What is Data Science?
We are happy to announce our first episode of Data Science Wednesday featuring our Data Science Team Lead Tessa Jones. Check back every Wednesday for a new video on data science.
Video Transcription
Hello, and thank you for joining us for Data Science Wednesday. This is our first episode and we're really excited. We thought that it would be a good idea to answer the question on everybody's mind, what is data science anyway? So before we really get into the nitty-gritty of what data science is, let's talk about how we can identify data science. What situations are you gonna be in where you can apply data science? So, we broke this down into three different words, why, future, and action. Why. Why is this happening? We've all been in those meetings before, right? The executive teams pulling out their hair. They're asking why did this happen and they want you to answer that question. Data science can help you in those situations. The next word to think about is future. Anytime you're talking about the future. "How many of product X are we gonna sell next month? How many salespeople do we need to have on the floor?" Anything like that, anything related to the future, data science can help you.
The next, here, is action. This is where everybody wants to go right out of the gates. They want to know, "Well, we think this is gonna happen. What do we do about it?" They need an action. They need something to drive their business. This is where data science can really help you. So, now we have a pretty good idea of how to identify situations where we can apply data science. So, let's break this conversation down into four categories. This is the industry standard for how we can think about data science. We have descriptive, diagnostic, predictive, and prescriptive. We're gonna go ahead and explain this using simple words again.
Descriptive Analytics
For descriptive, we're gonna apply the word "what." "What is going on? How many gumballs did we sell last month? What is our year-over-year profit margin?" Anything like that, anything where you're asking what is going on in my business, that's when you're in the realm of descriptive analytics.
Diagnostic Analytics
The next one here, diagnostic, that's when we start getting into the "why" that we were talking about before. So remember, you're in a meeting and the executives are breathing down your neck and they really want to know why is this happening. Diagnostic analytics can really help you with that. So, for example, you might be able to find out that a given product sales are driven by the age and the zip code of your customers. So, now you know why your sales are driving up or driving down. That's pretty great to know.
Predictive Analytics
The next one here is predictive, which aligns with future. We're gonna look at our crystal ball and we're gonna figure out how many of this product do you think we can sell in the future. So that's when we're applying predictive analytics.
Prescriptive Analytics
The next one here is prescriptive. And that's when we really get into the meat of where everybody wants to go first, which is action. What can we do, right? So in our given example, let's say now we know why our sales are driving up based on the zip code and the gender of people buying this product. So, now we can apply prescriptive analytics to say, "Well, why don't we do a focus marketing campaign targeting this zip code with this age group with this product on the cover?" This is when you're applying prescriptive analytics. Great. So, now we really know how to identify data science when we are answering questions around why or we're talking about the future or we want to have an action to drive, we're in the realm of data science. We also know how to go through data science. This right here is really the meat of data science. Descriptive analytics is really more of the foundation. You really need descriptive analytics before we can do any of this.
So, thank you for joining us. That's data science.
Posted by Gage Peake
