S4E9: How to Become a Data Scientist Without a Degree With Fernando Hidalgo

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Fernando Hidalgo is a self-taught data scientist who went from being a teacher’s assistant to a data scientist in just one-and-a-half years.

After studying economics in school, Fernando held a variety of jobs, unsure of what he wanted to do. One day, he started researching data science, and it captured his interest. Using trial and error, a bunch of online platforms, and a data science bootcamp, Fernando completely transformed his career path.

Now, Fernando works as a data scientist at Discovery Communications, and in his spare time helps others hack their careers at fernandodata.com.

In the episode, we talk about how Fernando started from zero to teach himself data science, what resources he used, what technologies aspiring data scientists should learn, and his tips for marketing yourself to employers or clients.

Disclosure: I’m a proud affiliate for some of the courses linked below. If you buy a course through my links on this page, I may get a small commission for referring you. Thanks!

This episode was transcribed with the help of an AI transcription tool. Please forgive any typos.

Laurence Bradford 0:06
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Laurence Bradford 1:13
In today's episode I talk with Fernando Hidalgo, a self taught data scientist. We talked about how he taught himself using online courses, his experience with the medical data science boot camp, and the importance of marketing yourself if you want a new career and a new industry. Fernando Hidalgo is a self taught data scientist, he went from being a teacher's assistant to a data scientist in just one and a half years using trial and error, a bunch of online platforms and a data science boot camp. He now helps others hack their careers at fernandodata.com.

Laurence Bradford 1:51
Hey, Fernando, thank you so much for joining me today.

Fernando Hidalgo 1:53
Thanks so much for having me, Laurence.

Laurence Bradford 1:55
Yeah. I'm really excited to chat with you about how you got into data science and how you are a data science. Today, so let's go back in time, what made you decide to get into coding and data science in the first place?

Fernando Hidalgo 2:07
Um, well, I in school, I studied economics. And I was always interested in having as much employee having scalable impact. But when after I graduated, graduated college, I didn't really find any positions that I wanted. And I feel like the marketplace was a little tough. So that I will, you know, started, you know, like, I tried a bunch of different jobs. I had a I was also like, a, like, you mentioned before, a teacher's assistant for a while. But at night, I was always researched like economics, like what's the future of economics? What are people doing and economics at the forefront? And I thought, I found this company online called premise data there in San Francisco. And they basically using data science are able to calculate economic indices independently of countries, and they're able to help areas that maybe nobody knew there he could like, there are other economies are so they can have like outside investment. And I that like blew my mind. So right then in that I went like online and just started researching data science I found out how, what a great market it is you can work in any, you know, any field you want. And so then right then then I just I was like, Alright, I'm gonna start doing data science and I started doing a little bit every day. And that's how I started.

Laurence Bradford 3:23
Awesome. So you got interested through this other like company that you saw online that was doing something really cool. You began doing research, you learned about data science, and then you just start to teach yourself. That's super awesome. What year was this about? This was two and a half years ago. Okay, cool. Two and a half years ago. So you were not working, you know, in tech at the time?

Fernando Hidalgo 3:46
Not at all. Yeah. I knew nothing like nothing.

Laurence Bradford 3:48
That's so I that's kind of how I mean that's how I was when I first got started. I like had no idea about anything related to technology and tech careers options. It's just so fascinating to me. How you stumbled upon data science like right away because at least in my experience, when I began doing online research, I remember seeing a bunch of stuff about like software engineering and web development and not so much about data science. So like, what were some of the early resources that you came across that got you to start learning data science?

Fernando Hidalgo 4:20
So I think one of the most famous courses in data science is Andrew ings, machine learning course, and Coursera. And I also looked at like Code Academy, like these are very if you just click like, learn coding on Google, like or learn data science, like these are the things that are going to come up. So that's I basically just saw the first ones that were coming up. I also tried. in data science, people decide whether they're going to like learn our coding language or Python. So I actually took an art coding course with data camp two. So I was just like basically just trying different things. But I don't know, I feel like a lot of the early work like just trial and error didn't really like what I know now what I use now. And what I remember is not, it's like maybe less than a percentage of what I learned then.

