 Good. Welcome everyone. I'm very glad to see all of you here. Valerio, I am now working for Anaconda as a DevRel. But before that, I was actually working in academia. So I was a researcher doing my research in machine learning, specifically applied lately to health care domain. So I'm working a lot with really different types of data. But most of all, working with different types of colleagues. So I'd be like to talk about that as well. And so my path to data science is one of the most boring ones. I have a background in computer science. I'm a PhD in machine learning. So this is how it gets boring. And then I'm working in data science since then. And it's been a very interesting path. Especially I've learned a lot through this. And I'm very glad I had the opportunity to share a little bit of this. And also the most important part is not about the technical things you learn, but also mostly what you learn from other people working with you. And this is one of the things I personally like about data science. Right. Passing it on to Chuk now. Hello. Hey. So, yeah. So my background is, of course, I'm not a smart. I didn't get a PhD. But my study is physics. So I was, I like math and, you know, complex things. So I study math and physics, but then decided that I won't get a PhD because there's no job for me in physics back when I came from, I grew up in Hong Kong. So there's no physics job there. Like all my friends, they all work for the government, which I don't want to work for the government. So I decided to go out and try different things. I've been a mascot in a theme park. So anyway, okay. Chuk aside, I moved to the UK and then wanted to start something new. And my friend suggested me to, oh, you have, like, you can do math, you have, like, you can code. How about, you know, but my coding skill was bare minimum, right? So then my friend kind of encouraged me to start studying data science, that kind of thing, try to get into the field. So I was very lucky. I got a job first as a data analyst. Then I was promoted to a data scientist or work as a data scientist for a few years. And then decided that I love the community. So I switched to more like community role. I used to work with Valerio. Yeah, and now I'm also kind of in a community role right now. So all right. So I've had like a bit of a weirdish sort of part, but but more or less traditional. So like my name is VB. I work as a machine learning developer advocate engineer. That's the official role. It's more boring than it sounds. And I work at Hugginface. And so I studied computer science, and then I did some web development for a bit that, you know, flask used to be cool back then. There was no fast API. And then after that, I did some consulting. So I did like business consulting. So I literally made PowerPoint presentations for a living for three years. And then after that, I decided to go back to academia. And then I, you know, did my masters in computational linguistics, which is a worldwide way, like worldwide way of saying that I, you know, studied English, but with a bit more maths. And so I finished that actually last, last Friday. And so I submitted my pieces and I barely scraped through. So I did that. And now I specialize in sort of speech and like text to speech and like audio sort of speech recognition and so on. So it's been like kind of like an up and down journey and like I've taken bunch of different roles. And, you know, I did, you know, well in some and horribly in some, but, you know, it's been a right. So yeah. And hi, I'm Jodi. I work as a data science developer advocate at JetBrains. So I probably have the weirdest background maybe of all three. I'm an academic as well, ex-academic, but my background was clinical psychology. So I was a psychologist. I was licensed to practice. I saw lots of patients with anxiety disorders and things like that. But actually what I fell most in love with during psychology other than the people was statistics. I loved it so much. Psychometrics, measurement, things like that. So I did a postdoc in public health and biostatistics because I just wanted to do so much more stats. And then I realized I hated academia and I didn't know what to do. And my now husband suggested I go into data science. But the thing was I didn't know anything about programming. I'd done like an introduction to Python course. I'd used some R. The first time I used R it took me two hours to read in a file because the slashes were the wrong way. And I started crying at one point and I was like, I hate this so much. And I was scared of the command line. I'm like, I can't do this. Luckily I had my husband to mentor me and I will maybe talk more about mentoring if anyone wants to know about that. But yeah, I just persisted. My first role was much more analytics as well because that was what I knew. And just over time I've kind of explored how much I want to go into the engineering side. I realized I don't like it that much. I'm a scientist at heart and I've stuck more with the research side of things and prototyping and communication and teaching. So I want to say, if you feel like you can't do the engineering side, think of me crying about the slashes going the wrong way. So over to you guys now. Who wants to go first? And does anyone have a question? So jump up to the mic there. Yeah. And if you feel more comfortable, you can grab the mic and sit down. This is pretty chill. Okay. Yeah, I don't really have a huge like it's a fairly open question. I guess that's what this is all about. I'm sitting in the lunchroom talking to colleagues and some of those sort of half developers and half data science and then something in between. So we tried to define what a data scientist is. And of course, this is you already introduced that and I see several different backgrounds here. I guess from what you're saying, I have a sort of data science background, and especially in research, but I am a software developer. Try to make that into a question, I guess, somehow. This is such a good starting question though. Does anyone