Our guest today is Fernando Hidalgo. He’s a self-taught coder and is currently a data scientist at Discovery Communications. He is interested in startups, block chain technology and helping others navigate the tech career field. Let’s hear what he has to say about data science and learn how you can follow in his shoes to earn the tech career you desire through online courses and personal projects on your nights and weekends.
Ross: Fernando, Welcome and thank you for joining me this afternoon.
Fernando: Hi Ross it's a pleasure.
Ross: Will you please share with the listeners about your career before you decided to learn data science and kind of what motivated you to take the leap.
Fernando: Yeah of course before I change into data science I was working as a teacher's assistant for a special Ed classroom. But I didn't feel like there was a lot of options. So I studied economics in school. So I was looking at what new, what's liked in the frontier of economics. Just like googling probably you know just like on weekends and I found data science. I thought there's like a lot of potential and a lot of power for like just an individual person. So I just started coding at night and now I'm going to you know now I just switched careers.
Ross: So really you started by looking at what careers maybe had the most opportunity for them or so you weren't getting into a spot that was going to be taken over by robotics automation but also had tangible outputs that you could and kind of feel fulfilled by.
Fernando: No absolutely. I looked at companies that were using data science. There’s this company called Premise. I found Premise online and they do they do amazing things. Like an example would be if they want to go to like Argentina or Ecuador and they pay people to take photos of staple foods like every day at the same time. They take all this data they use a computer vision to get the price of the food and the quality of it. Then they calculate economic metrics on their own aside from the government and now they're able to get interest for these areas for app from outside investors. So like these areas like even the government of their own countries didn't have enough money to track it. But now they have economic data on these places, investors able to put money in there. It allows a lot of growth for places that maybe couldn't before and this is just using it's like a startup and they're making a huge change using scalable technology and I know that really excited me. I think like working on problems and really motivated me to change careers.
Ross: Yeah mean yeah it's exactly. It’s a great example of it. I mean when you were, when you first were thinking of different career paths that you want to go into or different companies you want to work for, I mean did you just go on Indeed or just go on monster and start looking up employers of data science or kind of how did you approach it initially when you were kind of starting fresh?
Fernando: I didn't really go toIndeed. I was looking at more I guess what companies were doing like their goals. It’s like premises one of them. There’s a bunch of companies using like satellite data to optimize how cities are built. There’s people that are using Twitter data to understand trends and like culture. That’s so exciting and it's so powerful. like one person can just go online you know Twitter, right now Twitter allows anybody to just grab their data and companies are just using this to you know write really interesting insights on culture or predicting things and that really really excited me. I think it indeed if you go there there's nothing, it’s not very exciting like what they're talking about there. Because they want you to do specific job. But I think there's something to that. But what is really exciting about the field is the bigger goal, the bigger missions.
Ross: It sounds like you're looking more at the news and seeing what the companies are, what their outcomes are how they're using certain types of technology and new groups of data to be able to do great outcomes as opposed to I've heard of Google, I've heard of Facebook. Let me see what jobs they're hiring for and make myself qualified for that. Instead you're looking at what companies are doing things that I can relate to and feel inspired by and then how can I prepare myself f to add value to what they're working on.
Fernando: Yeah and you know I would argue that if you have this larger mission, maybe if it's like very ambitious. Even if you don't achieve that you've accrued so many skills that you could apply for those other jobs indeed. Because you've like overshot.
Ross: So speaking of preparing and warning online, I mean what did your schedule kind of look like while you were working full time still and still trying to learn data science online?
Fernando: So what usually happen is like you know I go to work. So when you're a teacher's assistant in a public school you get off earlier than most people. Right now I get off at me would stop working around 6:00. When I was at school I'd stop working around like 3:30. So it was like a little earlier. I'd go home, I take a nap, eat and then work for two hours and then go to sleep and then wake up and do the same thing every day. And then on weekend I probably work like four hours, you know Saturday and Sunday. But it took me like a while to get there. At first I was just doing you know I would create like tiny habits, like do one line of code a day. You know that maybe that happened for like two months and then I’d do like 30 minutes and then I added like you know to an hour and then eventually its two hours.