Laurence Bradford 5:11
So like, what? Okay, that's really interesting. So but you, you just said basically what you are using now, like less than 1% like only 1% was something that you learned back when you first started out. Why is that?

Fernando Hidalgo 5:24
I feel like a lot of this is this is least the way I've learned and like just through trial and error and stuff. And that's why I really like to help other people because you can like save a lot of time, if you know what you're doing, and how to approach learning coding and data science is that you have to actually do it. Like, when, when I took all these courses, I was just following. I'm like, I'm gonna trust that if I do every single step. Then I'm at the end, I'm just gonna, I don't know begin making my own projects or my own like machine learning apps. But what happened was that I took all these courses. And like, by the end, I was just like following or like I didn't, it didn't have any context. So when I was actually faced with a real problem, I didn't know what to do it, I didn't have any like guidelines. I feel like in and this is what I've heard from other people, and especially in data science, the majority of people right now that are in high positions, or make the courses, they came from, like a theoretical background, they have like PhDs master's and the way they learned it was through textbooks, you know, very theory based, but the way you actually do it is just like trial and error you like hack it through, and you are thinking more about like business problems. And yeah, just trial and error. So what when I actually started learning was when I started doing projects, I went to Udacity. And if I wouldn't recommend this, everybody with I took their course took all their material and left. I just started doing the projects myself. And that actually helped me a lot more because it forced me. Like, I have a mission like I'm gonna build this app or I'm gonna, you know, analyze this data. And then all the theory just fit together naturally. Like I remembered everything because it's like towards a goal, rather than just like learning regression or learning decision trees abstractly, and you'd remember anything.

Laurence Bradford 7:29
Yeah, I think what you said is so important about actually doing it and actually taking action and executing and not just following along a tutorial or course blindly. Back in season three I had on Alexander Callaway, He is the creator of the hundred days of code movement. This doesn't really relate to data science so much, but he's a huge advocate and just getting people to code on their own every day and like to break away from that course and online tutorial. So I think what you're saying has just a lot relate to what he had to say. And I definitely believe that as well because I know when I was first starting out, and I was taking a bunch of courses and going through books and tutorials just following along, you know, blindly and not actually like putting like, not like critically thinking about the problems and stuff, you don't really learn as much. So I think that's great advice. So in for data science, though it at least in my perspective, maybe nowadays, it's a bit different. But there's not as many courses I think, as there are just like basic coding ones, right?

Fernando Hidalgo 8:34
I don't know too much about like the coding ones. That's why but I can't even think you know, which one I think you so I checked out your top 10 courses. I think it was like 1010 tips for learning how to code. Yes. And in Python, I checked out obviously Python because that's what I know. And you like, the best online course that I've ever had is by You saw I tried a bunch like, like so many and so many failed, but this one's like the best and you. It's by Jose cortesia and Udemy. And you offer his other Python course in your recommendations i thought was awesome. He is is very like project based. If you go to Udemy, there's so many courses, so many courses, but a lot of them are just theory or just like you just get lost in it. But, you know, I guess I would going back to your question, I guess I can't really compare because I don't know that many basic coding courses. I just know a bunch of data science ones.

Laurence Bradford 9:33
Yeah, yes. So okay. So on that note, then if there's someone listening and they don't have a technical background, they think data science could be for them or there's a field they want to explore more. What are some resources? It doesn't even have to be courses, it could be books, it could be newsletters, whatever. What are some that you think are really great today, especially after you've, you know, done so many and you really went through all this trial and error.

