SDS PODCAST EPISODE 103 WITH EMANUELE CARBONE Show Notes: http://www.superdatascience.com/103 1
This is episode number 103 with data science freelance consultant, Emanuele Carbone. [Background music plays] Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur, and each week we bring inspiring people and ideas to help you build your successful career in data science. Thanks for being here today, and now let s make the complex simple. [Background music plays] Welcome back to the SuperDataScience podcast. Today I ve got a very inspiring episode for you. I ve got Emanuele Carbone on the show, Emanuele is a student who Hadelin and I met during our road trip through Europe this summer. And why is this episode so inspiring? Well, this episode is so inspiring because Emanuele has only been in data science for just over a year and he is already running his own freelancer business where he is helping out massive companies with data science. He s working with one of the top football analytics companies in Europe, he is helping a restaurant, he is doing a forensic project on the side as well in data science. Why I love this episode so much is because of the simplicity of Emanuele s approach. Show Notes: http://www.superdatascience.com/103 2
When Emanuele started out, he just went and learned something that he was very excited about, he learned Tableau. The two skills that he brings to the table when dealing with clients are Tableau and Excel. That s it. Just simply with those two tools, even without any advanced complex machine learning or deep learning or artificial intelligence, without any R or Python, he doesn t need any of that. He just takes Tableau and Excel and he brings them, and he shows how he can add massive value to clients and they hire him, and they love him, and he loves his job, he is his own boss, he does what he wants, he works when he wants. And all of this, he created all this for himself just within one year of getting into data science. This podcast really shows, and Emanuele s example really shows that you can, if you really want to, you can find a niche for yourself and you can bring value to the world, even without spending years and years and years of learning data science. I personally liked this podcast a lot and at the end, we actually discuss with Emanuele why that is the case right now, and why right now, this day and age that we re living in, why this is the golden era of data science. This is not going to last for much, much longer, this is the time and the opportunity to get into this space. So, if you want to get inspired, you want to get excited, then this podcast is for you. Without further ado, I bring to you Emanuele Carbone, freelance data science consultant. [Background music plays] Show Notes: http://www.superdatascience.com/103 3
Welcome back everybody to the SuperDataScience podcast. Today I ve got an exciting guest, my friend from Italy, Emanuele Carbone. Emanuele, how are you going today? Hi. Very well, thank you. I m really excited to be here, it s such a great opportunity to be able to speak with you again. I m so excited as well because, guys listening to the podcast you might know that Hadelin and I went on a road trip this summer in Europe and we had like catch-ups all over the place with students in different cities, and Emanuele was on our very first catch-up which was in Florence and that s how we met. Had a great chat and I really thought your story was very interesting, how you developed your career and what you re doing now, so I thought it would be really cool to share some of it with the world. So, are you excited about today? Yes, because I believe that having the opportunity to share one experience especially about this field of work that is not very common could be an added value to other online learners because many times online learning is not seen as something that can be used in a professional way in a working place, and maybe today I can challenge that preconception. Awesome. That s fantastic. Let s get started I guess by your background. Like how long have you been in analytics and what did you study, and how did you get into this field? Show Notes: http://www.superdatascience.com/103 4
I studied Economics but before that I wanted to be, for like 10 years, an engineer. I had a change of heart because I asked myself what are my talents, how can I use them at my best? Engineering was a good fit but not the best fit. I wanted to do something always practical and maybe related with mathematics, information, and I believed that economics was the right way to go. Data science was something that I did not expect to exist, so I liked doing graphs, my statistics classes I liked them very much but I wasn t seeing a job opportunity there. After some browsing through Udemy, I saw the Tableau course and even before watching the preview, I started going to the Tableau site, I didn t want to be influenced by some Udemy review. Then after I saw what I could do with this software, ideas started running through my mind and I said, okay, I want to do that. I want that people buy that from me and so my career started shaping itself because all of my decisions started going towards the end-goal of becoming a data scientist, a business analyst that can use data to make decisions. I see data science as a tool to do my work. Gotcha. How long ago was that? Was it like 10 years ago, how long? [laughs] No, not so long. It was just one year ago. Show Notes: http://www.superdatascience.com/103 5