Ross: How did you determine kind of start that way? I mean it's a great method and something that our listeners could certainly implement their own lives to get the ball rolling for whatever they're trying to achieve as well. But I mean how did you know to start with something small and then kind of build in to, build some more momentum into having some great outcomes as a result.
Fernando: I read this book a couple of years ago. It’s called “eat, move, and sleep.” I think is Tom Roth. He mentions like if you want to build an exercise habit, do one push up a day. So after I read that book I did one push up a day and eventually I was doing, I was like running like maybe like half a mile to a mile a day. But that's what I do now. I was doing like you know more than one pushup. I was doing a lot, it built up to a lot. So I had seen how powerful that idea is. So I just used for coding and I would recommend other people if they want to really build habits. Like learn how to build habits. Like start with exercise. Like do a push-up a day and once you do that you feel satisfied and eventually you see how powerful that is.
Ross: I mean while you were building momentum and I'm sure there were days that you missed occasionally and did you have other setbacks or other self-doubt while you were going on this mission or were you confident that hey I'm doing this, I'm building momentum. It’s going in the direction that I know is going to be beneficial or I mean what kind of roadblocks or speed bumps that you get along the way?
Fernando: I think when you're going I added alone. Sometimes you don't know if you're going the right direction and there is a lot of doubt. During this I think there's doubt. Because there's no context am I making the right progress, am I learning the right things? Like how would this transfer into a job and actually one of the things I did was I started looking for classes and I took a GA course and but to tell you that didn't help much. But one thing I learned is I eventually went to a boot camp and what I learned there was all that matters is your projects.
Ross: In terms of qualifying yourself for employment?
Fernando: Yes I think a lot of times people are a lot more ready than they think they are and you just have to have the right projects to do it. That’s what I think eventually landed my job. But before this you don't really have any context. You’re not really sure if you're doing the right thing or going at the right pace. There’s tons of doubt.
Ross: And so I mean what would you recommend in hindsight now that you've gone through it? I mean do people get a mentor or are there other resources that they can leverage to kind of build some more confidence that they are going in a good direction or are there anybody they could talk with to have some validation of the efforts that they're putting in. so they feel like they're less on their own.
Fernando: I would suggest first creating projects and sending them to employers and seeing what you get back or if you're just starting out and you want to work as a data scientist for like a well-known company or behave like a full time jobs of data Sciences, I would suggest to test out the market first. Try to be a data scientist in like a small capacity.
Ross: As like a freelancer or just like a junior data scientist professionally?
Fernando: I would say something part-time or where you're doing it for free. So right after my boot camp, it wasn't easy getting a job like after my boot camp. But because I didn't know how to approach job hunting and one of the really powerful ways that I was able to you know eventually get a like a good job was I went to this talk and this guy was, it was basically him alone. He started this company where he was using social media data and writing articles about it and at the end he said you know if anyone needs help like if anybody wants to help me with this projects then reach out to me. After that I emailed him, I talked to him and I built in the back end for the analysis. So I would use like automation to get all the Twitter data analyze. See if there's any topics that are interesting there and then let him know about it and then he would write it and I told him, I negotiated. I was like ” hey by the way I did it for free and I told him I want the title of a data scientist for this company.” so he would like publish things like maybe fortune would buy it and then he would put me there and now I had the title of a data scientist and my skills were being proven in a real-world scenario.
Ross: Right so you were kind of learning on the fly and maybe a more cautious situation than being on someone's payroll with high expectations. But really you were able to pursue a project that you were interested in learn from it and build your skills along the way and then also build credibility as a result.
Fernando: Yeah and I think also testing the skills like it's like validating my skills. Even after the boot camp, one of the things that you learn in a boot camp is; it's not even the skills. Because you can't learn everything in three months. Like people think you're going to go there and learn and then after that you know everything. It’s not like that at all.
Ross: Right, you’re not 100% job ready once you graduate. Yeah it takes more work on your own and more projects and experience before it's time to make that leap.
Fernando: No absolutely and I think what the thing that I learned the most probably from the boot camp was like projects really matter and just that you're probably ready before you think you are.
Ross: So what was the curriculum actually teaching and I guess what boot camp did you take? What kinds of things did they teach and then how well did that align with the projects that you worked on initially when you were doing some of those, the Facebook analysis and even now for the work that you do full-time. I mean how well they taught you to prepare for or prepare the skills that you needed even before you got to the projects and the other things.
