[MUSIC] Welcome to University of California Computational Social Science. I'm genuinely excited about this specialization for at least three reasons. The first reason being that computational social science is extremely relevant and also cool. That's kind of like what rules the world right now, right? So most of us and most of our teachers were trained in a paradigm where basically, the banks own everything, the industrial giants gave us employment and we started wars over petroleum. So since the 1950s, the most valuable companies the companies that have been running this world haven't changed a lot for over half a century. Now recently in recent years, there has been a dramatic change compared to that. The five most valuable companies by far our digital companies and the most exciting thing that has happened in the last few years is that a few of them sometimes change rank, but it's become clear that this is not a coincidence right for years. Now the most valuable companies on planet Earth basically, what they do is they study a big part of the business model is that they study human behavior detect social patterns and convert that into economic value and that's more valuable than even petroleum. So we will be studying some of the tools that these companies are using. Secondly I'm very excited about this course because it is also an introduction to the scientific method and the scientific method that spans actually well much more than one specific discipline a lot of science. What is changing is the way knowledge is created which is not surprising. I mean what these technologies do is they digitalize information and communication and research systematic knowledge creation. Is a 100% based on information, communication, and computations of these tools, of course a changing the way science is done. And in a science that has huge opportunities a lot of low hanging fruits. He had to be picked a lot of work to be done for all of us together. Think about it. For example, we've been extremely successful for hundreds of years of doing physics and this is a lot about the universe around us. But very honestly we still do not know how many stars there are in the universe not not even that right the same with biology and medicine medicine has been extremely successful. We have expanded the lifespan of humankind significantly over the recent century. But honestly, we still do not know where all the cells are in our body and exactly what are they doing in every second. Ecology the same thing we do not know where all the fish are in the ocean and what they're eating now compare that to social science. The vast majority of humankind 98% carries a tracker with them that tells them every second where they are. We also see it in the also know what most consumed and eat and what they communicate with their friends because they track all of that at least behind this digital footprint that we can now study. So while the social sciences traditionally has been the most data poor science people were even questioning. Is it really a science? Is it more like art? Is it humanity? Now we are probably the most data rich science because our entire universe everybody is basically tracked and there are a lot of discoveries to be made and a lot of discoveries that we already making in all kinds of fields. I already talked about economics and of course economics is an important driver, but also others communication, that's the field where I am, very busy studying media and messages of course is very affected by that anthropology linguistics psychology. Sociology and or political science you think about democracy by itself elections right now for out on the battlefield of these digital platforms. Some of these digital platforms are more powerful than any political party, which have been the guardian of power in the sense for a very, very long time. The same if it comes to governments to National Security, for example, so we know a lot about Bu nausea National Security and so forth but I think about one of the some of the biggest disasters like war cyberwar. We hear a lot about that and if I can turn off a country and a push of a button who needs bombs. So the biggest threats that humanity is facing its opportunities that have already been mentioning some of them right fought on this battlefield of computational social science, so we will have to really embrace a holistic view on the scientific method understand not not one specific discipline, but the scientific method in general. So in this sense, that's an introduction this specialization is an introduction to the scientific method to a system method the systematic way that knowledge can be created on the example of some of the one of the discipline with is a lot of potential during the years to come and that is the social sciences. And that starts with the data and I talked a lot about data and that's also we will start in the specialization but it does not stop here. It goes on to the analysis of the data. We will do a lot of machine learning artificial intelligence. And then we will also do simulations simulations of society of artificial societies to see what kind of future we would like to create. So the reach of computational social science is extremely vast and requires requires expertise and requires us to look at many different fields. So the third reason why I'm honestly excited about this course is what we were able to do with this specialization, since we are at the University of California. And the University of California of the advanced tertiary educational institution is the one the most comprehensive one in the world with all these ten campuses with the help of the California Office of the President. We went to all 10 UC campuses, UC Berkeley, UCSF, Davis, Santa Cruz, Santa Barbara, UCLA, UC San Diego, UC Irvine and Riverside and Merced and we got expertise from leading experts in all these cutting-edge fields. So you will learn from former specialist of all these fields directly in this course, and we are all learning. We will all learning and I have been I'm learning and I'm learning a lot in this course thanks to the expertise that we were able together. So what is computational social science all about what will we do in this course? Well, we will use computation in order to do science, but they are absolutely no requisites required in order to get going with us. Absolutely no coding even so at the end of the specialization, you will be coding but there's also, no requisite from any discipline because there's so many disciplines that we charge we couldn't require any discipline to begin with. So come on board and we will all learn together, we will start with learning about empirical approaches. So empirical science has to do with observing reality, right? So what we do has to do something with reality, that's the difference to art for example where you know in art not necessarily we can have fiction for example here. No, we don't we don't do our we do science. So they're arts and sciences, right? So empirical is very important for science. We work with data and in computational social science that's often do with big data. That's not a very lucky term and we will have to talk a lot about that. You can use the word digital footprint, digital trace data, the stuff that society leaves behind why we do what we do all day long through digital mediated platform. So that's what we start with and then second we will take this data and we will analyze it analytical so we will also use machines in order to analyze because this information processor here is often helplessly overwhelmed when confronted with the amount of data that we can extract from digital platforms. So we will talk about it official intelligence machine learning. You will do natural language processing and extract some meaning from these digital footprints that we find online and second and the second analysis application that we will do is we will study social networks. Social has to do with network. So it's a natural network. So society is a network of people. So it's useful to understand something about networks and we will spend a significant amount of time also studying networks because especially one of the biggest contribution that came with this digital trade status that we