One year ago? Yes. Almost exactly one year ago, my start up that I founded about two years ago after going well for some months, with some disagreement with my colleagues, started to collapse as a society. I wanted to change radically what I was doing and data science was something that was affiliated with my previous occupation because I was doing marketing research, website development, business planning, and it was like the natural complementary field to go. And so it was pretty random, I believe. I was lucky. [laughter] Yeah. What I want to point out to everybody is that why I asked this question. Because actually I know Emanuele s story quite well, at that catch-up we had a long conversation, he told me all about himself. Why I asked that question is because it s very interesting that it s only been one year, and so now I m going to ask Emanuele to tell us what he s accomplished in that one year. It is mind blowing. Like, Emanuele has gone from actually doing this work that he s always been dreaming about, to now he s working as a freelancer. Right, Emanuele? He s working on his own time, he s providing consulting services, he s living a very interesting and fulfilling life. Tell us a bit more. In this one year, you just started in data science a year ago, what did you get to know? Show Notes: http://www.superdatascience.com/103 6
After my start-up failed, let s say it pretty bluntly, I had almost 10,000 euros of debt. In just four months of hard work, I was able to move out from my parents home, I am 23 for the audience, so in Italy it is very, very difficult. Being able to move out, getting a home of my own, I do the job when I want to do it and I continue to receive job proposals. Because data science skills are so rare in the job market that as soon as you are able to do one or two things well, people start looking for you. Now I m working as I said before as a business consultant and business analyst and I mainly work with two companies, and then I do some other freelancing work. One of them is Wyscout that is the leading company for football and data analysis solutions, so again data related work. The other one is a restaurant consulting company that creates new restaurants. So, very different fields. Now I m doing different business, it s about digital forensics, so data science can be applied to really many fields because you can use your skill, again, for every kind of business. Once you get the right approach, it s easy to switch from one to another because in the end, the process is the same, it stays the same. This career is very fulfilling because it s not really about the economical compensation. It s about being heard. People value your advice. People know that when you speak it s not because you feel to speak, it s because you have done some serious calculation about your speaking, about your advice. You re really getting insights from the information before Show Notes: http://www.superdatascience.com/103 7
using them. I work mainly with people that are 10 years, 15 years, older than me, and they treat me as their equal. I work with the CFO, with the CEO, with the General Manager, I don t work with someone that is below their level. I always work with the executive level and I believe that without data science I wouldn t be able to do that because I don t have the gravity to speak. Yeah. It s very, very impressive. This is all just after one Tableau course, in one year, and at the age of 23 years old. This is a very good testament to the fact that it is possible. There is a lot of listeners out there who are just starting to get into the field, who are thinking about how to try it out, get their toes into the water and so on, how long it will take. It feels like from all these job descriptions that companies require four or six years of experience and so on, it feels like it will take forever to get traction, to get momentum, to get a job, to become financially stable and be able to continue growing in this space. And here is an example Emanuele, just one year and he s already doing this. So, Emanuele, for those people listening out there who are just thinking about getting into data science, that maybe come from a similar background to yours, or maybe are from a different background, what would you say to them? My only piece of advice is: don t start doing one thousand things, okay? Just start with simple ones and do them really, really, well. Become the best one to do simple things and then start growing from there. Don t be interested in acquiring Show Notes: http://www.superdatascience.com/103 8
many skills but specialize in a few skills that you can sell. In my case, what I wanted to do, I started with Tableau then I moved to Power BI, and then I started to create the skills to build databases and to analyse the data with Excel. What I want to do, I create my little environment of skills and applications and then I sell it. I know that in that particular field I m really, really, really good so I don t have many more competitors. My advice is don t start from now to go to machine learning, IE and other advanced programming. Start with the basics and master them. Build your blocks one at a time, and it will pay off because you will have a really strong foundation that you can build upon because maybe tomorrow Tableau, Power BI, Python, they will not be [around], any of them. But the approach, the concepts, the fundamentals of how the work must be done, those will remain with you forever. It s about the concepts, not about the tools. The tools will change. Fantastic. Fantastic advice. I can totally vouch for that. The concepts are much more important than the tools and that s the whole philosophy why data science is such a transferable set of skills, it s not just because you can apply, data is data everywhere, but also because this whole thinking, this whole critical thinking and the approach that you take to solving problems, once you know it in one tool, you can transfer it to another tool and another tool. As Emanuele says, you never know what the tools of tomorrow will look like. Concepts and methodologies, they don t change as rapidly, and they help you grow, that s what helps you grow further and learn new concepts and methodologies. Show Notes: http://www.superdatascience.com/103 9