Fernando: So you're saying like what I learned there and how am I using it now? How much it translated?
Ross: Yeah what are some of the skills should people expect to pick up if they take a data scientists boot camp?
Fernando: So I think you learn Python pretty well. The necessary things. Like what's the bare necessities. You don't learn like additional things you don't need and they teach you like bases for machine learning. Like the abstract ideas. But what I think is the most powerful and I think anybody could do this. You don't need to go to a boot camp to learn this is just like start projects. Like’s the most important thing. People came in there and you know you have this idea that you have to learn theory or you have to like read books or you have to take a lot of these courses. But all that matter was projects. That’s it.
Ross: So if you have somebody listening right now who hadn't considered data science before? They aren't sure where to start, they aren't sure if they're qualified. If they have any prepress or anything like that. What would you recommend they start? I mean should they, I mean do they need a certain math background or could they go on Khan Academy and in a few weeks or months prepare what they need to do. Are there certain books or online tools that they can, I mean cut ultimately we need to build some familiarity with Python and maybe some of the industry standard software to even start working on projects. Where would somebody who's completely fresh, where would you recommend they kind of take off from?
Fernando: So I would recommend there's a course on udemy. It’s called machine learning in Python boot camp by Jose Portillo and that course is, it's based on projects you learn. Theory as you do the projects and it teaches you the minimum amount of Python to do the work. A lot of courses just teach you so much more than you actually need or it's all theory you don't know where to use. Like you don't have no idea how to use it. That course I recommend it so much and it's like an amazing course. I think a lot of what I learned in the boot camp is actually in there.
Ross: And so you found I guess inspiration for one of your first projects by going to that seminar and hearing this founder, the startup founder. He gave a speech about some of the unique things that they were doing with data. How would you recommend that certain people find those first projects or get connected with some of those people that can help them with the initial work, initial projects to start building their credibility and build their experience?
Fernando: I would say you can go to meetups and you can talk to people there. But I think the most useful part though probably one of the most powerful ways to do this is to find a project that you're interested, get some public data about it and then publish it online. I've seen a lot of people get traction that way.
Ross: Just Google for something that they are passionate about and I add public data. add some way that they can find a repository of information they could build from and then maybe once they have their hands on it and they've had a chance to tear it apart and find outcomes or discover certain conclusions from the data, then they could from there have a much more targeted group of companies or people that they could search for to share those results with and to turn heads and get noticed.
Fernando: No absolutely and I think there's a lot of people out there that are like if you offer your skills for free and you have a really good portfolio, they're willing to help. Like they're willing to just like help you out. But you have to have I think just because you do it for free, doesn't mean they're going to help you. You also have to be pretty like have something to show. Because if you're offering something for free and you don't have anything to show that means that the person is going to have to train you and basically they're spending their time. So you have to have like a portfolio ready.
Ross: They don't have any confidence that you're going to be able to deliver anything for them and you might just be wasting their time and whether or not that's true that's how it could appear from their perspective.
Ross: So you mentioned twice that that people often don't feel ready even though they probably could be to start a career and start working full time. so I mean when did you feel ready to start working full time and when do you think, what part of this process do you think people should start looking at jobs and starting to apply themselves full time to this. If they're like you are, they’re working in a job now that's unrelated and they start building up in the evenings. At what point do they to actually start pursuing these projects and making the leap in changing careers.
Fernando: I would say, so in terms of like starting projects I think you should start doing projects like almost like day one. If you know the basic Python you should already start doing projects. That’s the most effective way to learn and while you're and the second benefit is that while you're learning you also documenting your projects and building your portfolio. It’s the most efficient way to learn. The most efficient way to change careers. So in Jose Proteus course you do the projects, you're learning. But I recommend having a website and every time you finish a project put it on your website and then describe the business problem. Then by the end of the course which I don't know it could take like a month or two you already have like five projects in your portfolio. Super powerful.
Ross: How would you recommend that people, again so if they are starting fresh, they've learned a bit of Python. They’ve started getting their hands dirty and getting involved in personal projects. But they don't have any experience with putting up a website. How would you recommend they do that? I mean are you getting a Blue host account and Word Press or instapage or kind of how did you create that website since you don’t necessarily have a background in creating them at all either.