want to start? Set the stage. So when it comes to like data science in general, right? So every firm on this planet has a different sort of expectation from a data scientist, right? And that's like that's the general standard in the field, right? Same goes for machine learning. Same goes for for, you know, actually, if you think about it from like a software development standpoint as well. When you look at a job description of a software developer, it can range from, you know, someone who's sort of dealing with like the entire front end stack versus someone who's dealing with like a back end stack, you know, someone who's dealing with data engineering pipelines and so on, right? So in my opinion, like the best way to explain data science is any and everyone who works with data, right? And it could be something as simple as just, you know, analyzing bunch of CSVs or like bunch of text fights or or do something, you know, more involved, like, you know, connecting to big data databases and then building dashboards on top of it to like building some productive, sorry, predictive modeling algorithms, right? So that's how like I sort of understand the field. Okay, so yeah, I think VV has a very good point here. But for me, I've heard my friend who's like very, I respect them a lot. They are very experienced data scientists. And he said, like, you know, he would say like, Oh, I used, I used to be caused that statisticians. Now data scientists sound cooler. I would say that the field is very broad, like VB said. But if you are using data to tell a story scientifically, which means that you use scientific method, you're not trying to mislead people, you're using data as an evidence to tell a story. That is data scientists. That's what you're doing. And the field is very broad, of course, like every company they expect that their science team is doing different things. There may be some like new job title. Like, I think before, you know, like 10 years ago, maybe nobody taught about data science. And then now like, every company will have a data science team. And then now there's like even roles that I've never seen before. There's like machine learning engineer, which like, you know, when when I was just started as data scientist, I never imagined what is a machine learning engineer, right? So there's like, you know, a job title and a job roles keep changing. But as long as you use data to tell a story scientifically, even if it's something, as you know, with minimal coding, or something you like, like machine learning, you're doing like last language model, like to today's keynote, you know, later today, we'll talk about last language model. All those are all data science because you're just using data and you're telling a story. So yeah, well, I completely agree with everything you said, torture on the same page in this. And it's also my experience. And in fact, I feel like the only thing I should add at this point is, well, when when you work with did science, first off, you don't have to do machine learning. So machine learning is just one bit of it. In reality, is always the last thing you do whenever you're doing whatever data processing or data science, how you want to call it. And I guess that the most important thing to highlight to me, and I totally sympathize with everything they said is so the takeaway message from data science is such a broad term, such an umbrella term that can like cover every every niche detailed role we want to come up with, is that there's no precondition to get started with it science, you can tackle with the data science domain from the angle that suits you best. Meaning, if you have a background in computer science, well, data science has a lot about programming things. And, and we've been talking about ML engineer, this is just the sublimination of, is that word? Oh, no. It's my God. It's, it's like, it's whatever is ultimate into, yes, I have something working, I need to make a production. This is what we're doing. But, and this is really a computer science job. This is this is what it is. But somehow you have to come up with a model, you have to do data analysis, data analysis, you have to come up with a method, you have to prepare the data. And there's a lot of experience and expertise in, in play in doing this. And so you can really contribute to this project. It's not just like a label. And I am a data science, I am a data scientist, I can only do these and that, but I cannot do that. I mean, you can contribute to the, to the whole scheme of project with your expertise, no matter what. That's, that's my takeaway message. And just a brief thing to add. I think what really overwhelms people when they're first starting is because of the broadness, or the breadth. I swear, I speak English. It can be really overwhelming and you feel like you need to know everything. So you need to know math really deeply and you need to know how to program like an engineer and you need to know this and that and that. My code is embarrassing, but I'm not embarrassed anymore. I used to be. Because that's a specialization, I don't want to learn it. Over time, you will gain the knowledge you need. There's sort of a core set of things that I think you do need to get started. And again, you can ask us about that if you're interested. But what I would say is when you're starting, think about what interests you and start projects in that. And over time, it will come. You don't need, like, I don't know everything. I don't know. I've been doing this for seven years. I don't know everything. The tools and the skills are secondary to the problem solving. But I also think that's true of software engineering. You don't write software. You create a product when you are doing software engineering. Maybe you could disagree. But with data science, you're definitely, like, you're doing a scientific method. You're solving a problem. So, yeah. I think, first, I would like to clarify that data scientists normally don't work alone. There's lots of people involved into the project. You never work