Emanuele, I had another question for you. Once a person has found where they want to start and as you say start with simple things and get very good at them, once they ve done that in terms of their learning and training, and upskilling themselves, how do they sell? Like how do you go about selling? How do you go about [inaudible 00:15:47] clients and so on? [laughs] Okay. I sell one simple thing. I answer questions. So, when they ask me, what is your job? I always say it s halfway between an accountant and a fortune teller. [laughter] They task me, give me a bunch of questions. My job is answering questions and resolving problems. My customer doesn t really care how I do that. As I said before, data science is a tool. You don t need to sell the tool. You have to sell the final product. Do you want to know if you have a shot to sell your product in Albania? Okay, let me do the thinking. I will come with an answer. Does the answer satisfy you? Pay me. It doesn t? Do not pay me if you are not satisfied. The first approach is all about what do you need? I will provide it for you. Then after that, consultancy becomes like they become dependent from the consultancy because they always have someone that can be a benchmark for their Show Notes: http://www.superdatascience.com/103 10
business. As a business analyst, you can create an optimal economical situation. So, if there aren t many external variables that interfere with your business model, what would be your revenues output? So, I have to calculate that and then I confront it with the actual revenue stream. Thanks to that they are able to gain insights to their business. Maybe I have the cost too high, maybe I have the revenues too low, and when you talk with the customer and you are able to identify some critical point about their business, they will pay you because they pay you with the money that they can save from the costs, or they can have more revenues. I don t know if it s clear. Okay. [laughs] This is my personal trick. First question is free, you see that they like me, and then after that it starts growing. I believe that people that do data science can be maybe too technical. Don t focus on the technical side because people don t comprehend that. People only see the end product. Sell the end product. Okay. Yeah. That s a great way of doing it and thank you for sharing your approach to clients and building and growing your client base. What would your advice be to people who want to follow in your footsteps, want to also learn some basic skills and then go out there and freelance? What would your advice be for finding the very first client, when they don t know anybody, they don t have somebody to work for? Do you have any tips on that? Show Notes: http://www.superdatascience.com/103 11
First strategy I would suggest is to go to some client, random client, and do the first five work [tasks] for free. Completely free but the only thing that you get is to be able to create a white paper about that work so that you can build the first portfolio. After that, the sixth client [work] you will say, okay, this work value was 1,000/ 2,000 euros, okay? In this way, the future clients can see the quality of your work and you will be saying also its value. So you can say, okay, this cost the client 2,000 but he cut a cost of 10,000, okay? So if you give me 20% of what you are not spending, I believe that is a fair deal. Okay, so you reduce their costs by 10,000 and they will only pay you 20% of that which is 2,000, so it s fair. Yeah, I agree. It s not about the price but always about the value of the information. If customers don t see value in what you do, you will never be able to sell what you re doing. This is my suggestion. Yeah. Gotcha. I can totally also sign onto that, that is the most probably safest and smartest approach when the first couple of jobs you do for free. It s a lot of work and of course we all want to be rewarded for our work, but think of it as an investment. Investment into your future career, into getting started. I can give an example from my own personal life, and personal experience in this. Most people listening to this know that I Show Notes: http://www.superdatascience.com/103 12
teach courses online. When I first started, which was exactly three years ago from about today or maybe three years and one month ago, my very first course I published it for free. It was a course on programming for the foreign exchange market and I put in a lot of time, I was very nervous creating that course because it was my first one, it took me a lot of effort, re-recording, I spent weeks creating it. But I put it up there and I put it out there for free. Completely free and it was free for months, it was free for like two or three or maybe even four months. I just wanted people to sign up. But I think my second course was also free and I just want to show the value I can bring to the world. I knew that if I make a paid course, because I don t have any name, and a reputation, nobody is going to buy it. Or maybe, you know, five people are going to buy it and that return would have been great, it would have been like very encouraging but it s a strategic move to sacrifice your first return in order to build up that portfolio, that resume like you say. It s very important to talk to the client that you ll be able to showcase the results in the form of a paper, a white paper, a presentation to others, even if it s to sensitize, and get a testimonial from the client as well so that you can then showcase to future clients. It s a strategic move, it s important and pretty much like in any kind of way you re starting out especially if you re starting out on your own, if you re not getting any, like, seed funding, any capital investment and so on. When you re by yourself, you re just bootstrapping this process, it s a very wise way to get started. Not only that, but also because you- like say for me when I was creating this first course, I had no experience creating courses. So I knew Show Notes: http://www.superdatascience.com/103 13
