanding of what go actually, a data scientist is what they’re responsible what they have to do.
It’s not just sitting around coming up with a hypothesis, like, you know, what we should explore today, this.
So I used to work with research scientists, for example.
And three times out of the year, out of the whole entire working year, they were asked to come up with a hypothesis.
And write down for grant submissions, write down how they plan to test this hypothesis three times a year.
The rest of the time, these research scientists, were stuck in meetings with project managers, you know, meetings with research assistants, calling through paperwork, trying to fill out forms, like it was a very taxing job.
And the thing that they were asked to do, the research part of it was minimal.
So minimal, and I think that what I’m hearing is, the same is true of data scientists that the amount of times you are asked for a hypothesis is so small compared to all of the other crap you have to do.
Christopher Penn 19:24
And what separates data science, at least in my mind from other forms of science, is that a hypothesis has to come from data, right? It’s not something that you make up right, like, oh, we should use poop emoji in our tweets or show me the data that you know, the from your exploratory data analysis that indicated that would be even vaguely a good idea, right? Like, is there even though they’re there to look at? In that’s where, you know, we’re going back to your example of testing things on the website, if what you chose to SaaS was purely arbitrary based on anything Other than data, then it was not a data science experiment was a science experiment.
It’s not a data science expand because it did not start with data.
Katie Robbert 20:07
Now see, I would argue that it’s not the data, it’s the science that makes it.
So, you know, when I’m speaking about these examples of the research scientists, they could come up with whatever hypothesis they felt like, but they had to then back it by research, to say, this is why I think the thing that I’m proposing will solve a problem, not just, you know what, I’m just going to invent a thing.
And, you know, let’s, let’s see what happens.
But there had to be a problem existing in the first place that this research was set out to solve the same is true of data.
So I would say, for a data scientist, the data has to exist for the research scientist, the research has to exist, but it’s the science part, you have to be trying to solve an existing problem.
So your hypothesis is based in reality.
Christopher Penn 21:01
Exactly.
And we have talked to many people over the years, who have attempted to try and explain what we do, or to to happen for fit for events, sell it to other people on our behalf.
And they had no understanding of what data science was.
So their inability to describe Would you accept it, you know, I vaguely recall, in one meeting, someone was outright describing as magic and like, that is the worst possible way to describe this stuff.
Because it is the antithesis of magic, it is not there’s nothing mystical, it is it purely around, you know, quantitative and to some degree, qualitative data that’s been quantified, so that you can build hypotheses and test them.
And that goes back to the beginning of what we were talking about, which is, you know, when we talk about limitations, data science skills, the skills themselves are, you know, they fall in those four buckets, the limitations really are on the understanding of people about what this stuff is and what it can and cannot do, it is not magic.
It is, you know, the same is true of AI and machine learning.
It is not magic as mathematics most of the time.
And if you are trying to solve a problem, that is not math, with math, it doesn’t work out what’s like trying to solve, you know, people who use ROI, which is a financial calculation for a non financial problem.
Katie Robbert 22:19
Or I’m going to totally butcher your kitchen analogy, but it’s fine to use, you know, a frying pan to, you know, blend a smoothie.
Christopher Penn 22:29
Exactly.
It’s not going to work out well.
No matter how much you want to be able to talk about frying pans all day long, right? There’s a lot of people, for example, right now in the AI space, who have no understanding of artificial intelligence, none other than what they read, and like, you know, the the news that morning.
And so when they talk about it, they talk about it in such a way that if you know anything about failure, like wow, this guy is almost always the guy, this guy is just talking out out to the wrong end of the horse.
Katie Robbert 23:06
I have no comment.
Yes, I haven’t.
I have no appropriate public comment.
Christopher Penn 23:16
Exactly.
But you know, that that’s the limitations of data science skills is the lack of understanding about stakeholders about what to do with them, or how to use them best?
Katie Robbert 23:26
I think so.
And that sort of goes back to the lack of understanding of what data science is in the first place.
Sure, it’s a fancy pantsy title.
But at the core of it, it’s a lot of hard work.
It’s a lot of on, I won’t call it an interesting, but if you, as you sort of questioned me about, you know, do I want to be a data scientist, if I’m not interested in building databases and coding, then I’m not going to enjoy being a data scientist.
Just like if I am not interested in reading academic papers and running clinical trials.
I’m not going to enjoy being a research scientist.
Christopher Penn 24:06
Exactly.
But you know, all the people who are in, in the AI space like yeah, if you’re, if you are talking about the highest and most advanced levels, you’re talking about a lot of math, and math is not your jam.
It’s going to be an unpleasant experience.
And, you know, kind of what you were saying when you were talking about why you should become a data science scientist for the money or not like the advice I always give people is get really good at what you love, because you’re gonna be doing a lot.
But also if you if you love it, you will be really good at it.
And it is and you will the Money Follows the experience and skill, right money.
Mitch Joel says money flows through responsibility.
The faster you get to positions of responsibility, the more money you make, and the faster you get there, is by being really good at what you do.
There is no there’s nothing lessor about being a really great graphic artist, or being a good manager, right? You can, if you are a skillful Manager, you can make a lot of money because the world desperately needs them.
Even if it’s not something that does, it doesn’t have a fancy title, like, you know, chief data scientist, you got one bad Majan they can wreck an organization.
Katie Robbert 25:21
Well, I was gonna say like, I’m really good at being bossy, and I will love it.
I will do it all day long.
But I’m a professional manager.
And I’m really I really enjoy helping other people succeed in what they’re doing.
And that’s what a good manager should do.
And therefore I enjoy it.
I’m happy with what I’m doing.
So I’m happy to be you know, call me CEO, call me project manager, call me whatever.
Just don’t call me late for dinner.
You didn’t see that coming? Did you? Nope.
But the point is, I’m not a data scientist.
I’m not interested enough in the foundational pieces, like, sure, I like to come up with a hypothesis and test it.
But that doesn’t mean I’m a data scientist.
It just means I’m curious.
And that’s a good thing.
But I’m gonna leave the data science to the experts.
Christopher Penn 26:19
It is but I will say as the counterpoint if you are using the scientific method, or with data informed by data to make data driven decisions, whether or not you have the title of data scientist, you are doing data science, and that is something that everyone is welcome to do as long as you’re following the principles of the scientific method with data, or doing data science.
So don’t sell yourself short either.
Katie Robbert 26:45
Fair, I’ll take it.
Alright.
Christopher Penn 26:48
If you’ve got some thoughts about data science, data science, skills and limitations that data science skills and you want to share them hop on over to our free slack group go to trust insights.ai/analytics for marketers, where you have over 3200 other marketers are asking and answering each other’s questions every single day, including some data science questions, and wherever it is, you watch or listen to the show.
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And while you’re there, please leave us a rating and a review.
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Thanks for tuning in.
I will talk to you next time.
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