alone. It means that you're not responsible for everything. You're responsible for the thing you're good at. And there's no shame in not being able to do the rest of it, because it's not your expertise. Sorry, I don't think it's possible, either, except in a teeny, tiny startup where you have three people. I totally agree. And actually, the thing I wanted to say is these breadth of the field and this diversification of expertise and experience in the team can be overwhelming in the beginning for beginners. But at the end, I promise you, it's a feature. It's not a bug. Meaning that you, first, you don't have to worry about everything. And talking about the code, it is very likely that if you're not coming from a computer science background, you don't care about the code. Because it's something you're not so opinionated about as a computer scientist. So it's like, yes, it works. Fine. For me, it's fine. And it's very fair. And in fact, if you want to make it working in production or whatever, you want to prepare the pipeline to be maintained and going in the experimental is numerically sound and whatever, it's not your job to do that. Your job is may probably come up with the analysis. So once it works, someone else is going to take over. Yeah, I will add that. I do agree on this at some point that you should focus, if you're a data scientist, you should focus on how to use the data to solve the problem, like Jody said. But I do suggest that if you want to, you know, heighten your experience, like do learn some new skills, like learning what tool would help you solve the problem, maybe learn some, so learn programming as it's like a tool to help you to do your job, not as like, you know, you don't have, but like, or, you know, sometimes like, oh, there's always the debate of like, all the scientists, they don't write like quotation, good code. Like, I think it's not a very good statement, but you can try to understand how to write some code that would work better with your colleague. For example, if you have data engineer, what kind of, what code will make the job easier? What, like you, you both work together so you can learn in the job of like, how to make the whole team success, be successful. And I can tell my story of me working with, with mathematicians, for example, in this, in this team did scientists. And I was the one at PK about the code, of course, as you might imagine. So I was like, what, what does code, what, what are you doing here? You can do this better. You can do this more efficiently. And, and he was like, yeah, right. Okay, it works. And I, and jokes apart, that was a learning process for both of us, meaning that whenever I had some doubt about modeling or like brainstorming some ideas of things we might want to do or something, I was working with him. And with other people in the team. But in return, when he, when he had doubts about, should I do this, should I do that through my code and blah, blah, blah, he came to me and all the colleagues and like sharing ideas. So it's just like a learning experience. And that's why I personally love data science. It's a never ending learning process. Even when you, even when you're working on something new, it's always a new experience. Can I add like one small thing? So like, like this, like, sort of just like remind me of like how exactly did like, I start into that data analysis, I would just like go on like any open data set that was available that I could find an API for and or like just the CSV. And there was like this open sort of data sort of portal within India that, that anyone has access to. So what I would do is I just like download a CSV and import pandas and then like start making like plots. And what I used to do is this is really nice subreddit. It's called data is beautiful. So I used to go there and I used to just like create a tagline and upload a graph on it, right. And my goal for a week would be that I would try and depict one problem with this graph. Thank you. Thank you. Thank you. And you know, as soon as and so week over week, I would like, I would get quite a lot of criticism as well. Like people would be like, what are you showing? Like you can't like plot absolute numbers. I mean, whoa. And then like, so like week over week, I would learn. So like one thing that I would really, really strongly encourage is to like, not get into the perfectionism loop as, you know, Valeria was saying, is to just like, just really just like try and get into the habit of like, just pushing out one thing, one week, like in a week, in like two weeks, just like push, push out something, get some feedback, be it critical and like try and do something different the next week and so on. And that's like a good way to sort of, yeah, you know, yeah, get in. Yes. There's just one very pressing thing I want to say in the interest of time. And this is something we all discussed before. And by the way, you just remember that I should add a slide in my talk with a joke about graph and things because I'm not talking about the graphs you're going to. Anyway, let's put this out. Chris, you don't need a PhD to get to do this science. So I have a PhD, but it's, I'm not recommending you to do that. Do that, do that only if you really want to. She might agree with this. But let's talk about this now and forever. You don't need PhD to do machine learning nowadays. It's probably never been the case. So if you don't have, if you don't feel entitled enough, that's, to say politely, that's something not very true. It could be some, yeah, I had different words in mind. Right? Yes. Yes, okay. Yeah, I think we can go for another question. Sorry, we went for so long. Yeah. Which man coming up to the mic? Oh, sorry. And then, yeah. And we'll just maybe try and keep it a little tighter. Yeah, because I think we've got around, oh, five, ten minutes. So yeah, sorry. Hi, nice to meet you. Just a quick question. You mentioned about the breadth of different facets of data science. Just what do you guys do on a day-to-day basis? What are you working on right