that I was going to make mistakes, I knew it s not going to be perfect, it s not going to be anywhere near even great level of quality and value and so on. I knew that I d got a lot of learning to do and if I had charged money for that, then people would have been unhappy. But because people get it for free, they re more likely to be less unhappy and more helpful in giving me feedback on how to improve and so on. There s a couple of benefits to that, it s just sometimes you ve got to bite the bullet and get into it that way and then after that, after the first five jobs, you start getting momentum and then, as Emanuele says, you can start charging for those next jobs that you re going to have. Yeah. That s how to get started I believe, and everybody, I think, knows like a local mum and dad shop or some local store, somebody or a friend has a friend who has a pizza shop or something who you can go to for this very first job, and then they might have a friend who you can go to for the second job and so on. Yeah, that s very cool. The other thing I wanted to talk to you about Emanuele, was your progression of skills. You said you started with Tableau which I think is a fantastic place to start, even in my data science A-Z course, that s where I recommend starting because it s so easy to pick up. Then you moved on to Power BI which is very similar to Tableau, so it s a very quick transition there, then you built on your, like, Excel analysis tool box, which by the way, very impressive. The things that you were able to do like you mentioned a forensics company, a forensics project like a football company, a restaurant company, like quite a few things and just with this simple tool Show Notes: http://www.superdatascience.com/103 14
box, you are able to help so many different companies in quite a few projects. What s next for you? What have you got your eyes on next that you think is an important step in your learning process? I believe that I have to take it to the next level. Okay, now I m pretty confident in my basic skills and I want to move on, on more advanced skills, so maybe doing the same job but with different tools to understand, even to do some research projects. Because I want to create dynamically what I m doing now manually. I want the final step I believe will be being able to write algorithms for machine learning that are able to do what I m doing now each time for every job so my goal is that. To create, I don t know, Emanuele IE [laughter] that is able to do many tasks for me, or maybe different kinds of IE that can see different patterns in data. I have a lot of ideas and now I m just taking some months to experiment different fields, I m writing down what I want to do. I believe that is as important as doing it because without a pattern, I will not be able to create skills that are applicable to my job. Maybe I like very much Java [but] I cannot use it so why learn? I don t know if I m clear. The most useful way to learn some new skill is I use very often this trick. I sell a job, work that exceeds my actual skills so now I have to learn it. [laughter] Show Notes: http://www.superdatascience.com/103 15
You have no choice. Now I have to learn it and in this way, I always have something that keeps me pushing through my limits and I believe it s working because I see almost immediately the fruits of my labour. So, if I spend 20/30 hours learning some new skill and then 50 hours practising it and applying it to the work and then the customer is happy, in that way I have really emotional experience with the learning process and the job process. Gotcha. Very interesting, like pushing yourself outside your comfort zone and locking yourself in by committing to a project in that outside your comfort zone. That s a very bold move. A lot of people would first learn something then only do a project. But, yeah, as I said, this is very cool. It s kind of like very daring but it works, it s good. Another thing I wanted to ask you is, I may this may change for you going forward, like you ve got a huge career ahead of you and your preference might change. But right now, what is your target? Like, you re a freelancer, are you planning on staying a freelancer forever, are you planning on starting your own company, are you planning on maybe joining a company at some point? I know it might change, but what does it feel like right now? Okay, my final goal will be to become a professor. Show Notes: http://www.superdatascience.com/103 16
