If you're interested in knowing the similarities and differences of Data Science in Latin America countries and others, how to contribute to the data science community, 5 characteristics of a data science project, and why data science is just becoming' a Science right now, then make sure to tune in!
ABOUT FAVIO VAZQUEZ
Favio Vazquez is a physicist and computer engineer working on Data Science and Computation Cosmology. He is currently the Principal Data Scientist at OXXO, Mexico's largest convenience store chain with over 17,500 locations. He is also the creator of Ciencia y Datos, a Data Science publication in Spanish.
Our guest for today is Favio Vazquez, a data scientist who's proud to be born and raised in Venezuela. Right now, he's currently living in Mexico where he has been working tirelessly to change the game of data science in there and also in other Latin America countries.
He's working four different jobs right now related to data science, machine learning, artificial intelligence, etc. Favio says it doesn't overwhelm him because he loves what he does. It's just pure passion and hard work right there that he's trying to give to improve his career and help the community.
His data science skills and knowledge are not stuck to just the industries he's working for. As much as possible, he wants to share them with other data scientists, analysts, and newbies. He teaches a data science course right now in a state college in Pennsylvania. Aside from this, he writes books or blog posts and makes webinars.
Writing has given him much freedom to share his knowledge and insights about data science to the community. Right now, his 37000+ followers on LinkedIn alone are benefitting from those. He was surprised with the growing reach of the posts he's making. He says he is thankful for this and always encourages him to continue sharing - especially for the Spanish-speaking data science community.
So, how's the current state of data science in Latin America? Favio says it is not that different compared to the United States. Local industries sure have heard of it and are interested to incorporate it. They're gradually transitioning. In terms of data science education, there's only a few who offer such specializations and there are also drawbacks with the usage of English language in data science courses.
We also took this time to expand on the 5 characteristics of data science projects, a topic he discussed in a webinar. These 5 characteristics are reproducible, fallible, collaborative, creative, and compliant with regulations which will be discussed fully in this episode.
On top of all of these, know why Favio thinks that it is just right now that Data Science is becoming' a Science. What did he mean by this? On what basis? Was he right all along? Curiosity kills the cat! So, make sure to listen in!
(Favio will also be attending DataScienceGO Conference 2018 on October 12-14 in Las Vegas. So, make sure you've secured your tickets!)
IN THIS EPISODE YOU WILL LEARN:
Aside from being a data scientist, Favio is also a mentor, a teacher, and writer. (03:45)
5 Characteristics of Data Science Projects. (15:48)
Data Science is becoming a full Science right now. (16:28)
1: Reproducible (16:54)
2: Fallible (18:55)
3: Collaborative (23:15)
4: Creative (27:43)
5: Compliant to Regulations (31:32)
Why is Data Science becoming a Science right now? (36:16)
The current state of Data Science in Latin America. (42:08)
Favio shares his experience as a teacher and a writer for the data science community. (48:35)
The 3 Different Kinds of People Who Explore Data Science. (49:38)
Kirill Eremenko:This is episode number 187 with Principal Data Scientist at OXXO, Favio Vazquez.
Kirill Eremenko:Welcome to the Super Data Science podcast. My Name is Kirill Eremenko, data science coach and lifestyle entrepreneur. 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.
Kirill Eremenko:Hello and welcome back to the Super Data Science Podcast ladies and gentlemen. Today we've got a very exciting guest on this show, Favio Vazquez. Favio is originally from Venezuela and he lives in Mexico where he works for a company called OXXO, where he is the Principal Data Scientist.
Kirill Eremenko:If you're not familiar with OXXO, then this is a chain of convenience stores, and actually a very, very massive chain of convenient stores. Think of it as in, it's a massive competitor to 711. They have over 14000 stores in Latin America. We discussed with Favio quite a lot of interesting topics, and in addition to being a principal data scientist in OXXO, Favio is also an influencer, a thought leader in the space of data science.
Kirill Eremenko:He's got over 37000 followers in LinkedIn and that's why I was very excited to bring him on the show, because you may already know him, may already have seen his work and followed him and heard of these things he's been doing.
Kirill Eremenko:In this podcast, we discussed quite a lot of cool and interesting things. We talked about contributing to the community. We talked about sharing ideas on LinkedIn. We talked about why data science is becoming a science, an actual science and what that means. We talked about GitHub and we discussed the five characteristics of a data science project.