now? I'd like to see if you guys are all working on different things. Let's do a lightning round. Right now my job's a little weird because I'm a developer advocate. But at the moment I'm working on a series of videos on how to use database tooling better in PyCharm. Same for me, actually. I'm a developer advocate as well now. So it's content generation and what I'm actually doing is like working a lot in this new PyScript project. So trying to put these signs in the browser. That's what I'm doing. I think I've made the career switch. So now I'm working with the open-source community. So I would say that I'm an open-source advocate. Right. I help sort of make complex machine learning models specifically for audio accessible for developers on Huggingface. So essentially like I help bring like models for speech recognition and text-to-speech within Transformers which is a library within Huggingface. So hi. I think stepping back a bit, I think you've covered it anyway. I'm going into my final year of doing a master degree. I've got basic coding language. I can Python, I can do a bit of that. And I am potentially looking into going into data science or whatever you want to call it. What do you think the best way is going in because you meant to go to data analysis and then going into data science? What past do you think from someone who's quite got basic levels of coding is the best kind of route to go and is willing to learn? Okay. Can I maybe just queue it in? Okay. So I would say that the best the best path would be to just like start with analyzing any sort of data set that you get. Right. So like get a feel for data first. Right. And when I say like get a feel for data, I mean like just try and understand the nuances that exist when you deal with data of different kinds. Right. So data is just not just not like a tidy CSV or like a tidy Excel sheet right around. It could be in like different formats and so on. So just like try and get to get to sort of look at the breadth of available things. Like once you get a feel for it, you know, then then you can sort of build your way through. So like say you start with understanding the data, then you start with like building certain visualizations on top of it. Then like you once you understand data, then you can like start looking into like predictive stuff and so on. But I would say like just take it step by step and start with the data and then go all the way from visualizing it to like predictor side and to you know modeling site and so on. Yeah, again quite briefly. Perfect point. I actually started a blog when I was first. My posts are so embarrassing but they're still up there. I still have the blog and then my first role was data analytics because it was a lot easier to get into. So you don't need to limit yourself to that but maybe a role where you're going to be doing less machine learning even and more focus on maybe analysis and stuff like that. And then on the job you can learn the engineering stuff. There's always chances to have people look over your code and see what other people are doing. Yeah, very one time thing I want to add and thank you very much for the question is about the technology, the tooling you choose. Choose whatever you like. There's no right or wrong choice and I tell you my story. I was the only one in my lab as well. To put everything in context was my PhD time so I was sort of free to choose whatever I liked but it doesn't really mean that you have to choose whatever you also use at work. You can choose your own path to learn independently but what I'm trying to say is I was the only one using Python back then. It was like no one was using Python. We have to use Java and I said it is Java. So yes, that was my essentially reaction all the time. I didn't want to because I was feeling more comfortable with Python. It's also a case of not just the tool is also the community and also the support you have because if you're using like very tiny little niche language, yes, it's fine. It could be fun for you but it's just it's limited. So it reflects to the next steps but nonetheless to get started start with whatever you like and this is a really broad recommendation for tools to language to technology even the laptop. I have a bonus point for that is to find something fun to do. Find something fun to do. You can either like Jodie start a blog. If you found something interesting for example today you hear something interesting you want to dive deeper into it study it and write a blog about it or if you prefer working with people volunteer. I know there's like an organization called DataKind. They are doing a lot of like investigative like data investigative things. So by learning like spending a day hacking with people doing some like projects it's good for the society but at the same time you learn from each other learn from other people. So go go to meet people go to meet up and go to the community and you'll find lots of fun stuff to do and for me I think that's the best way to learn. Go to Reddit data is beautiful but cannot recommend it more. Are you done? All right. Okay. I was thinking of I totally agree with what you said and actually I'm more like more strict to that is not one way it's the only way to learn because you can take a book learn everything about it. You don't understand anything. You're just like yes you can understand that sign but then it's like it's very difficult to put in practice. Whereas if you have really a project to work on however complicated you think it is it's always the right one. How are we doing for time? Do we have time? All right. So yep sorry what you're going to say. Yeah so we got through three questions. I would say we can stay in here if you want to stay for another question. I think we can do maybe one more. You may need to run Chuk. Yeah yeah I think we can yeah so maybe let's end the yeah let's end the formal session. Let's stick around to you. You guys can go out and get your coffee and then we'll sort of slowly make our way out of here as we get kicked out. So thank you.