A professor? At a university? Yes. Yes. Master courses and so on, courses for professionals rather than university courses, and I would very much like to create a company, a consulting company. Because now I have more work than I can do. So naturally I will be searching for someone to help me. It s not that I want to do a company but I feel like I should do because it s a natural process. The first company didn t [go] well but now I know the errors that I made. So I believe that I should give another shot to create something, I believe it s about creating value and this will give me much more credit as a professor because many times you have professors that never really worked a day in their life, they only did theoretical work. This, I believe, will be really important for my future students and my colleagues to have a practical background and experience, how to apply the knowledge. As I said before, a lot of knowledge isn t necessarily better than less knowledge but put it really [to] work. More practical. Okay. And so, that s a very interesting goal. Why do you want to become a professor? Why do you want to teach people? Because I believe a consultancy is a lot like teaching. I really like giving advice. A lot of people come to me to receive advice even [for] matters that are not related to work because they trust my opinion, they trust how I think, and so I believe that Show Notes: http://www.superdatascience.com/103 17
transforming it [into] a profession like being a professor will be one of the best ways to put it to use. Professor is someone that has a really huge impact on the future life of their students. A really good professor can literally change the course of life of a student. I believe that that is creating value for someone, not just doing some analysis. Yes, that is okay but I would like to have a much more profound impact on the life of someone and teaching would also allow me to do a lot of research, maybe it s not applicable right away but I can think out of the box, I don t have the boundaries of a company, and it would be really fun. That s so cool. I think that s a fantastic future that you ve envisioned for yourself. You ll have a company of consultants working on the side, you re going to be a professor teaching and changing people s lives, it s all totally possible. How different is your life now compared to a year ago, before you started into data science? Like how do you feel about this transformation that you ve gone through? The biggest change is about how I feel about myself. When I was doing my previous job, I always felt, okay this is a good fit. Okay, I m good at doing it but I feel like I cannot become one of the best. I don t know if you can relate to that. As soon as I started moving to this field the dots started connecting and I said, okay, my talents are very much aligned to this job, to this profession. I can give 110% what I was Show Notes: http://www.superdatascience.com/103 18
doing. Before, I always felt I was limited because I was good but I wasn t really interested in it. In this field, I feel really confident about myself and this is what I believe most works for me when I relate to the clients, because clients feel that I m confident and they trust me and so it s a virtuous circle. Gotcha. Fantastic. That s a fantastic way to feel and to feel about your present and your future. It s definitely going to encourage you to keep that momentum going and get even more experience. I ve got a couple of questions for you that are kind of like about what s been going on for you in this past year of data science. First one is, what s the biggest challenge you faced as a data scientist? In this past year, what s the biggest challenge that you faced? It was a recent one. The CFO of one of the companies asked me a really difficult job, it was to do a treasury budget for them. A treasury budget is a forecasting of the cash flow for the next 12 months, to the short-term liquidity of a company. In practical terms, you have to forecast what a company will have on their bank account each month. So, as you can see, let s just think about how many variables there are in a company, even a small one. You have a lot of costs, a lot of different types of costs, different streams of revenues, so my job was to create a model based on the actual data to forecast this stream of revenues and what consequences will have on the long term for the company. Show Notes: http://www.superdatascience.com/103 19
It was a conceptual challenge rather than a technical one but in the end the model became almost 40-page Excel model, different kinds of data base that interact with each other. Yes, it was really difficult and the executives were blown away because they didn t expect that something like that could be made. It was a really analytical job and I took about one month to complete. One month only for one job. That s crazy. And how were the results. Were the results pretty accurate when you compared to the-? Really, really, really, really, really, accurate because they needed it to understand when and how much they had to ask the bank for a loan and how much they needed for a capital increase. The implication of the work was really, really heavy, it was a lot of responsibility and so in economics you also have to understand the differences between the economical side and the financial side. The model had to take into account both of the aspects of each transactions and of each variable, so that was very difficult. But in the end, I was so happy when they viewed it. That s pretty crazy. I m sitting listening to you here, this is like, I don t know what I was doing at 23, but I cannot imagine on my own going up to executives, CFOs and CEOs and building a 40-page Excel model, and you know just like talking about it so confidently. Show Notes: http://www.superdatascience.com/103 20