Kirill Eremenko:Those are just some of those that we talked about in this podcast. Of course, there are lots more and I'm sure you're going to enjoy this journey and get some very valuable insights from Favio. Without further ado, let's dive straight into it. I bring to you Favio Vazquez, Principal Data Scientist at OXXO.
Kirill Eremenko:Welcome ladies and gentlemen back to the Super Data Science Podcast and today I've got an exciting guest on this show, Favio Vazquez. Favio welcome. How are you doing today?
Favio Vazquez:Hi, hello everyone. I'm very happy to be here, very excited and just hoping to just have a great talk with you.
Kirill Eremenko:Yeah, yeah, for sure, very excited as well and where are you calling from? Tell us, such an unusual occasion, I haven't heard anybody from this country in the podcast yet.
Favio Vazquez:Okay. I'm calling from Mexico right now from Monterrey.
Favio Vazquez:Yeah, in the North of the country.
Kirill Eremenko:That's awesome, but originally you're from Venezuela, correct?
Favio Vazquez:Yeah, that's right from Maracaibo.
Favio Vazquez:Very close to Colombia
Kirill Eremenko:Yeah. Okay, awesome. Well, very excited about today's podcast. It was actually yesterday looking at a webinar you were having with Randall Lau, so there are quite a few things that I picked up from there that I'd love to talk about. Of course, as we just discussed, it will be cool to talk about the situation of data science in Latin America, which is quite different to the US. To get started, tell us a bit about yourself. If somebody off the street were to ask you, "Favio, what do you do?" What would you say?
Favio Vazquez:All right. I think my first answer will be, I'm a data scientist and then I'll try to explain what's data science in a language that's very easy to understand. Normally this is not that easy, but what I normally can say is, I take data and I confirm it to actions for companies.
Kirill Eremenko:Okay, so you get insights to help companies act on their data?
Favio Vazquez:Yeah, and normally then there will be a short discussion on AI or maybe what's machine learning and how very common it is to have these tools in our hand, like in our phones and we don't even know it.
Kirill Eremenko:Okay, yeah, for sure. Where do you currently work?
Favio Vazquez:Right now I'm working for different companies. My main work is at OXXO. OXXO is a commodity is to be one in Mexico and have like 17000 stores here. It's very big and I work with Raken Data Science group and it's like a consultant firm for data science. They have a partner in a firm called Iron AI, where we do consult by yourself with data science. I also teach some classes in data science in Python, R, Spark and these kinds of things.
Kirill Eremenko:Wow. That's so many different things. You have a full-time job. I'm looking through your LinkedIn if you don't mind, I'll read them out. You're a Principal Data Scientist at OXXO. You're a Senior Data Scientist at Raken Group, that's the consulting. You're a Chief Data Scientist at Iron AI. I'm guessing this is the entrepreneurial venture and startup.
Kirill Eremenko:You're a data science lecturer at Afi Escuela de Finanzas. That's crazy man.
Favio Vazquez:Yeah. It's a school for, I mean it's from Spain, but they have like a new program here in Mexico. I just finished that last week in Mexico City, where I teach some things about data science or Pythons Park. I think it's very interesting because we don't have that many things here in America for starting data science, like in a serious way.
Kirill Eremenko:That's so cool that you're teaching and you're helping people. It's also very, it's insane that you have four occupations at the same time. How do you get by? How do you find the time?
Favio Vazquez:All right, so what I think is that life does not work. I normally have a lot of time for being with my family, my girlfriend and have fun and stuff. I mean because I really love what I do, I'm not like overwhelmed. Like oh man, I have so many things to do and this is kind of weird and stuff. What I normally do is that I do my best every time to do most of the work very fast, and then just tweak the things I do.
Favio Vazquez:Normally I'll start working with some model or something and I'll recommend it right away so I have to do it later. I have my whole setup for doing data science that's very easy. What I normally do is that I create like blogs informational webinars for people to understand what I'm doing and also for me for later to re-visit the topic. Normally you'll try like a 1000 libraries in one year. In the end, you don't remember hey, what did I do with this library? This is why I create some blog posts and stuff for me to remember what I'm doing and for people to have a peak of what's the life of contributing in open source in data science, something I'd really like. I'm a contributor to several libraries too, like Spark.