Fernando Hidalgo 9:56
So I think the first place to start Like you don't know Python, you don't know, like you don't know, pet, you know how to code, I would recommend or, you know, you don't know anything. Basically, I would recommend Jose portages, Python for machine learning bootcamp. And, you know, the first, I don't know, one fifth of the one fifth of it is the bare minimum Python to do data science. And I don't think it's necessary Learn More like when you get more advanced, like, then you learn more, but he teaches you the bare amount. And then after that he teaches you gives you like business problems first, and then gives you the theory to say like, Alright, this is why this algorithm fits this specific problem. And it just sticks and he also like, has three different ways of learning the same thing. So he'll have it in audio, he'll have it his ipython notebooks, they're all his code there. He'll have like a lot of like questions and answer like things. I think it's it's the best course I think to start with. There's also a really good book called Python, Python for machine learning. by Sebastian. I can't say his last name, but he's on Twitter. And he his book is pretty famous has come in the new edition is coming out now. It's it's a really popular book for actually learning Python and machine learning by doing. So I did all the projects in that book. I think those two, those two to begin with are really strong. It's a really good place to start.

Laurence Bradford 11:31
Okay, awesome. So that was Jose's Python for machine learning course that's on Udemy. And then another Python for machine learning book that you mentioned. And we'll definitely include these in the show notes for the listeners. So links that they can easily find these two resources. Okay, awesome. So that's really helpful to know, especially after you've surveyed so many different things that you recommend those for someone just starting out. But of course, there's a lot more to getting into data science and just you know, reading a book and taking a course and so on and so forth. So what did you do next? So like, in your journey, you began learning, you began taking these online courses, you began familiarizing yourself, what did you kind? What did you do next then to kind of get, you know, a career in data science.

Fernando Hidalgo 12:17
So, obviously, I was just starting to get frustrated because I was making progress. But now that I was like, when am I gonna actually get a job in it? So, you know, I, you know, looked up courses like in like person to person and I found the one in GA. So, there, you know, I, basically a lot of the course I already knew, because I already been studying it, but they put a much more focus on projects again, so I was doing projects, but there we would do it in a higher frequency. So that really helped me even get more invested in projects. And when I took the after that I took the data science boot camp, and there it's all projects, and, you know, maybe not to get too ahead But I think like the last part, once you do all these projects and you learn is like how to market yourself and how to show off the work you're doing to, you know, to get a job. And I think that's where the data science bootcamp really helped. I, you know, I still believe that someone could do it without all this stuff, but it's, it almost like compresses everything and makes it faster. So I was like, Alright, either I can trial and error this and spend a little more time or just like try to speed it up. So I, that's when I took the course and then ultimately, the data science bootcamp.

Laurence Bradford 13:34
So you did this part time course at General Assembly, which was like a data science program and then you did a more full time data science boot camp. How long was the full time data science boot camp for?

Fernando Hidalgo 13:47
It's three months?

Laurence Bradford 13:48
Alright, three months. Did you move to take that or like, did you did it there happened to be one in the area you are loving?

Fernando Hidalgo 13:56
So yeah, I live in New York. So It was I had to quit my job, but it wasn't. I didn't have to move.

Laurence Bradford 14:06
Alright, awesome. So then do you think those like in person boot camps the part time and you know, the full time? Did they really help get you to that next level then?

Fernando Hidalgo 14:15
So I think, I think in terms of projects, yes, but it I think it's very in terms of knowledge. I think it's almost impossible to learn data science in three months. It's very, like they give you the start enough, so that you, you could do a lot of work for companies, right? Like when you graduate from boot camp, you don't know anything. So you don't know anything at the beginning and then you finish the boot camp. You're not going to start doing you know, computer vision or very high level things like you're going to start maybe as a junior data scientist where you're doing more data analysis and building small models. But I think what it did give me And I think this is like the real virtue of the boot camps is this almost like a busy like a helping you find a job I think like almost like a marketing yourself like how to approach these things how to frame yourself how to frame your history like your past. So that it it like companies actually say okay this person knows enough you know we know that like the the people that are in the boot camp know that but in order to sell yourself correctly to the companies.