could finally also see the connections between people. That were tacitly always existing but now we have them recorded so we could make a science out of it literally. So we will talk about social network analysis as second application of analysis and third well science can also be theoretical you don't have to be afraid. We're not going to take out our differential equations. It's going to be well as much fun as playing a video game. So we create societies that exist in theory not in practice. We grow artificial societies. We do computer simulations and at the end of the course, you will grow your own computers at the end of the specialization rather. You will grow your own computer simulated artificial societies you will be able to study them study their behavior kind of like in a laboratory the laboratory is your simulated reality and we will do all of that on hand of this framework that basically represents the scientific method in this sense. This specialization is also an introduction to the scientific method. So let's become a little bit more concrete the specialization consists of five parts the first Is course one you going to be give runs through on computational social science. So we start with our general concepts about how systematic knowledge creation works in the social sciences. And what are the changes and what are the challenges then we go into big data. We have our first guest lecture here Professor Blumenstock from UC Berkeley. He will tell us how he uses digital trace data in order to reduce poverty in order to make the world a better place. Then we continue also as an overview with machine learning and Professor Shelton from UC Riverside will tell us will introduce us to machine learning and what are some of the tricks then Professor Fowler from UC, San Diego will introduce us into social networks and their importance in the social sciences in society in general and finally Professor small Dino from UC. Ed will introduce us to theoretical model. So this is kind of like the first course is a tour de force around the field of computational social science and you will quickly dip into each one of them kind of like paint the big picture but it will be very coarse-grained but you will already do some hands-on work and it will start right in this first course already with getting our own data. So you yourself in a few hours would already be web scraping the life internet. And getting data from the live Internet getting your very own personal database and you will see how easy it actually is it have 20 30 minute tutorial. You can learn how to do that and you can collect all the data you want from the internet into basically, but some kind of spreadsheet and you will be able to do that. So no requisites required for that. We will also start to play with some machine learning techniques and do I get a feel for what machine learning this artificial intelligence. That's so important how it is. What are they? She is and what it does then in the second course, we will dive deeper into big data artificial intelligence and ethics with is also very important. So we start with a big data the big data that we collected before we talk a little bit more about what are the benefits and the limitations of working with digital Trace data and then our Hands-On lab and throughout this course, they're always very very concrete Hands-On Labs that you work with. We will do natural language language processing. So the data that you that you gather for example on the internet, you can then put into an artificial intelligence and ask the artificial intelligence. What are the emotions Behind these words that I collected? What is the personality of the person that has written that and artificial intelligence is incredibly good. It's kind of like creepy at some points how good it actually is in revealing these kind of hidden patterns behind. This digital Trace data. So we do Hands-On analysis. And in the same second course of the specialization then we already will have to talk about research ethics because as you can already kind of like see you do it everyone what I'm saying? Right? That's very important that if you do science, we do it from an ethical perspective. So at the last part of the second course, we will talk about that. Then we come to course 3 here. This is dedicated to social networks because they are so important in body. That's what Society is right. It is a network and we will talk about static networks at the beginning and we will then also analyze static Network. So you will have your own network. The one that used you scraped from the internet, you will use a social network analysis software. Then we introduce you to the software and you can display this network and analyze this network understand a little bit. Well, how can you then analyze not only humans but society which is a network. What are the characteristic? What's the structure? What are the social? The structure and understand what then will become these emergent phenomena that we call social systems and we will end this third course with network Dynamics. And here we go already in the modeling perspective. So we don't only describe the data if you don't only analyze the data view model the data now and we move on to the theoretical part of it. So this course has five modules and at the end of the course, we study Network simulations. Then in our fourth course, we bring this further on the simulation story which is which is important because the one we don't want to live in the world as it is right now and if you agree reality, we want a better world right a world without pollution without poverty without contamination without without Wars So in theory they do exist. So, let's create some artificial societies and we do computer simulations and you will be able to talk a lot about what they Stuff because they are very tricky to do but they are as fun as playing a computer game and you at the end of course for will study some of these some of these artificial societies the stylized one the prototypical ones and then you will also create start to create your own ones and that's very easy code it as easy as as easy as us intuitive is writing English language. So with a software here, we will then model model some some some artificial artificial societies. T's in our fifth part of a fifth module of this specialization we will then do it altogether be run through all of it. So you again start with web scraping the internet getting the data you will then analyze the data with machine learning make meaning out of the data create a social network analyze the social network and then do a theoretical computer simulation coded up yourself to see what where could we go from here? How Society has right now, where could we go from here to make the world a better place? And as I said, absolutely no requisites at the end of this specialization, that's what you will be doing and it will be a lot of fun. So during the Centre to come much will have to be discovered about Society. We understand very little and many people argue that the first century of science we gave a really big push to understand physics to understand that say the dead matter around us during the second century of He has many people argue. We gave a big push to the living organisms biology and medicine and 60% of all scientific Publications. Nowadays are in biology or medicine every made big advances. And so these people argue that the next Century will be dominated by understanding Society better because now we finally have the data about society and once we start to study society as complex as it might seem Seem we discover. They're often quite simple rules at the bottom and that it's quite predictive and we can understand a lot of things about Society. So there's a lot of work to be done and if these people are right with this argument or not, I don't know but I know there's a lot of work to do and we need all that when we can what we can get and you happy very happy to have you on board with all these colleagues together. He also from the University of California. In this course, we all learn together in this course and as I already said independently of what we understand or might not understand what I can assure you is that it truly will be a lot of fun.