Kirill Eremenko:Yeah, okay. That's like a lot of things and you're right. When you don't feel like that your work is work, you don't get overwhelmed. In addition to all of that, you actually also post quite a few things on LinkedIn, right, like as you said to help you remember things so that you can get back to them. Is that like, does that all tie in together? That's a common thread that I've been seeing when people are very excited about the work and they have, on the side they teach as well. At the same time, they post articles on LinkedIn to help people and to help themselves remember. Do you feel that these things feed each other and make it easier for you so there's like synergies in between them?
Favio Vazquez:Yeah. I really think that my point, that the point where I realized that LinkedIn was a great platform for sharing ideas and also getting ideas from a lot of different people, I think my life changed. From that point, I realized that, okay, so data science is not only about working in a place here. It is about also explaining the work of data science and this is what I think data science is because it's very new. It's a very new topic.
Favio Vazquez:If we only do it by ourselves and we're in a closed environment, it's very hard to know people that are doing the same kind of things that we're doing or maybe understand some things that are not that easy to understand. Right now I think we're in the era of the blogs and books from data scientists. This is very interesting, because if you thought about it before when we were doing physics in the 1900s, I mean just like a very small group of people that actually develop the physics that we do right now. It's like Max Planck and you have Einstein and you have Heisenberg, you have Stoellinger.
Favio Vazquez:All of these different guys wrote books. They wrote articles and also they didn't have like Medium to create blogs, but they were doing articles in the format of a blog too. Nowadays I think it's very easy to understand hard concepts as truth blogs. I mean, like five years ago when I wanted to understand a thing, I just had to go maybe to an article or maybe just a book. Right now, every day you get hundreds of amazing blog posts or only like videos, webinars where you can get all of this different kind of information really fast. I think it all comes together like doing data science and also helping people because right now, I am where I am right now because people helped me. I think they indirectly help me and directly help me too.
Favio Vazquez:You don't know the power of the words you're saying, because sometimes there can be a post with two likes you created. For someone who says, "Oh yeah, this is like an amazing post that has changed my life." I have that experience. That's something that encouraged me to continue, because sometimes when I was writing a LinkedIn post and stuff I was not getting that much attention in the beginning. People started to write to me, "Hey, thank you very much for doing this post. It was very helpful." Or maybe people just wrote me like, "Hey, if you want to know more of the things that you were writing, I wrote a blog post yesterday." There was a big combination of things that made me the person I'm right now.
Kirill Eremenko:No, that's very inspiring and I love that you mentioned even if your post has like one or two likes, you never know how that impacted those one or two people that liked it. It might have just changed somebody's life.
Kirill Eremenko:Definitely I've been in those situations as well, so it's definitely inspiring to hear you say that. Yeah, that's a very descriptive and congratulations you're on the, what, you're like at 36000 followers on LinkedIn right now, what a journey. Tell us a bit about that. How did that happen? How long did that take?
Favio Vazquez:I mean, I think my experience with LinkedIn was very similar with my experience with GitHub and I'm going to explain why. When I was starting with GitHub, I was studying computer engineering back in Venezuela. I used it as a source of a code to help me to do my homework. That's it. I mean I didn't even understand, it was like a platform for building open source codes. I didn't know it was these different kinds of things.
Favio Vazquez:I used it like, I search on the internet and all the answers were stuck overflow in GitHub. I started as a user in GitHub, but in the end, I realized hey, this is people like me, that they're just posting their ideas in a code style. I started to contribute to my projects in GitHub and also on parts like Spark and things in Julia, Python and R scholar, so I got very excited. It was the same for me on LinkedIn.
Favio Vazquez:When I discovered LinkedIn, it was like, okay this is for searching for work. I worked at a time because I was right away from my masters and then I realized hey, but this is more. People are sharing here ideas, they are sharing blogs, you can create articles on LinkedIn. I started doing the same, because I was so inspired about so many people like you, building these great things, like Super Data Science and big pages for data science. Hey, I can contribute too with the little things I know.
Favio Vazquez:That's what I started doing. I mean I didn't do it for the followers, I started sharing my ideas. Some of my posts were very popular and I think that the first time I got a lot of attention in one of my LinkedIn posts was when Jeff Weiner the LinkedIn CEO liked my posts. That was very weird, because it's like, how did he find my posts? That post got like 3000 likes. I went wow, this is amazing.
Favio Vazquez:Then I realized I had a voice on LinkedIn and this is why I started writing more things and more things and more things. Right now I think the amount of followers you can have on LinkedIn is a way for you to say that you're doing things right and you're sharing important things for those people.