Laurence Bradford 15:34
Yeah, I think that you know whether you're coming from a coding bootcamp or not and whether you're trying to pursue data science or another tech career, I feel like marketing yourself is such an important aspect of landing a new job in a new industry. So what were some of the things that you did to market yourself?

Fernando Hidalgo 15:53
Oh, so I, I feel like marketing yourself and actually doing the technical work. like to have very little overlap. So it's like learning a totally different skill I you know, I could even name courses so I read a bunch of books on this and and then I also took some courses on like, you know, even like learning how to get a job in marketing yourself. But the main takeaways main takeaways is that and that I use and I thought that were most helpful is you know, once you have these projects be really good at showing them off. So document all the work you do before I would just do projects just keep in my computer and then I don't know why I thought this but I was like, eventually someone would No, no, no one's gonna know. Like, you know, now I recommend people and this is what I started doing is every single thing I did, I would finish it all the way to the end and then put it on my website and tell you like, you know, this is an Explain the whole thing be like, okay, imagine we're trying to optimize with something I did afterwards.

Fernando Hidalgo 17:06
So I built an app. I was like, I know one of my friends, she works in real estate. And she was manually looking at houses and then trying to see the houses around it, how much she would price the houses. So I was like, you could just use machine learning for that. So I, you know, scraped a bunch of data online, use a regression model and predicted how much a house would cost in a certain area, depending on the rooms, the square feet, whatever. And I put that on my website, I made it so that my employers can go in there, check out the app and see how the machine learning model works, what features I use. So I think, you know, building projects, showing them off is really, really important. And the other part is that I use is I cold emailed a lot. So once I had built this portfolio, and like almost like crafted my story, then I would called email, like 10 people and get two responses a week back like 20% conversion rate. And I would just keep iterating these emails. And you know, you'd be surprised who's going to email you back, you know, maybe, like the majority are going to be no one's going to respond to you, but someone from a high position would be like, Okay, let me just, you know, chat with you. Maybe in in startups, like the CEO, or like someone really high will be willing to meet coffee with you. And that's what happened to me. So I think, you know, doing the projects, showing them off and then, you know, really presenting them well and then showing them off to people that see value in them. I think those three things are really important.

Laurence Bradford 18:42
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Laurence Bradford 20:59
Yes I love so much of what you're saying. And I love what you said about showing off your projects because you're 100%, right like you. So you could build something super awesome and really helpful to people. But if you never put it online, or you never make an effort to show anyone, no one's ever going to know about it are going to find it. So I love that advice. I also love the advice about a portfolio and a strong personal website. I talk about that all the time, especially for people going into tech and how crucial that is to just yet marketing yourself and branding yourself as someone in the tech space. So I have a few questions based on some of the things you were just talking about. First, was the only place you would show your projects on your website, or were there was there anywhere else that you put it maybe like on GitHub or something?

Unknown Speaker 21:43
Yeah, GitHub. So GitHub, and so before I had a website, I use GitHub, but then I would have it everything on my website and then a link for the code to GitHub. Alright, super smart, like a link to the repository or something on Jakob.

Laurence Bradford 21:56
Okay, that'd be on your website. So then the project employers would see your projects and information about it, and they could click over to see the GitHub repository.

Fernando Hidalgo 22:06
Yeah. And in the website, I would talk to like business cases. That's another thing. Like, I feel like in data science, people just get like bogged down with like, the technical things, like the like the person hiring you is going to care like about these little things. I think the majority of you know, there's some people that are going to be like, they have like very technical places. But a lot of companies like the person just wants a business case all. And so in my website, I talk about business cases and how I solve them. And like, you know, if someone is knows how to code, then they click on my GitHub and then seeing all my code.

