Many of us are stuck at home right now, due to the Covid-19 pandemic. There are pros and cons to this. We have less of a commute, more quality time with people in our households, and time to do little tasks we’ve been putting off. On the flip side, it can feel isolating, basic necessities are much more of a concern, and every day often feels the same. Today we talk about taking advantage of extra time by upskilling in economies that may suffer as a result of the pandemic.
Ryan Nokes: And as everybody is sitting at home kind of thinking about the economic implications or potential economic implications of what’s going on, upskilling and improving your skillset is a good activity to be engaged in.
Ginette Methot: I’m Ginette,
Curtis Seare: and I’m Curtis,
Ginette: and you are listening to Data Crunch,
Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world.
Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.
Many of us are shut in right now, so why not use this time to upskill? Brilliant.org is a great place to learn important data-related skills. Their classes help you understand algorithms, machine learning concepts, computer science basics, and many other important concepts in data science and machine learning. The nice thing about Brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code writing, and interactive challenges, which makes them entertaining, challenging, and educational. Sign up for free and start learning by going to brilliant.org/datacrunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription.
Curtis: So we wanted to talk a little bit today about Covid-19. By now everyone knows what that is. We know there’s going to be some pretty severe probably economic impacts, health impacts, and societal impacts. And we’ve noticed some interesting things in our podcast data, actually, as we were looking through it. The last two days, we’ve had 10x the listenership on our podcast, probably because a lot of people are at home and they’re working from home and they have some extra time. And of the episodes that have been listened to, we noticed one in particular seems to stand out and that is the episode dealing with how to get into data science. So it sounds like a lot of our listeners are . . . have some time on their hands. They’re thinking about maybe a career change or thinking about upskilling themselves as they’re at home and working from home.
And so we wanted to address some of those things. We train lots of people in fortune 500 companies across the country, how to learn how to be good analysts, how to get into data science. And so our CEO is joining us on this episode, Ryan Nokes, who’s been in this industry for over 10 years. He’s trained for over four years and has some thoughts about data science analytics, how to be a good analyst, some things that most people don’t often think about. And, uh, some things that you may be surprised are what really matter when it comes to analysis.
Ryan: Thanks, Curtis, for having me on on the show. It’s good to be here, and as everybody is sitting at home kind of thinking about the economic implications or potential economic implications of what’s going on. Upskilling and improving your skillset is a good activity to be engaged in. So why data science, right? Why, why is that such a popular skill and continues to top the charts year after year. And as a corollary to that, I saw this question on the website Quora the other day. Someone said, “if I could learn Python in a couple of weeks or a couple of months, why are companies paying so much for it?” And the answer is it doesn’t matter how long it takes you to learn the skill. In fact, the technical is really not that important. As a CEO, whether you program in R or you program in SAS or you program in Python or or whatever. What I care about and what what leaders of companies care about is can you solve expensive, difficult, thorny problems for me? If you can do that however you do that, I’m interested. And that’s why data science continues to top the charts year after year is because data science promises to solve big thorny, expensive problems for companies.
Okay, so here are some, some examples, particularly in uncertain times like this. How do we find cost savings right now so that we can preserve cash as much as possible while at the same time not having to let people go. Okay, that’s, that is painful. No one wants to lose their jobs. No one wants to be the person letting people go. I certainly don’t. We are trying to find every opportunity we can to preserve our team, to preserve our cash, to preserve the projects that we’re working on, so how can we do that creatively? How can we find cost savings in our company? Maybe there’s software licenses that we’re paying for that we don’t need, maybe their services we subscribe to that no one is using. Maybe there are cheaper options. Maybe we could automate repetitive tasks that we are doing all the time. Maybe we can look for ways to help our people accomplish more without any increase in budget or without any increase in additional head count. Those are things that data science can help us do or how can I better target my customers? How can I improve my marketing spend? How can I find who’s most likely to respond to a particular message? How can I look at who’s likely to defect and or churn? These are all things that we can look at that data science helps us fulfill, and whether you’re using Excel to do this or any other program, what I care about is the problem because those are hard problems to solve.
When times are good, how do I know when to hire? And how much should I pay them and what is the effect of hiring somebody and the effect of their salary on our overall bottom line? How long ’till they become accretive to the business? There’s carrying costs as people get up to speed Then they start to become accretive to the business, and so we need a model for these things and say, okay, well we can afford somebody at this rate doing these things and we’re going to carry them for X amount of time. Maybe we want to model some scenarios. What if they quickly get up to speed? What if it takes longer than we expect? What if we need to move them to multiple projects? Can they handle that? So these are decisions that companies are wrestling with that the promise of data science and good analytics and good data visualization can help solve.