Kirill Eremenko:Yeah, that's true. Interesting to hear how Jeff is, Jeff Weiner is learning data science. It looks like everybody is learning data science now.
Kirill Eremenko:Yeah, okay well, that's very cool and I appreciate you saying that you're not doing it for the followers, you're doing it to share and help people. As we discussed before, it's very important, even if you have one or two people that are, you've already done a huge contribution. Okay, well, so that's a quick overview of who Favio Vazquez is. Now let's talk a bit about data science. Let's talk a bit about your work.
Kirill Eremenko:I probably would like to start with something you mentioned in your webinar with Randall Lau. I really enjoyed this so maybe you can share it here as well. You were talking about data science projects and you said that all data science projects have to have five characteristics. I will read them out here and then maybe we can go through them one by one and you can give us your thoughts on that.
Kirill Eremenko:You mentioned that all data science projects should be reproducible, fallible, collaborative, creative and compliant to regulations. Could you walk us through this one by one and maybe give examples where it's possible.
Favio Vazquez:All right, so there's a lot of talks right now about reproducibility in data science because we are doing science in the end. I think before talking about all of these five points you mentioned, I think I need to start with my viewpoint on data science. That, it is becoming a science. I think right now it has the name science in it, but it is only becoming a full science right now.
Favio Vazquez:If data science is a science, and if this is the case, I think all of these five points should be there in every project we do. The first one is, of course, necessary to do science. It's impossible to create an experiment and if you're the only one who can reproduce the experiments, it's not, in the end, an actual contribution to the world. I mean if I say in an article, "Hey, I just discovered that water boils at 100 degrees Celsius," but I'm the only one who can ever test it. This is like a mysterious article, like oh, what is this guy doing.
Favio Vazquez:Science is based on the way that other people can reproduce your works. I think data science is very easy to do, these different kinds of things because we are in an open source world. I mean most of the data science contributions are in open source, Python, Rs, Color. We all have the blog posts and information and articles on all the things we do. I think the path to reproducibility is not that far away from data science.
Kirill Eremenko:Got you. Makes sense.
Kirill Eremenko:Basically what's the point of insights if you're the only one who can get them? You want anybody to be able to do that.
Favio Vazquez:Yeah, of course. I'm not saying that you should post the work you're doing for your company because you're going to get fired. What I'm saying here is like the techniques you're using are of course sharable if your company allows it. Of course, all of the designs that people do in companies or schools are not fully reproducible. I mean you need to have the data they have or the equipment they have. There are paths to or several steps on reproducibility and I think data science should be at least reproducible in environments that are similar to yours. I think this is my first one.
Kirill Eremenko:Got you.
Favio Vazquez:The second point was collaborative right?
Favio Vazquez:Oh fallible. This is a very important thing here. Science is not in the look for the truth. I think this is a very important thing to say one and again and again and again. We're in the look for knowledge. If you want to read more about this, I really recommend you, you do some reading on processing or epistemology. The thing here is, we're not solving the problem forever with a solution that will hold for eternity, all right?
Favio Vazquez:We're creating a solution to the problem with the technology, the theory and all of the apparels we have right now. This is important in science because if I go through an article and the article says, "This is the final solution to the problem, no one else should do anything more about this," this is not going to be a thing in science. This is not a found solution.
Favio Vazquez:This is also very cool for you to explain to your company. You're creating models and models are an obstruction of reality. You're actually trying to create a vision of reality that works for you and you can understand it and test it. You're not actually finding the ultimate solution for a problem. You're finding a solution to the problem.
Favio Vazquez:Right now with data science what we should be thinking is, okay, I found a good solution to this problem, but of course it can be better in the future. That should give you a touch of being humble of what you're doing, because sometimes we think, yeah, this is the best model we can ever create for this problem. That's never true because science is never going to look for the hidden truth. We're looking for the knowledge. When you look for the knowledge, there's no stop.
Favio Vazquez:This is very interesting because people sometimes ask me when I was doing science, when does it end? What is the end of science when you know everything? My answer is, we're never going to know everything. This is not the path of doing science. Science is not about the truth of the universe. It's about the knowledge we can get from the things we see, understand and can cite.
Favio Vazquez:That's the second point.
Kirill Eremenko:Totally agree. I'll probably just add to that by just defining, I'm sure people are getting an image of what data science kind of is in terms of being able to make mistakes or not being able, not having the ultimate truth right away. Just to sum it up, I will read out the definition of the word fallible, because when I saw in your presentation I realized that I didn't know what it was, so I had to Google the definition.