Laurence Bradford 22:37
Yeah, perfect. I think that's great advice. And that could go to like anyone trying to get a job in tech matches a data scientist, someone who wants to do web development or anything that you could put on GitHub that is code related. So another question I had when you're speaking, was about cold emailing. I think there's so much value in being able to send a Strong cold email and knowing how to like get a response. And you could be sending a cold email for a variety of reasons and your cases sounded like to people or I mean to companies that you would want to work at. Because you talk a bit about that, like, how you went about the cold email. You said, I think you said you had about 20% conversion rate. What do you mean that that's the rate that you got response to that email? Yeah, I would love to hear more about that.

Unknown Speaker 23:23
Yeah. So. So 20% is the response. So positive response. So like, it could either be, you know, right now enough, like, we could schedule this for in the future, or, hey, let's do it this week. Or maybe we're not hiring, but I'll keep you, you know, like, follow up later. Like, that's, for me is like a positive one because there's still a chance. But in terms of like, cold emails, I think you have like these warm connections next to you know, you know, you know, maybe acquaintances, but I think to really maximize I think that's why cold emails are so important and there's an art to writing them. If you're writing to, so if you're writing to somebody in the same position as you, like, if you're writing to another data scientist, then you can talk about, you know, your background, your projects, like more friendly. And the email can be a little longer, I think. But when you're talking to like a hiring manager, and this is what I found most successful, and I've also done some reading on this is they should be short, so they should be short, and they should be concentrated on a problem they have.

Unknown Speaker 24:29
So for example, right now, I work for discovery, like Discovery Channel, discovery, communications, and we're hiring somebody. If someone was to send me an email saying, like, very short Fe, like, and this is the type of emails I wrote. So they would say, you know, Hi, my name is x. And, you know, I really like discovery because of why I see that, for example, we just acquired scripts were in the process of acquiring another company. Okay, so, oh, I noticed that you're acquiring you know, scripts. You know, a lot of when companies acquire another company, this is a problem that's very common. You know, and I've solved this problem before. And here's the link to my site. And you could see the problem I've solved. You know, we'd love to grab coffee, whatever, thanks. Right? You know, that's, it's like very short. It just addresses the person's pain point. And then you're gone. It's not long. It's not like just talking about your life, like, it's just solved. It's about them. It's about you're empathetic to their problem. So, and I think a lot of mistakes. One of the mistakes people make is they'll send like a friendly email, like a long life story email to somebody that's a hiring manager that has like zero time to read your email. And so that's, that's what I found to be most useful.

Laurence Bradford 25:44
Yes, I think there's so much. We can even unpack just with what you took the response you just gave. But one of the big takeaways I'm hearing is just knowing your audience, so who you're writing email to, yeah, if it's a fellow data scientist, or if it's a hiring manager, who may be bombarded with emails and just to keep the email short, not give your life story and just make it super clear on how you can solve a specific problem for them. Exactly. So I'm curious, as you mentioned, today, you work for Discovery Communications. Did you get that position through a cold email?

Fernando Hidalgo 26:16
No. So I, I got that through a recruiter.

Laurence Bradford 26:20
Okay, cool. Did you get any of your earlier positions, though, from a cold email?

Unknown Speaker 26:25
So I got a couple meetings. But well, that was happening a lot of the so Okay, the first position after Metis I was working with with us at like a very small startup. And that was like, you know, they had a talk and I went up to them and asked them to, if I could help with them. So I don't think that's a cold email, but it was just like a cold introduction, and we ended up working together. But the rest was just that after that it was discovery, but it wasn't through a cold email.

Laurence Bradford 26:55
Okay, but it was still through a recruiter and did they find you on LinkedIn or through your website or something?

Fernando Hidalgo 27:00
So it was through LinkedIn.

Laurence Bradford 27:01
Yes. And any of my longtime listeners will know I love LinkedIn. I always talk about LinkedIn as such an important tool in getting a job in tech. And I think like for people first starting out, it's like, really, really crucial. But then even when you're further along, like how you were when you got this job at Discovery, right, you already were a data scientist somewhere else. But then these recruiters are using it as like a discovery. Well, sorry to use the word discovery twice, but they're using it as a discovery platform, right for finding candidates. And then you actually were approached about it saves you time from having to apply to new position. So it's like a win.

