Hello and thank you for signing up to this course on "Introduction to statistical thinking and data analysis in R for public health". I'm Alex, I'm a senior academic statistician at Imperial College London and I'll be your guide for this course. I'm now going to cover what the course is about, who it's aimed at, what learning materials you use and most importantly what I hope you'll get from the course. First of all - why should anyone want to devote a chunk of their time to learning about statistics when there were all those cat videos waiting to be watched? Well, statistics are everywhere; the probability it rained today, trends over time and unemployment rates, the odds that India will win the next Cricket World Cup. It's sports like football they started out as a bit of fun but they've grown into big business. In this course, we're going to take a medical angle with examples coming from medical research and public health in particular. I'll first get you to think about how health research is reported in the news and other media and whether you need to dig a bit deeper into the methods used in that research. You'll take a peek up what medical research is and how, and indeed why, you turn a vague notion into a scientifically testable hypothesis. You'll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Now some of those ideas are massive. Uncertainty and variation are features of the human condition. Later on, you'll get your hands dirty with analysing a data set exploring how what people eat affects their risk of cancer. You're going to get to grips with one of the most widely used and versatile free software packages around called R. It's used at a lot of universities and businesses so R programming is a useful skill to have. There's an expression in Spanish "Aburrirse como una ostra" - "to get as bored as an oyster". Well, I don't want that happening to you during this course. I'm hoping that above all you'll enjoy it. So, what will you get from it once you reach the end? I hope you'll see the value that that statistical thinking has in your work and indeed your life. For me, it's more than a set of skills; it's a mindset. I want you to grasp some of its fundamental ideas, begin to see how science works in the medical field and be able to do some simple analyses using R software. In this way, you'll be ready to build on this foundation and ready for the follow-on courses on the topics of regression and survival analysis. Even if your job is more about reading reports that contain data analysis than doing the analysis yourself, statistical thinking skills will help you ask the right questions, challenge the interpretation of the data and not be misled by 3D colour pie charts that try to cover up poor quality information with pretty graphics. You'll also see why the advanced analytical methods and data science known as machine learning, like artificial neural networks and image recognition, should not be used without a solid grounding in statistical thinking. So how will you get there? This course consists of a set of videos, readings, quizzes, group discussions, R software exercises and knowledge reviews. Now statistics is often taught using mass of formulae full of Greek letters. But not this course. It's impossible to avoid formulae altogether and some of you will be comfortable with them. They're a neat way of expressing an idea and can help you understand that idea but every learner is different. I'll therefore give just those formulae that are really necessary and those that are in the readings can be skipped if they make you uncomfortable. I don't bother to remember formulae so I don't see why you should either. Formulae are for computers. The later courses in this series follow this principle too. You've taken the pre-course assessment and decided that you need a course that assumes that you're happy to start from the beginning in statistical terms. Being online, the course can be taken again at your own pace as everyone is different. You can review any video, you can review any text, retake any quiz and redo any exercise, all as many times as you like. None of your friends is going to know. So I hope that gives you an idea of what to expect during a course. Let's get started. The next video is called "what has statistics ever given us?" and we'll give you some examples of the use of statistics in public health and medical research. So don't be an oyster! Repeat after me: Statistics is fun!