Guys listening to this, if you ever meet Emanuele in person, he doesn t look 23. I honestly thought, Emanuele, I thought you were like my age, 28 or like 30 or something like that. I think you ve got a lot of life experience which is great, which is on your side. Thank you. It s really cool to hear these things are going good. Okay, so next question. It might be the same answer, so if it is then we ll move to the next one but maybe you have another answer. What is a recent win that you can share with us that you ve had in your role? Like you ve talked about this project which sounds like a big win, is there anything else that you d like to mention? That win it s not a recent one but maybe the most important win of this year is the task that I received from the Wyscout company, and that was, okay, you have to redo completely our informative process, our financial informative process and our marketing informative process must be- you have to do it all over again. We want you in the next two years to rewrite all the dashboards, all the flow of the work, we want you to be the lead person for that. The win was to be recognized as someone that could do it, the ability to think of something that you want to do and have the power within a company to actually do it. Changing the way the people work in a better way, giving value to each person that collaborates in the process of building that information. It s a really Show Notes: http://www.superdatascience.com/103 21
challenging work but it s giving me so much satisfaction to be able to do it. That s really cool. So you re not only getting the fulfilment from delivering a successful project, but you re also helping people in their roles through your work, which I can totally relate to. It s a very powerful thing, it s like it s a next level, you re not just- it s delivering something which is actually helping people do their jobs better and more efficiently which is really cool so, yeah, congrats on that. Sounds like a big one. Thanks, Kirill. Okay, next question. Oh, this one. What is your one most favourite thing about being a data scientist? What inspires you the most, what makes you, like, wake up in the morning with a smile and you go and do this amazing work that you re doing? Okay. This is a simple one. [laughs] It s the ability to answer questions. As I said before, if I want to ask myself, what if I did that and what would happen if we did that? Data science together with modelling gives you the ability to create theoretical scenarios and to play with them, so you really can create your little world and then experiment with it, putting variables together and seeing what happens, seeing patterns through data, seeing what happens when people change their behaviours. I believe data science is a technological tool, Show Notes: http://www.superdatascience.com/103 22
obviously, but has a really heavy social implication. It helps you understand how people behave. Gotcha. That s very- Yeah, that s a great answer. I think you ve found something. Everybody finds their own thing in data science, what they love the most, and it s great that you found that and I think it s something powerful that s going to keep drawing you forward. For those listening out there, it s important to identify what s the most inspiring thing for you because, like, that s what ultimately is going to keep you going in any kind of activity, not just data science. It can be sports, it can be relationshipmaybe relationship is a bit of a stretch there, that s a bit different. But in activities and jobs and stuff like that, very important to understand why exactly you re doing it. What s inspiring you the most. All right. To wrap this up, I ve got a philosophical question which I like to ask our guests. From what you ve seen, you know like you ve got a very short but very saturated experience in the field. In one year, you ve gone from like zero to where you are now, like consulting and doing lots of different work and knowing lots of different tools. From this journey that you ve had, this very rapid growth, and the things you ve seen along the way, where do you think the field of data science is going? What does data science, two or three or five years from now look like, and what would you advise our listeners to prepare in order to be ready for the future that s coming? Show Notes: http://www.superdatascience.com/103 23
My personal opinion and this is not based on calculus, I will say data science business I believe is going in the same way of the website business. There will be on one hand where data science will become increasingly simple, so everyone will be able to do data analysis like everyone now is able to do a website without having really experience in doing so, without having the knowledge or the skills. Data science will be even more broadcasted to the general public but it will become a danger for the real data scientists because their work will become suddenly less valuable. Because, okay, even I can do that. This is what happened in the website business. The prices are constantly falling because everyone can get a website and if you want to really be ahead of this phenomenon, and you want to do real data analysis, you have to be always informed, be always kept up with the latest trend of the business, and you want to create real value to your data insights. My goal is to be on the high end of data science. I know that there won t be enough business analysts and data scientists able to analyse and crunch all the information that each new device provides but we have to do is to not focus on the low end, we have to keep pushing up to the most sophisticated tools and ways to approach, creating value. Not only creating some dashboards, putting some nice colours, because everybody in a short period of time will be able to do that. If you think about how many financial forecasting applications we have only for your phone, for your pocket money, Google analytics there are already so many dashboards ready to be used. The real question is how can I add more value to this Show Notes: http://www.superdatascience.com/103 24
