Skip to 0 minutes and 7 secondsWelcome to the course. My name's Suzy Moat. I'm an Assistant Professor of Behavioural Science here at Warwick Business School. And my name is Tobias Preis. I'm an Associate Professor of Behavioural Science and Finance also here at Warwick Business School. Now, we've put this course together because something's really changed in the last few years. Increasingly, we rely on large network systems and smart cards to support our everyday activities. And nearly everything we do is now generating data, from buying bread at the supermarket, from taking a ride on the tube, to just calling your friend on your phone for a chat.
Skip to 0 minutes and 47 secondsNow, over the next nine weeks, we're going to talk to you about how people are starting to use these large new data sets to better measure what people are doing in the world right now and even possibly forecast what they're going to do in the future. Let's look at one example so that you know what we're talking about. Let's look at the following map. This world map was generated by retrieving the locations of photos taken by users all over the world. These users uploaded these photos to Flickr, one of the photo sharing websites used worldwide. And we retrieved these locations and plotted for every single photo one black pixel on this white map.
Skip to 1 minute and 40 secondsAs you can see, very naturally, the shapes of the continents are emerging, visualising the global coverage of this new type of data set. At the same time, you might realise that there are differences throughout the world. So obviously, there is a certain bias towards technological countries, so the western world is slightly over represented than, for example, parts in Africa. At the same time, given that these data sets can be retrieved from an online platform, these data sets are extremely cheap to handle. They are naturally occurring because people are taking photos where they are at the moment in time. They are interested in something.
Skip to 2 minutes and 34 secondsThis gives us the opportunity to study human behaviour on a worldwide scale in a very, very cheap way, and it allows us to get this data as soon as it is generated, so the process is very fast. If we compare this to traditional methods in the social sciences research area, then we have to say that this might complement these methods, which are mainly consisting of laboratory based experiments and online or offline surveys. These have the advantage that you have the control about your participants. You can ask specific questions, and you basically get the attention of all the experiment participants.
Skip to 3 minutes and 27 secondsBut at the same time, you're very limited in the number of participants, so the global scale of these new data sources might complement these existing sources. This is one example, and over the coming weeks, we will show you many more of these. So in our research group and research groups around the world, across business and across policy, people have been looking at how we can use these new data sets to make better decisions about what we do and make better decisions about how we allocate resources. Over the next few weeks, we're going to be showing you examples of how people use these data sets across areas as diverse as economics, health, happiness, and crime.
Skip to 4 minutes and 20 secondsWe'll even give you some insights into the tools that people use. Now, we're not assuming that you've got any previous experience with programming, but we'll show you that, with free tools, you can also find out what people are looking for on Google, for example, or what people are looking for on Wikipedia. And we'll even show you how you could use this data in your own analysis or perhaps even visualise the data that you've found. We hope you enjoy the course.
Welcome to the course
Welcome to our course, Big Data: Measuring and Predicting Human Behaviour.
You can access the course on any desktop, tablet or smartphone, but we recommend using a larger screen when engaging in the comments and discussions.
The course is broken down into nine weeks: eight weeks of study with a break for reflection in Week 5. Each week contains a sequence of individual steps for you to complete. You will be learning by watching videos as well as reading materials and articles, taking part in discussion activities, and a weekly quiz.
What will you learn?
We increasingly rely on networked computer systems and smart cards to support our everyday activities, and everything we do generates data – whether buying bread at the supermarket, taking a ride on public transport, or calling a friend for a chat.
This data is opening up a new era for our understanding of human behaviour – and also for policy making and business processes which depend upon this understanding. Research has shown how data can give us insight into the risk of an upcoming stock market crash; decrease delays in measuring the spread of illness; or even allow us to predict where crimes might occur.
This course will help you understand and unlock the power of these new datasets. You will gain an overview of the state of the art in big data research across a range of domains, including economics, crime and health.
You will also acquire some basic practical skills for data science using, learning to write basic programs in R, create basic data visualisations and carry out simple analyses. By the end of the course, you will be able to find out and analyse what people have been looking for on Google and Wikipedia.
Each week there will be videos to watch and, if you’re interested, articles to read. There will be opportunities to engage your fellow learners in the discussion activities, where we will provide a topic for investigation or discussion, and the ability to interact through the discussion.
At the ‘half-way’ point in the course we will pause and give you time to reflect on what you have heard and read so far. We will also give you the opportunity to join us in a Twitter chat where you can ask Suzy and Tobias questions about the course and Big Data in general.
Who will deliver the course?
In this course you will see both Suzy Moat and Tobias Preis on screen each week, along with invited colleagues for the different areas of activity we have highlighted in the course. Accompanying Suzy and Tobias will be Chanuki Seresinhe who will help with the comments and discussions you have and collate the themes of these together for a ‘round-up’ video we will record each week and post for you to watch.
We recommend that you click through to their profiles and follow them, which makes it easier for you to keep track of their comments on each step and on your activity feed.
Join in the conversation
We encourage you to discuss your interests and knowledge with other learners throughout the course. You’re welcome to post comments and share your work outside of the course, too - don’t forget to use the hashtag #FLbigdata or tweet our @BigDataMOOC Twitter account in your activity on social media.
Before you get started
We would love to know a little more about your background and how you plan to study the course, whether you plan to study every week or dip in and out. Your feedback is anonymous but will have massive value to us in improving what we deliver. If you haven’t already done so, please fill out our start-of-course survey.
When you’re ready, click the pink ‘Mark as complete’ button below, and then click Next to move on…
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