Kirill Eremenko:Fallible is an adjective, it means capable of making mistakes or being erroneous. That's exactly what we just discussed, that if you're not 100% like every single time you do something and make a discovery, that's the final truth. That might be correct, but might not be the full picture, right, that there might be more to and usually, there is. As you say in science, usually there is.
Kirill Eremenko:I love how before you gave the examples of Newton and other physicists like Einstein and so, because like you're a physicist. It's interesting that when we started just before the podcast we were talking like we both studied physics and just different styles. Like you studied, what was it? Cosmology, right?
Favio Vazquez:Yeah. Cosmology yeah.
Kirill Eremenko:Cosmology and now you're into data science and I also studied physics coming into data science and a couple of other people like a lady [inaudible 00:22:45] that was an astronomer now she's into data science. Interesting how depending on your background, depending on where you came from, you see data science from a different perspective. I think this will be valuable for a lot of our listeners who might not be coming from a physics background to hear this perspective of data science as a science and all of these different aspects and characteristics that we're mentioning here. Let's move on to ...
Favio Vazquez:Yeah, I think that's a very good point.
Kirill Eremenko:Yeah. There's more, number three, collaborative. What do you mean by that?
Favio Vazquez:Right. Collaborative I mean here that, we exist in a team. I mentioned that in a post last week on LinkedIn because some people are thinking that you can be a data scientist by yourself without seeing anything else. That's impossible because we are an applied science. That means that we need someone else to solve problems too, because if no one else is giving you a thing to solve, or you're not having a problem to solve with data science, then you're not doing data science.
Favio Vazquez:So we exist in a team in two ways. First is your actual team like or data scientists, your manager or some people data analysts, data engineers, people that work close to you. It's very cool to have this kind of different like the perspective of what you can do with data. I think a data scientist should not be like an expert in each of these topics but have an idea of what these people are doing.
Favio Vazquez:The second part of the theme is the business you're working with, because sometimes when you are a data scientists, you don't need to reply or to solve problems for very different kind of people. Like marketing people, business people, people working with the distribution of something in your company. You'll need to be able to talk with these people in a different way you talk with your team in data science. You'll need to have a way of understanding what they're saying and what are their requirements.
Favio Vazquez:It's not the same to hear someone talking about a business, their business, they're working if they are a business guy or they are working in the marketing department. I think this collaboration is what in the end will guide you to make a good solution that will answer the problems for the business you're working with.
Favio Vazquez:This is the same in science. I mean, of course, you're going to have this scientist working by himself and stuff. In the end, his trying to solve a problem for everyone. He's trying to understand the world. In the end, right now I don't think that picture of the mad scientist in a laboratory by himself, that's not true at all. I mean, my experience doing science was very different. A lot of people working with me. Every Friday we got together to see what others were doing, understanding the different parts of an article or different kind of things in a subject. I think this collaboration is what makes you an effective data scientist.
Kirill Eremenko:Yeah, and just when you were saying that example of the mad scientist in the laboratory by themselves, I remembered a part of a book I'm reading now, it's called 'Sapiens'. There they talk about collaboration or the author talks about collaboration in the sense that, during the cognitive revolution, that was the deciding point for our species as sapiens to take over the world. The fact that we can coordinate our efforts in large numbers is what distinguishes us from others.
Kirill Eremenko:Like try putting 10000 monkeys into a stadium, there's no way they're going to collaborate, there's no way they're going to be able to follow rules or to work together and watch a concert or do something together, watch a soccer game. They're limited in their capacity to collaborate 20 or so people and things like that or monkeys and things like that.
Kirill Eremenko:In our case, the fact that we can, we have this cognitive apparatus that allows us to collaborate, that is one of our biggest advantages. We should really use that, especially like in complex things like science, data science and whatever else we're doing for our profession. I totally agree with that.
Favio Vazquez:Totally agree, yeah.
Kirill Eremenko:Very important to keep that in mind.
Favio Vazquez:Okay, so next point.
Kirill Eremenko:Excellent yeah.
Favio Vazquez:I think it was created right? Creativity is something that is like really defined in a weird way for most people. I don't know if your people listening here ever listen to people saying, "Yeah, so the creativity is in the left part of the brain. The math and language are in this part of the brain, and the artists are the people who are very creative. Science is like very on the other side with mechanisms and tools."