So this is why I think the skills top the list, and this is why it’s valuable for you to know. It’s not so much about the technical, it’s more about understanding what companies need and are looking for and how can you use your technical skills or how can you acquire the technical skills to solve hard business problems. Just knowing the technical and not understanding the business problems that keep business leaders up at night will not help you in your career. You need both. You need to understand the strategy and the thorny problems that keep leaders up at night and what they’re working on. Then you need to be able to come to them with a plan and say, “I understand your problems. Here’s some, but if you have others, tell me what are some of the problems you’re thinking about? What are you working on?” Then go find data, whether we have it internally, whether it’s from external sources, if we can buy it, you know, find it. We can cobble it together. Can we simulate it? Help us find data and then come back to me with solutions and then say, “okay, here’s how I think we can solve this problem. I ran this model, I did this analysis. I built this dashboard.” Open up your model so that I can see it. I can look at your assumptions and I can see, did you miss anything? Can we trust this data, K? Then we need to talk.
Once we have a model that we can trust or a visualization that makes it intuitive for us to understand and make decisions on, then how do we productionalize this? How do we get this to the front lines so that the rest of our company can use this, can make decisions on this? If it just lives on your computer and your head, it’s not good enough. We need to find a way to embed this into applications that people are already using. Maybe that’s, that’s email. If we’re going to, we’re going to email out insights. Maybe it’s going to be a team meeting. Maybe it’s going to be in Slack. Maybe it’s going to be in Salesforce or in HubSpot or in, you know, something else that we’re using all the time. But we need a way to productionalize the insights that you are finding and get them out to people so that they can make decisions. And finally, once we’ve done that, we need to be able to track the results. A report or a model or a dashboard is no good if people can’t, can’t trust it, one; interpret it, two; and take action on it, three. We need them to take action.
Reporting or data science by itself is no good, literally no good unless people take action on it. So as you are learning data science skills or you are thinking about how can I improve my skill set, how can I create value, especially in like uncertain economic times. It’s this: it’s understand our problems, understand what keeps us up at night. Then come to us with solutions on how to solve that in a way that people can both understand, trust, and take action on, and then let’s put a process into place to measure the results. If we take this action, did we get the result? If we wanted a 10% cost reduction, did we achieve it? If we were trying to figure out who to hire or who to fire or can we give raises or can we give bonuses or are we going to meet stockholder returns and our quarterly estimates? Are we doing these things? Are we off, are we within our range of margin of error? These are the kinds of things that we need to do.
Okay. So as you are learning data science skills, learn Python, learn data visualization tools like Tableau, learn to be great in Excel, but most of all learn how to solve problems and communicate results effectively. So that would help me, that will help a lot of other business leaders out there. So as, as you do this, look for courses that offer that mix of both the technical as well as the business acumen and communication and presentation skills.
Curtis: And I would add to that that this is skill that is learned over time. So just like Ryan is saying, if you want to be good at understanding people’s problems and communicating with them and really helping them solve those problems and bringing very technical skills to bear in a way that other people can understand, it takes a lot of communication with people and asking them questions and learning how they respond to what you present. So be prepared to spend a lot of time talking to your managers and your leaders. Ask for time with them and ask them what their problems are and then bring them solutions and listen to how they respond to your data. And that will show you maybe where you can be better, a better communicator, a better problem solver, and more valuable to, to the company that you are working for.
So along with all of this, we have decided to open up some of our courses. Historically, we’ve done big corporate trainings and uh, that requires trainers to be onsite in big corporate campuses. We’ve been doing this for a while now, but because of Corona, that’s no longer possible, at least for the time being. So we’re opening up some of our courses to be on demand so anyone can access them from home. You don’t have to sign up and wait for an instructor. It’s all on demand training, and you can go and learn some of these skills.
The first one that we’re opening up we hope will be not only interesting but also maybe a little bit entertaining. So it’s a training that’s based in a story. So it’s a learning Tableau, which is a data visualization tool, which is very standard, easy to pick up and easy to use to solve people’s problems. And it’s based in a world where the zombies are taking over. So the zombie apocalypse is happening and you as an analyst need to figure out what’s in the data, what are the insights in the data so that we can stop zombie apocalypse.
So if you’re interested in learning Tableau, interested in maybe a more entertaining, interesting way to learn that, then then you can find maybe elsewhere, ah, head to datacrunchcorp.com/zombie, and you can read the story there. You can sign up for the course, and we want to know what you think. Hopefully, you know, we designed this course to be really interesting and helpful in helping you not only learn the technical skills but also learn how to think like an analyst.
Ryan: I’ll just add to that our philosophy is “thumbs down to boring.” Don’t be boring. Solving problems is fun. It’s interesting, it’s challenging, and we think that if we can teach you how to do that as well as give you a fun story and not be boring, then that’s a win for everybody. So have fun with it. We’re people just like you—we want to have fun and we hope at least that what we’ve created is both fun and instructive and will help you in your career and not be bored and especially as we’re all sitting at home waiting out this virus. So best of luck to you all. For those of you who are going to go save the world from the zombie apocalypse, this is Data Crunch extending our thanks over and out.
Ginette: Thanks for listening. Again, if you want to beat the zombies while learning to use Tableau head to datacrunchcorp.com/zombies. As always, for our transcript and links to attributions, head to datacrunchcorp.com/podcast, and we’ll see you next time.
“Loopster” Kevin MacLeod (incompetech.com)
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