Fernando: So I used the actually Squarespace. I can create websites but they're not going to be as nice as Squarespace and it's going to take a bunch of time. So I went online and I just googled data science blogs and then you see some really crazy ones in there, like really really great and I just copied them. And I took whatever I thought was the best and then I created like for each project a little description, business problem description. Because that's what like hiring managers want to see. They want to see that you're breaking down problems. They just don't want to see code. Code is important. But they also have business sense. Right now that we're actually hiring a discovery and that's what we're looking at a lot. A lot of people can code, but not lot of people can have like intuitive sense of problems and so I went online, I saw the best layouts or data science probably. Like way that people were framing it. I just copied everything and then I linked all my code to github. Yeah that's how I created my website.
Ross: And you touched on something important there and the fact that you need to understand who your audience is and what it is that they're looking for. Because in most cases businesses are using or looking to use data to create meaningful outcomes. Whether it’s for their customers, whether it's for the products that they're building or even if it's just conclusions and making sure they're making the right business decisions. And you may need to make sure as a data scientist that you're not just spending your life behind the computer coding all the time that you're actually understanding what's important to the business or if you're applying for a job and you're trying to convince them that you're the right candidate. What are the challenges that they're facing? What are the opportunities that that business has to be more efficient with how they're operating or with anything with their strategy and I think that's something that's very important. I am going to take a step back to the website and so when you are creating, when you're like trying to tell your story. You can't just say here's a business challenge or something that I'm trying to solve. Are you telling the whole story of here's the information? That I got and why it's reliable and the initial things I found from it and then how I kind of like telling the story of how you got to the final conclusion or what is it look like? What are you actually posting on that page?
Fernando: So in my website, it's very short. It’s like this is the problem. This is what we're trying to do and these are the outcomes and if you go to the python notebook where the code and everything in description and then I had more details. So then I asked like the type of data, how I cleaned it, what type of models I used, what approaches and like all these little… it's more detail-oriented. But for me it's not that important. I don't know maybe somewhat people are going to disagree with this. But on my website it's more of like exploratory. Like people want to go in there and check it out and they're going to find what they want to find. But when you're looking for a job I specifically create links from where what I want the higher manager to see. So if I'm sending a project out… when I send a cold email to a hiring manager, I don't just like send my website. sometimes I do. But sometimes I just show them. I create a link that shows him exactly what I wanted to see. What they want to see.
Ross: So you're making a page specifically for that company.
Fernando: Not necessarily, but I created maybe like a specific project that I think is very relatable and before I created my projects I kind of created different ones to show different skills. So it's like a varying amount of skills. So then now I know like you know maybe it's a visualization company. Like they want really desire to have a visualization project. Then I have a one specifically for forecasting more analytical then I do this. Like so I created my projects to fit that kind of, just to fill different needs and then when I go to a company, I have like a bunch of tools that I can just send out.
Ross: So if people are looking to see your website and see the projects that you're working on or at least have published, how can they find you?
Fernando: So it’s at a fernandodata.com. You could just, I would actually recommend… I tell a lot of people just go to that website, copy everything and then just like put your projects instead of mine and then you use that to get a job. I don't think you should create something from scratch.
Ross: Agreed and that's something that you can definitely benefit from others is what can see what they've done and what's been successful and then how you can retool those best practices to fit what you're working on without sending the same exact URL to a recruiter as well. And is that website have links to your Gits and some of the other resources as well?
Ross: Perfect. So when you were sending the emails to recruiters, are you basically saying hey I'm Fernando and this is what I'm trying to…? I'd love to work for your company. data science, here's a great case study or here's a project I've worked on that's relevant to you and your business or how are you framing that message and how are you finding the recruiters to reach out to? How are you getting their email addresses?
Fernando: So I think there's like two types of emails I sent. One was for to learn about the company more. So it's like someone other data scientists. That one is like maybe two paragraphs. It’s more about like my interests, why I want to learn about the company. It’s a little longer. Because I'm talking to somebody like in the same position. Just like giving more of a flavor of who I am. Then the second one is hiring managers and those are like very very short. So I say, it's like three sentences. So it's like, hi my name is Fernando I work at X. it's already saying like hey I have experience. So before I used to say like you know I work at Prizmo G which is like that startup. I'm a data scientist at Prizmo G. So you're saying like, you're creating like a validation in the first sentence right.