Favio Vazquez:Okay, so this is why I think it's weird. I don't think that a mathematician solving a theorem is not a creative person. I think he can be the most creative person in the world right now. I think trying to solve things with science and with mathematical things and trying to understand the world, you only have to do it in a creative way. They're in the workplace. When someone says, "Hi, this is the creative department," they're not the data scientists. They're people creating like images and stuff.
Favio Vazquez:I think this should not be the way we see data science. Data science needs creativity because some of the things we're solving are not solved. We have no answers for them and this is why we've been, I mean if you search for data science on the internet right now, you'll get a lot of different things. Data science for applied businesses, for science itself like papers, research, review, overviews, surveys. This is why we're trying to find a definition that contains all of this information we're trying to use to solve problems.
Favio Vazquez:I think we need to be creative because we're using things from other domains. Let me give you an example here. A lot of the things we're using right now for doing machine learning and deep learning, they come from biology or chemistry or different kind of science. I used just look for the articles of [inaudible 00:30:04] they were all published under biological science review magazines.
Favio Vazquez:It's very creative to have found a way to apply a model that was created for a specific purpose to another place. Right now we're using random forest and GBMs and literature rations for doing things that no one ever thought about. I think this is one of the realizations of creativity in data science, being able to use a different kind of things from other domains and apply to your work.
Kirill Eremenko:I definitely agree with that as well. Data science. I find that some of the best data scientists come from creative backgrounds, whether it's arts or music or something completely unrelated to science. Simply because there is so many capacities, so much room to be creative in data science. They find how to apply their existing creatives skills to become even more successful. I think everybody in data science should.
Favio Vazquez:Yeah, no and again, my point here is trying to make people understand that science is also creative. Data science is creative because it's a way of science, so this is my view on creativity in data science.
Favio Vazquez:I think the final point is very important and is compliant to revelations. I put that, I mean I work on that list like a year ago. I work in a bank for like a year and then I realized how important it is to follow the rules you have to create models and see the data. The thing is, right now we're seeing more and more updates for the things we can do and cannot do with the data of the customers or of people we're analyzing.
Favio Vazquez:I don't know if you remember but last two months, I mean my inbox from my email was full of emails. We changed our rules and agreements. It was all because in Europe there was a lot of changes in how people can access, view and understand the data from people.
Kirill Eremenko:Oh the GDPR right?
Favio Vazquez:Yeah, the GDPR.
Kirill Eremenko:Global Data Protection Regulation.
Favio Vazquez:Yeah, and I got a lot of messages from companies that were sent to me for me to be aware that now they're following their extenders. Right now this is like the main point of revelations in the data world, but there will be more and more in the future. I mean if you think of science, I think everybody knows right now that cloning a human being is illegal.
Favio Vazquez:If you think of data science, we can do almost whatever we want with the data. I think we are very free to do whatever we want right now and I don't see that as a bad thing. I really think that relations should be in place because data are people too. I mean when I was working in the bank, when I created models for like credits carrying model, I really had to think about, hey, this is not only data. This is people's lives.
Favio Vazquez:I mean I'm here understanding what they buy, what they cannot buy, I mean how they spend their money and what are their emergencies. Why are they asking for this money right now? When you're working with the data from people, I think you need to think about this not only points in a spreadsheet or a data frame. This is people you're talking about here. I think recollections will be more important in the near future and also in your own company.
Favio Vazquez:I mean there are some departments where I've worked that they said to me, "Yeah, I need a model that follow this different kind of bullets here because we need to be able to understand the model and the full picture of it. I think if you want to do whatever you want, I think first you need to understand what you can and you cannot do in the company you're working with. Then you start working after understanding these restrictions.
Kirill Eremenko:Yeah, I love the point. You mentioned this in the webinar as well. I was quite touched by it, that when you're working with data for us, any role is a data point, it's completely anonymous, we don't really, often we don't even know what's behind it. If you stop and you think about it every data point is actually a human being where feelings, emotion and life, their own privacy and that's important to always respect, always important to keep in mind. I think that's ...
Kirill Eremenko:We try as a society, we're trying to put it into regulations, but if everybody just thinks of that and everybody just keeps that in mind, like half the problem will be solved. Half of it is data issues and privacy problem will go away. All right, well thank you so much for that overview of the five different characteristics of data science. I also like how you mentioned at the start, maybe let's recap this whole thing with just talking about that for a second.