dashboard, okay? It s not only the ability to be able to process the data but to extract information. I understand. To summarize, it s very interesting how this podcast went. We started off with the recommendation for people starting out to start with the basics and get very good at the basics, and then now we re finishing the podcast with in the future, the basics will become democratized, everybody will be able to do them so you will need to know how to add value in more sophisticate ways. So, kind of like my take away from this is that your view is that right now, based on what you described, I can agree with this: right now, it s a great time to be in data science, to get into data science. Yes. It s a golden age because there are few of us, we can do the prices, but we have to be ahead of the general market phenomenon. Yeah. It s really cool. This podcast should be titled The Golden Age of Data Science. This is fantastic, like, guys listening to this, if you re thinking of getting into data science, you saw what Emanuele could do in one year, this is not going to last forever, you know. Three, five, definitely 10 years from now, like you say, like the website business, these skills are going to be so democratized it s going to be much harder to break in, you re going to have to start higher. Right now, you can start low, build your way up and within those three, five, ten years, you re going to be performing much more sophisticated work, adding more value and you ll be on this wave so it s good to get on early. Show Notes: http://www.superdatascience.com/103 25
Thanks a lot for that overview, thanks for sharing your experience, fantastic. Thanks to you, Kirill. Can you tell us how can our listeners get in touch with you and connect with you? Okay, I can send you my email address and we can keep in touch by email and I promise I will update my LinkedIn profile and I will send it to you. I will try my best. Sounds fantastic. Definitely do that. Guys, connect with Emanuele on LinkedIn, I ve got a feeling he s got a very exciting journey ahead, it ll be very interesting to follow. I will be following along to see what you re up to in the year and how you re going with everything. I have one more question for you. Final question. Do you have a book in mind that you can recommend to our listeners that can help them become better data scientists or just propel their careers forward? Okay. I believe that the best book is not about data science, it s called Freak Economics by Levi- Freakonomics. Show Notes: http://www.superdatascience.com/103 26
Yes. It shows how you can use data to answer incredibly silly questions like for example why drug dealers live with their mums and this is really important to be able to understand how to think outside the box and how to use data and the economical approach to be able to answer questions. I believe that this book helps you give a broad understanding of what you can do with the information provided from the environment. As a data scientist, I believe that you must not have preconceptions. You have to listen to the data, there are some variables that you have to keep in mind, but you have to be humble enough to admit that you know nothing about the environment that you are going to analyse. Using silly problems is a great way to start thinking about it and it gives you the ability to build the what if mental framework. Gotcha. Yes, flexibility is one of the most important skills I believe in this field of work because it s more difficult to answer in a serious way a silly question rather than a serious one. Okay? Yeah, I totally agree. I haven t read it yet but I ve had a few people recommend it to me so I might check it out. Guys, it s called Freakonomics by Stephen Levitt and Stephen Dubner. Once again, Emanuele, thanks from us for coming on the show. By the way, I forgot to tell you. Thank you so much for Show Notes: http://www.superdatascience.com/103 27
that bottle of wine that you gave to Hadelin and I when we were there, we had it, it was fantastic. [laughs] Ah, I hope you enjoyed. Yeah, yeah, it was great. I think it was a white wine so, was it white wine? Yes, it was. Yeah, it was white wine. Thank you. Yes, a bubbly one. Appreciate it. We ll definitely have to catch up sometime soon. Thank you. Thank you, Kirill. Have a nice day. You too, bye. So, there you have it, that was Emanuele Carbone and a very inspiring story. I hope you were able to pick up lots of insights from here. For me personally of course, the most inspiring Show Notes: http://www.superdatascience.com/103 28
and exciting part was how Emanuele within just one year- I ve heard of people becoming successful in data science in three years but Emanuele s example tops all of that. In just one year he is his own boss, he is running a freelance data science consultant role for himself, he s helping clients, he s enjoying his work, it s really mind blowing and it s very inspiring because that means that anybody can do it if they really want to. Make sure to connect with Emanuele, you will find a URL to his LinkedIn at www.superdatascience.com/103, make sure to follow his career also at our website you ll find the transcript for this episode and the show notes as well. And, yeah, I really wish all of you the same success as Emanuele, of course in your own ways, in your own direction. This field is so new that you that you can always find different unique ways to add value. When you find them, you can create an amazing career for yourself. I can t wait to see you back here next time, and until then, happy analysing. [Background music plays] Show Notes: http://www.superdatascience.com/103 29