Ross: I am somebody qualified for what you're hiring for.
Fernando: Exactly. I think that's why it's also important to have some sort of position even if you're working for free. Once you have that title everything changes like and LinkedIn people reach out to you like its changes completely. But if you're saying like hey I have some projects and it's not as strong as saying like hey I am this. But it's so doable. Even if you're working like two hours a day for a company and but have that title, it's so powerful.
Ross: So again like you're saying where people are working for free or they're working more in voluntary basis just to build that title and that credential. So that when they're trying to get something full-time and secure they have that credibility.
Fernando: Exactly and I think I'll mention something that we talked about before. And why I say that people, I think people are ready before they think that like people often study too much like way more than they need to before they could get a job. When I started you know when I started working or meeting with other data scientists you see that the field is so large that not everybody knows as much as you think they do. That they, you're actually very close to where they are. Like if you know Python, if you know SQL, if you know basics of machine learning, if you can build a model like which I would argue doesn't take that long to do. Because a lot of this is automated in Python. Like the machine learning part. You have the ability to learn other skills on the fly and that's what data scientists are doing. It’s not that they come in there and know everything. It’s that they're learning as the problems come in. so it's not like they have this huge advantage over you. What’s more important is showing the employer that you know business problems and then also framing your experience to like having the right like… I don't know if you heard about like credibility triggers in your resume to you know have that trust from the employer that you can do the job and you can learn. So two people can have the same amount of skills but one person has like I don't know like a master's and one doesn't, that person's going to have more credibility. But if the person doesn't have a master's creates the right narrative in their story you know has the title maybe what they're working for free, they maybe give a talk somewhere. Then I would argue have the same credibility as a person with a master's. If you create you know if you go for the right company and if you frame everything the right way. So that's why I think people often study more than they need to. Because they have this idea that the data scientists working now know X amount or they have this knowledge. But when you actually are there it's not as much as you think. So you know maybe going back to the email. So the first line is you know I write it I say like I'm a data scientist. You know even if you're working voluntarily its super powerful there. Then you say like why you want to work for the company. But you talk about something very specific. So this is in one line. Hey I'm a data scientist from this and I'm reaching out to you because you know I'm so excited about this and it can't be faked. You really have to do it. like it comes off you know a lot of people I get sometimes emails from people and it's like I like discovery because of this and it's I could tell it's not real.
Ross: So, they should show that they’ve clearly done the research and they know about the projects that you're working on. The problems that you're facing.
Fernando: Yeah has to be. So this is just one sentence and it's already showing like interest and credibility. Then the second line is something that they can see, a tangible thing. So I'm like you know I like this is what you're doing. I see that right now I'll give you an example of discovery. Like Oh discovery is merging with scripts, which we are. We’re merging the scripts. You probably are dealing with X problem and I've already done this. Like I've done something similar and then I put a link to the project. If you and then that's it. Like you can check this out whatever and then the last one is like hey if you want to chat about it how I could help then this and then thanks Fernando. So it's also not very pressured like hey you want to talk about a job opportunity or it's very, let's just talk about the problem. Let’s not talk about how I'm going to get a job. Let’s talk about how I could help you and so the hiring manager is very short.
Ross: Would you mind sharing one of the emails with me so I could include it in the show notes. So the people who are listening along could look at an example of what you sent to recruiter and maybe what helped, what was successful for you.
Fernando: Yeah of course and can I add one thing. Though the most useful emails I sent to was where I linked my project to an app I built. So it's one thing to show code. But if you build a machine learning app that someone goes in there and uses it, it's like so powerful.
Ross: Blows them away.
Fernando: It's like undeniable could that your skill. Like you built this website, you build this model, that it's predictive. It’s like you're also using for end to end. So using front-end and back-end skills. Having something so tangible where they can go in there and live, you know experience it in that app is so important. It’s also so underrated. I don't see a lot of people doing that. A lot of people that I interview I get a bunch of code, it's just how you have to read it. But if I clicked on something and I'm using the thing, it's so much more powerful.
Ross: And just to reiterate. so you came from background where you're working as a teaching assistant in special education without any kind of technical background at all and then using a few hours and nights in after work you were able to learn the skills you needed, build projects and then develop this app that you could show that just blow people away just kind of in your free time.
