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You and your data science team

Watch Alex Cowan provide a working view on data science and what it means.
In this video, we’re going to talk about how to create a successful interface with your data science team. Your inputs to the data science team are not that different than the inputs you would give the development team. They are vivid descriptions of the customer journey and the customer experience, probably more so with the product as it is. And of course data, hopefully coming from your own product or your own infrastructure, your touch-points with the customer, because you can’t do data science without data. And back to you, the data scientists are going to try things and give you inferences and maybe some ideas on how to act on those.
You’ll try those out and that’s how you create a robust interface here with your data science team is through experimentation. Because even though it’s called data science, it is like any science experiment, you’re going to try things that work out well and move the needle for you and you’re going to try things out that don’t. You need data to do data science. So one failure mode you want to avoid is that you’re not collecting any data off your product or your touch-points, because that will make it really hard. It’ll create certainly fewer opportunities for your data science team to do something valuable for you.
And my advice to you is to pay special attention to acting on the inferences that the data science team gives you, and iterating on those and trying them out. Because even if early on, they’re sort of rough and you’re not able to use them that well. This is such an important area, and such an important learning opportunity for you in your career that I think this is an area I would describe special importance to you even if it’s not in your A-list at the moment. The data science process we have in here, we have questions of interest
and, an idea of what’s the user experience? How does the user interact with both the product and kind of like, lets say our support system or all the different touch points that we have with them? And from here we get insights and hopefully those are actionable for us. And then we’re going to see if we can move the needle with those and we’re going to iterate through these things and try different stuff. One of the interesting things about the practice of data science is you have to be able to kind of move forward and move backward.
By forward, I mean the best way to get a good experimental result is to go into it with a nice strong hypothesis, and that is where these inputs come into play, questions of interest, vivid depictions of user experience. The way that you move backward is a little less obvious. This is something in fact I’ve been learning about myself. This is a case that we use here at Darden to teach data science called movie lens. This is a plot of two factors, factor one is here and factor two is here, and these are movies.
This says Dungeons and Dragons, this says Jackass the Movie, and this is a plot created by the machine intelligence about the variables, the attributes of the movies that are most likely to explain people’s different preferences for them. So for instance, this might be a scale of happy to sad, and this might be scary to not scary. I don’t think that’s actually what these things are, but this is the kind of output from your data science team that you might need to kind of work on with them a little bit, to understand in human terms, to make actionable for next step and testing.
You need to make sure that you’re collecting data off of your product. Your data science team will help you with that but let’s work through a simple example. Let’s say there is this company Enable Quiz, and they offer light weight quizzing solutions for companies that hire a lot of engineers so that HR manager and the hiring manager can assess whether somebody is really skilled in something, really experienced in something or not. And then through that, they can assess the fit with the organization.
So, let’s say Enabled Quiz, we have inside the company, we have this customer or user of the HR Manager whose job it is to get candidates and the Hiring Manager whose ultimately going to be hiring these people. These two work together inside the company and then we have job candidates. And we provide a quiz that they use to assess the fit, okay.
So what data might the product manager of this product collect from the customer? One idea is for giving these quizzes, we might want to make sure that we’re instrumenting and we have ready access to the pass rates on the individual questions because a really, really unusually high pass rate. My main question is too easy or too obvious, and extraordinarily low pass rate might mean their question is too hard, or poorly worded, or just playing wrong and you want to fix it. That one’s an easy one because the instruction of assigned team here, we get this data from the learning platforms that we use. What else might be interesting? Well, this company is in the general space of skills assessment.
So they might want to know that for a given type of job that they’re screening candidates for, what skills do they select to put into their quiz? because let’s say their quiz can have multiple skills in it, Ruby on Rails, Linux Systems Administration, whatever. If they collect that and they have data on the success rate of different hires, they might be able to proactively suggest to their customers what skills and what kind of quizzes they might want to make for a given job description that they could analyze, and that will be really great. Anything that makes the customer’s job easier is really great.
If we go back to Google Analytics example, if Google Analytics can accurately predict exactly what I had to do with my website to make it perform better, well that’s a much more valuable product than a product that need to dig through a lot of analysis and make my own inferences by hand with. And that’s what’s so exciting about the future of data science among other things. So, we’ve talked about how to create a successful interface with your data science team. We’ve talked about some of the activities you guys might undertake in your collaboration.

In this video, Alex introduces the field of data science, its history, and how it is an intersection of substantive expertise, programming, and math & statistics. As an example of data science in one’s everyday life, Alex mentions the Assistant function in Google Photos. Have you used this, or a similar function, and thought about how you were utilizing data science?

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