Monina asks, "What kind of analytics can data science uncover?"
This is an interestingly worded question. Analytics are fundamentally about explaining what happened, the what in your data. Very often, as stakeholders we also want to know what's relevant (data overload) and then why (insights). That's where data science can help, especially with understanding what's relevant. Methods like regression, clustering, classification, and dimension reduction can greatly assist us in finding out what really matters.
You'll never create something out of thin air - always derived from your initial data. That's why domain expertise matters - to know what else is available.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode menina asks what kind of analytics and data science on the cover? This is an interestingly worded question.
Analytics fundamentally is about what happened, right? It comes from the Greek word online, which means to loosen up to unlock.
So you have your data.
And you want to unlock that data to loosen up that data to turn it into something usable.
Fundamentally, most analytics, at least as we experience it, within marketing, are about what happened in the data, right? You log into Google Analytics, and it tells you what happened.
You load up your social media analytics to tell you what happened, and so on and so forth.
And what's missing from that? Which is the whole reason any of us have jobs is it really is just What happened? There's no explanation of why there's no explanation of what variables matter.
You just get a pile of data.
It's like someone backed the truck up and you know, poured data all over your desk.
And so what data science can help with is answering those next step questions, right? Your boss comes to your desk and says, You made me a dashboard with 438 variables on it.
It's like, Okay, what what am I supposed to pay attention to here, right? You deliver a presentation, you talk to your stakeholders, you interact with your co workers, and they're all like, we got too much data.
What do we paying attention to? Or the next step after that is your stakeholder, your boss, whoever is like I say, Okay, so now so now what, right? or Why did this happen? You know, hey, you said that the software says website visits were down 40% yesterday, why why did that happen? What are you going to do about it? So this is where data science can help to take on some of those Extra questions and start using business skills, domain expertise, technical skills, the scientific method mathematical skills, to start to pull apart the big pile of data in that early analysis and turn it into something meaningful.
Data science can especially help with understanding what's relevant, right? So you have, and it's not technically the same in data science, more advanced analytics, where, say you have Google Analytics, Google Analytics has hundreds of dimensions and metrics available to you, if you go to the Google Analytics website.
You can actually see lists of all these things that you can analyze.
And it's kind of like Microsoft Word and a lot of ways right? There's so many different options because so many different people use it in so many different ways.
So you have to figure out which of these variables these metrics and dimensions that are available which ones actually matter to your company, and That's where data science helps.
Four methods in particular, can help turn that huge pile of data into something more manageable regression, which is figuring out essentially what numbers have the highest association to other numbers.
So if you have a, a, what's called a response variable, like conversions, goal, completions, revenue, you can then do a regression, multiple regression, of all the other stuff to figure out which ones have that association and those may be the ones you pay attention to those clustering we can make different variables sort of plotted out and figure out which one's kind of clumped together with classification for non numeric variables like good customer and bad customer, the domain name so if you're analyzing your email list, does the domain matter does a webmail address matter and dimension reduction where you're using math mathematical techniques to to boil down into just a few couple of dimensions the all this data that you have these four techniques and they are data science techniques, their advanced analytics techniques statistical nature can greatly assist us in figuring out what really matters.
And from there, we can then dig into Why Did something happen? And that's where you'll see a lot of that hypothesis testing that makes up actual data science.
Once you have figured out Okay, these are the variables, these are the five things that matter maybe it's unique visitors the website or returning users or people who visited this page, whatever the case may be, you can then set up a hypothesis saying if we increase visits to this page by 5%, we will see a revenue increase of 10% right because remember, our hypothesis has to be provably true or false, single variable statement So that you can test it.
That's where data science can help us in our analytics process to boil things down, and then set up the testing conditions that we need in order to validate and build that, that theory, that model of how we think our data works.
Now, one caveat with that is that you can't create something out of thin air, right? All of the data science work that you do in your analytics is going to be derived from your initial data source.
You can do a regression, you can do measures of centrality, like means and medians, and modes, and distributions, and all these things.
But you're still taking that data from your initial data from where you got your data like Google Analytics.
And that's one of the reasons why domain expertise matters so much, because you're never creating anything.
You're always working from your initial data if your initial data has issues with it.
You need to know that in order to either stop implemented maybe to fix the data using methods like imputation you will have to do essentially some some management of the data and possibly bring in more or additional or supplementary or, or countervailing data to work towards, that hypotheses are just trying to prove.
So again, these are things that, as a data scientist, you would need to know, in order to be able to get the most out of your data, you need to know the business, right? If, if you see an anomaly in your data, and you don't know that the brand manager, it was on vacation, that we can stop running ads for the brand, you're like, wow, something's really unusual here.
You need to know what's going on in the business.
You need to know what the goals are the business and then you need to be having enough of a command of the statistical and technical methods to figure out okay, what approach Do we need to take when trying to solve this problem? So, to summarize, analytics can't be uncovered by data science, more or less, what we can do is find what's relevant, start to pick apart perhaps why and help reduce the amount of data clutter to those things that we can then build solid testing on to build a model of how we think our marketing is working.
So really, like I said, it's interestingly where the question I, I think I know where it was going, and there's a lot that you can unpack in your data.
So start digging into some of these methods, particularly those the top fourth regression clustering, classification, dimension reduction, those four are going to give you a lot of mileage out of your initial data, and help you clarify what's really important.
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