Fernando Hidalgo 27:35
No, absolutely.

Laurence Bradford 27:37
Yeah. Awesome. So how long have you been at Discovery Communications now?

Fernando Hidalgo 27:41
Eight months? So still, I don't know. I feel like that's fairly new.

Laurence Bradford 27:44
Yeah, I mean, I think eight months is I don't know I guess I work at a startup. So to me eight months is a pretty long time. I guess at my company, a ton can happen in eight months. It feels like every week is almost like a month. Yeah. So so that's really exciting now and then what kind of work are you doing there?

Fernando Hidalgo 28:00
So I worked on, you know, everybody knows, discovery for like, you know, TV network, but we're making a big push for digital. So now we're trying to, you know, we have, like apps we have, we're on connect devices. And so I'm a data scientist on the digital side. So I handle a lot of, like, hit level data, and then trying to find, you know, there's so much analysis like, where people once they enter the app, where people stopping, how can we cluster together different shows, so that we can, you know, recommend it to people? How can we understand our own shows and understand our own audience by clustering these these populations together? But I think a lot of just focusing on understanding our audience, and where is the next point of growth for our digital product because it's almost it's, I think it's like about a year and a half old, not not really like it's very, it's Very young product.

Laurence Bradford 29:00
I got it. So at work, how many other people are you working with, like on the data team?

Fernando Hidalgo 29:07
So our data team is still very small since we're starting. Since the product is so, so new right now, we have a team of three, and we're hiring somebody else. And then we may hire other people in the future.

Laurence Bradford 29:21
Got it. Thank you for sharing that. Yeah. So well, so even though you are working this bigger company, it's this newer product. So the data team is still smaller. But that must be really exciting in a lot of ways. You must be getting a lot of autonomy, I would imagine.

Fernando Hidalgo 29:36
Yes. A lot. It's like a balance because sometimes, um, you know, maybe like the higher ups want something like a metric done and I have to, you know, come up with a very quickly, but at the same time, if there's, if we have an idea, and we're like, Hey, we think this is a good idea to like to check out or, hey, like, this is something interesting. The upper management, there's no there's no, there's not a lot of structure, like somebody like from that has a very low position can offer something and then they get implemented. So I think that's, that's what I like about it.

Laurence Bradford 30:13
Yeah. And one thing you mentioned really early on in our conversation was how, like most of what you learned early on was in is nothing that you're using today. So I'm curious what skills and technologies are you using today at your position at Discovery Communications.

Fernando Hidalgo 30:30
So I actually am using some of the things, but not like I had to, like relearn them. Because I've forgotten so much. So it's, I'm using a lot of Python and SQL. I'm using like pandas, like that's from Python, using some SK learn. I think that those packages, but mainly in terms of languages, Python and SQL, that's what that's what I use day to day.

Laurence Bradford 30:57
Got it. Sorry, what was the one thing you mentioned? K Learn How do you spell that?

Unknown Speaker 31:01
Uh, it's it's really s. It's really Scikitlearn. So s-c-i, so sci and then kit and then learn it learn. Yeah.

Laurence Bradford 31:09
All right. So machine learning. Okay. Yeah, I was gonna say I've heard of that before, but I have a feeling a lot of listeners maybe haven't. Could you just explain what that is a little bit?

Fernando Hidalgo 31:17
Yeah. So it's just let me see like NumPy is for maybe speeding up calculations, like, doing like linear algebra. scikit Learn is for machine learning. So now if you want to run a model, if you want to build a regression model, psychic learn has all the code already written up so that you can just train this model to the data that you have.