Fernando: Yes but I would also say like you know I took the Intro to Data Science course. It give me the push to do the project. So I was doing project before but not as focused and that's what I learned from the boot camp. That boot camp is actually really really good. But I would also say that if someone can't get into there I think it's doable if you focus on projects. The biggest mistake I see people make is focusing on theory and I think that the most important thing you learn in the boot camp is project based.
Ross: I mean that's a big advantage of the online courses that are available today, is that they recognize that and they teach to build your experience as opposed to teaching your theory. Like if you go to college now and get a computer science degree, because people assume ok if I went to motor I want to do something like this. I need to get a $200,000 degree from a brand name institution in computer science and that teaches you a bunch of theory. It doesn't teach you the necessary skills that make you a successful data scientist or what have you in in tech.
Fernando: Yeah I think you said it perfectly.
Ross: So when you were sending emails out to individuals specifically, did you even apply online? I know a lot of people when they're trying to find a new career, they're sending out application to hundred or hundreds of different online applications trying to find one that catches to get an interview and then from there they get maybe one job after doing 12 interviews. I mean it seems like the path that you're taking is much more direct. Would you recommend people even apply to the jobs that are posted online?
Fernando: No I would. I think you need to cover all your basis. But I also I found more like… I would definitely recommend it. But I think everyone's doing that and I think if everyone's doing something, if everyone's doing X then there's it's like over flooded with competition. You want to go somewhere where there's less competition and the only reason people don't do this is because is like you're afraid of getting rejected. So that means that there's very little people there when you're cold emailing and it gives you a like a huge edge.
Ross: Now that totally makes sense. So like so once you actually got into interviews for positions beyond what you were doing for free. I mean how did you prepare for those interviews? Because I'm sure there were plenty of technical things that they were asking you or maybe what tough questions that they ask that the others should be preparing for when they get to that stage.
Fernando: You know I think the tech part, unless you're going for like a very technical position is if you know Python and if you know SQL and you know you could just go online and like look at theory and I think you know one of the reviews is for theory is for I think interviews. it's funny what you talk about it in the interviews and what you actually uses the data scientists sometimes doesn't overlap a lot. So the technical part I think you could learn on your own. Sorry go ahead.
Ross: It seems like you have to warm both things and I mean we talked a second ago about avoiding getting too caught up in the theory. But at least need to focus on enough of it. I mean it'll help you problem-solve do get into certain roadblocks and things when you are working full-time. So it is helpful to learn the theory for sure. But it sounds like you're learning it just enough to prepare yourself in some cases for the interview. But still focusing the majority of your energy on the projects, on the actual applications of the theory.
Fernando: Yeah and you never even get to the interview if you don't have the projects. Yeah I think just the projects get you there and then sometimes you got to just learn the theory for the interviews. But I think the big differentiator is the business skills again. I think it's much underappreciated. we've been interviewing for a data science position and you know sometimes we say like this person is technical, but they don't have the business skills and how to talk through problems and I think that's people should focus on that a lot. That’s where I…
Ross: The soft skills. You can be a genius at the technical side of things. but if you can't communicate it and explain it to people who aren't familiar with what you're talking about and if you're not able to understand what are the actual outcomes that the business needs not just what are the cool technical problems I can go work on, I mean that's certainly an essential part is the communication.
Fernando: Yeah I would say more than communication. It’s more just knowing where to put your energy. Because you understand how like a business works. People that are going into data science… they don't have the business background. I didn't have that much of a business background and that's where I was struggling at first in interviews. They would ask me something and I would give very like superficial answers to business problems and now that I look back I see that it was, they weren't great answers and I see that now still some people interviewing. They don't have a concept of business problems.
Ross: So how did you come to warn those things over time or how would you recommend others become more familiar with it to make themselves better candidates?
Fernando: There was an actually a really really great book. It’s like an online book. I forget the name though. It’s by this guy that he worked at an Airing as a data scientist. Now he created this like online course. It’s in R actually. So I don't know too much on. But it was all business problems. I think it was like 50 business problems and he would, the top five he gives the answers and the rest you have to like pay for. But I did the top five or just look the business problems and you see how he solves it. I think there's not a lot of courses like that. But that book is, it's great. I can send to you afterwards. I actually have it in my computer.
Ross: We'll follow up and include it in the show notes.
Fernando: Yeah that's a really really great book. It focuses mainly on business problems. Some of the solutions there don't even use machine learning. They use only statistics in business problems.
Ross: But it's just a great primer for not only learning the technical skills but how to apply them in relevant ways to the people they're actually going to be hiring you.
Fernando: Yeah and I even used some of those words and his framing. So I looked at how he was solved. He worked in Airing. So it's like he's dealing with business problems. When he was talking about the solutions he was talking about in a specific way and what would the business needs and I would just you know you see that how he's framing it. So then I would frame my problems like that or in my website I would frame problems like that or I would do analyses that he's doing. Because that's what's important into business terms.
Ross: So about how long it taken did and how much did it cost to make this change from start to finish?
Fernando: So it took about a year and a half to do the whole thing. The first eight months I was doing it like basically on my own. No little more than that. Then I went to GA and then I took the Metis Boot Camp.
Ross: General Assembly.
Fernando: Yeah General Assembly. It was like a part-time course. General assembly was I think $5,000 and Metis was $10,000 for three months. But after that then I got a job.
Ross: And so, once you started working as a full-time data scientist what did your role kind of look like day-to-day?
Fernando: So it depends. But most of it is I would say like half at hoc analysis. So maybe like the marketing team needs X or you know some different department needs Y and then the other half is longer-term projects. So either building dashboards using tableau or doing, understanding our users using clustering. I think that's the major thing. Like understanding connections of our users like segmenting them in different ways and also understanding… it's like clustering shows so that we understand like maybe we should create this type of show or what shows go together. Just understanding the shows and the users.
Ross: And then one thing we talked about before we started recording this podcast is you like being in this role. A, because you're working on meaningful projects. But you also feel that there's more career growth and there's a future, there's a runway for your professional career now.
We talked a little bit about that.
Fernando: Yeah so I think it's important to go to an area of growth. Because I think half of enjoying a career like having a enjoying what you're doing is feeling secured or that you're not just constantly worried about money. I think that's half of it. you can do something you love but if you're constantly worried about you know how are you going to make the rent or whatever, that you're not going to enjoy it. You can't even focus on the work. So folks you know going into an area where there's growth, there's also less competition. Like the pool is growing so much. There’s a lot of people falling into data science. But data science, but the field is growing much faster than how many people are coming in.
Ross: So there's a huge demand for data scientists.
Fernando: yeah just a humongous demand. The supply is increasing a lot because now there's a lot of courses. But demand is growing so much faster. So like it's, you're not just constantly worried about… it's a job seekers market. It’s really great and another thing is, because there's not a lot of people doing data Sciences compared to the demand you can go into any field you want. These skills are not just specific to like you know healthcare or media. You could use it for anything. Like I think that's another really really powerful thing. If you want to switch fields, if you want to just solve a specific problem these skills are super useful and people are constantly seeking them out.
Ross: And they're transferable. So regardless of where your interests lie, you can learn the skills and apply them to whatever you're passionate about.
Fernando: Yeah you know some people say when I tell them about like you know you should learn coding, you should learn some statistics, you should learn machine learning data science in general. So you know I often get the answer you know me I'm not that type of person. Like that's not my interest or whatever. I would argue that data science skills are like they're very comparable or coding in general is very comparable to writing or reading. It’s not about whether you like it it's that it's a base knowledge for to use for the interest you really want to do. Like nobody just inherently likes to code. Maybe some people do like the problem solving. A lot of people, it's not like their passion by itself. But it's a tool to create the things that you want. Just like writing, just like reading. So just the idea. If you're not immediately, you know when you started reading you didn't immediately like it. But now it's just part of what you do.
Ross: Yeah it's like a base skillset today's age is to learn how to code. Whether it's data science or any other application of it. That’s just like learning to read and write in learning basic arithmetic. Learning these skills are becoming basic for our society.
Fernando: Yeah so if you don't immediately see it as something that it's like interests you, I think you should compare it to reading or writing.
Ross: Now that's a great message to share. Because for a lot of people it seems like its engineering and it's something that's over and beyond what's required. but you're right that it's that it can get you learning basic skills in tech can get you, get you anything regardless what your passions revolve around.