Laurence Bradford 31:42
Okay, so it's a package for machine learning. Yes. Got it. And then do you use Python with that? Is that like a Python package? Yeah, it's a Python package. Okay, and it's for machine learning. So you can run decided to say run models on sets of data?

Fernando Hidalgo 32:00
Yeah, so like, if if this didn't exist, I would have to like code up, like the math behind the models. But somebody just like wrote up, you know, group of people just wrote up all these all the math already. So that if I have, like, if I have the data already to train, it's already written up. It's like all automated.

Laurence Bradford 32:22
Got it, got it. And for the listeners, we'll definitely link to these different things in the show notes so you can learn more about them and all that good stuff. So Fernando, thank you so much for coming on the show you, like gave us so much information. I really loved what you had to say about the marketing aspect and about how you learned earlier on and everything that you're doing today. And I just want to add that I am so impressed with you because I feel like a lot of data scientists that I know at least personally have had, you know, advanced training and when I say today's training, I mean like PhDs or something related I think it's so awesome that you were able to transition careers just from going to like these boot camps. And now you're a data scientist at a big company. So kudos to you.

Fernando Hidalgo 33:09
Thank you, thank you, there's gonna be a lot more of me, trust me, your listeners, they're all gonna be dead. It's like, it's a new wave of people.

Laurence Bradford 33:17
Yeah. And I always tell folks who email me and say, Oh, I'm really interested in data science or something that's kind of related to data. Should I go for it? I always tell them Yes. Because, at least in my experience, it is such an in demand career and an in demand field. Like I'll tell you at the company that I work for, we have a much harder time filling data roles than software engineering roles. And I think that just goes to show like how lucrative a career in data science is and how in demand these positions are. And there's like, honestly, not enough people to fill the roles.

Fernando Hidalgo 33:49
Yeah, I agree. 100%. The same thing is happening in our company. There's very few people that actually in terms of data, like there's a team of us of three, but there's a bunch of data people But they use, they use like Google Analytics. I think a lot of people just afraid to code. So I think that's why it's important to have, you know, like these, this podcast and your website, to really like take the fear out of coding, because I think it could offer a lot of people like more control over their lives.

Laurence Bradford 34:17
Yeah. Awesome. Well, thank you for saying that. That means a lot. And finally, Fernando, where can people find you online?

Fernando Hidalgo 34:23
So I have a website, it's fernandodata.com. You could you can find my email there. You can set up some time with me there. I really just I really like to help people and making it easier to transition. You know, whatever career they're in into data science. And I'm actually even more interested in people that have no technical background because that's where I came from. And I think the process can be smoother than if you just trial and error. So you can find me at fernandodata.com.

Laurence Bradford 34:52
Great. Thank you again for coming on.

Fernando Hidalgo 34:54
Thank you so much, Laurence.

Laurence Bradford 35:01
Thanks for listening. If you want to recap of this episode, you can find the show notes at learntocodewith.me/podcast. From there, you can browse through recent episodes or find old favorites using the search icon in the upper right corner. If you enjoyed this episode, you can subscribe to my show on whichever podcast player you use. For more free tech related resources, tips and recommendations, visit my website and blog at learntocodewith.me. Tune in again next week for a new episode of the Learn to Code With Me podcast. See you then.

Key takeaways:

  • When you’re learning technical skills, don’t only follow along blindly, completing tutorials and courses. Put what you’ve learned into practice yourself, a little bit every day.
  • It’s important to know how to market yourself. You could build something awesome, but unless you show someone, nobody will ever see it.
  • When you’re building projects, document everything you do for others to see. When you’re done, show them off! A personal website and GitHub are good places to do this.
  • Cold emails can be successful, but you have to know your audience. There’s an art to writing cold emails, depending on who you write to.
  • Data science is a highly lucrative career and there are not enough people to fill the available roles, so if you want to be a data scientist, go for it!
  • You don’t need to have high-level formal education to get into data science–like Fernando, you can do it through teaching yourself.

Links and mentions from the episode:

Thanks for listening!

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