Fernando: Yeah and you can you can build stuff on your own. It’s just so powerful.
Ross: So taking a step back from what we've been talking about specifically. One of the things I like to share with the WehnerEd audience is to always be leveling up yourself and always making yourself better today than you were yesterday. We talked a little bit about this with micro habits and things we talked about earlier in the conversation. But what are you working on now and how are you making yourself better in 2018?
Fernando: It's a good question. So tiny habits is a big part. The other part that I think is really important is seeking areas where there's no competition. So data science is an example of it. The field is so new that if you put one unit of effort you're going to get five back just because of the demand right. If you go to a crowded field you're going to put one back, one unit of work back and you're going to get 0.5 back. So I went to data science part of it was because of this. So now I'm looking at what is the next area that I can leverage my skills that I have now and is also even has less competition than data science. Just constantly pushing the, constantly going away from competition. So you're able to like innovate better, not constantly worried about you know someone like taking your spot. So the field that I'm looking at specifically is blockchain. I'm looking at like how Blockchain can be used with data science. I'm also looking at a building like data products.
Ross: Like what do you mean?
Fernando: Do you know product hunt?
Ross: No I'm not familiar.
Fernando: So product hunt is uh… So there's two levels of startups. Like one is like a number. You want to be like domination. But the other one is you want to be the local store for your specific niche. Like these smaller type companies that still like sustain you. But you're not trying to be like this multi-billion dollar company or corporation.
Ross: You're trying to be a local store as opposed to a Walmart. It has everything. But you have the right solution for the right audience.
Fernando: Exactly. It’s not like you're so small that you can't make ends meet or it's a side job it's like your main thing. So your product hunt is basically a duration of this. It’s a community for people doing this and to create these products you have to have an idea of marketing, an idea of testing products, an idea of basic coding. Enough to build these products. Its I think they're the most flexible skills. If you know these skills then you could, if you have an idea to create a product in a week; test it out and then see how it goes. Then use you know create like passive income there and then just keep building these products and that's what you see people doing in product hunts.
Ross: And these are skills that you're learning online as well? These could be other udemy courses that you're taking to build the marketing or to build to expand what you have to offer, to kind of create even more opportunity for yourself.
Fernando: Exactly. So I read it blogs a lot and I see what people are doing. There’s a lot of really really interesting people out there that are documenting how they're doing this. Like one person is Peter Levels. He just won a maker of the year in product hunt and he documents everything he does. You could see how he's testing products. The marketing, the testing products is supremely important. I think including that with data science skills, including this with this you become even more and more away from the competition. Because you're directing where the projects are going to go and then you're seeing how people are going to receive it and you're creating sustainable products from there. I don't think a lot of people have that combination. So if you are able to do this even further away from competition and you create a better position for yourself. So for me I'm learning by doing and by reading, testing products and I'm also learning you know reading about block chain just to see, it's very early days. So that means there's less competition. So I'm learning, trying to combine. Just learning basically and trying different things out. I am failing a lot. But just testing things out.
Ross: It's funny how at the beginning we're talking about micro habits and how you were start out with one line of code a day, then 30 minutes a day, an hour a day and then it kept building from there so that you could create an opportunity for yourself in a new space in data science from where you were before. And then now that you've done all of that work and you've become a full time data scientist, you have the title that you want. You’re working on projects that you're thrilled with. Now you're applying the same habit to new projects. You’re finding what's next. You’re clinging on to something that's going to continue to add more value by being a lifelong learner. I think it's neat to see how you've already been successful doing it once and getting yourself into a position that you're much more happy with and satisfied and comfortable and like we're talking with financially, just having that steady income and now you're adding even more value to yourself. So I think that's a great place to kind of cap it off here and, Fernando, it's been great talking with you. You’ve shared so many things. I was taking notes frantically, more questions I want to ask you. We just frankly don't have more time for. But I think this is a great start and plenty in the show notes that we can share with the audience to get them started on a similar path and help them build habits of being lifelong learners as well. So thanks for being on the podcast with us today and it's certainly been a pleasure, thank you!
Fernando: Ross thank you so much. It was really fun.
Ross: This wraps up our episode for today and of course links to each of the items we mentioned will be included in the show notes on wehnerEd.com/podcast.
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