Overview of the short course with Dr Leonid Bogachev

Overview of the course

Dr Leonid Bogachev

Hello and welcome to our short course about the role of statistical models in data analysis. I hope it will be useful and interesting for you.  

This course has been designed for students who have not formally studied statistics. But even if you have, the course will still provide a valuable state-of-the-art revision that would strongly enhance your knowledge and skill.  

This short course it is titled, Statistical Models. But throughout we talk about statistics and data science together. Why? Well, quoting from American astronomer and writer Clifford Stoll,  

“Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.”  

Sometimes, this is also referred to as the DIKW Pyramid. Not aiming to talk about wisdom here, let’s agree that data is not yet information. Indeed, data is what we get in a sample of observed values, but our real interest is usually in a wider population from which this sample has been taken. Things are complicated further by the data variability and uncertainty. Therefore, to be able to make reliable conclusions based on the data, suitable tools are needed to quantify or measure uncertainty.  

This can be done using statistical models that are based on the concept of probability as a measure of chance. Statistical modelling is not straightforward. It requires training, insight and skill. Statistics is the art and science of learning from data. Without statistics, data science would amount to using some algorithmic black boxes for prediction, but not being able to explain or interpret the result. That is why a solid training in statistics is of great importance for becoming a good data scientist.  

Statistics cannot be separated from handling data, and this is where we need computers. Throughout the course, I will introduce you, in steps, to how you can use the R software for statistical data analysis. We will use it to do calculations and to draw graphs to summarise data. We will practise a lot to develop good computing skills.   

Your learning in this course will feature a variety of learning activities such as short videos, reading pieces to introduce concepts, ideas and techniques, and exercises to practise skills, including statistical computing. There will be quizzes to reinforce learning and to test your knowledge as it develops. You’ll also be set some tasks to perform so that you gain practical experience using the ideas introduced in the course.  

By completing this short course, you’ll master the basic concepts and tools of descriptive statistics, which will be your first important step towards becoming a good data scientist.  

We hope that you enjoy the course and log on to learn more.

Title: Dr

First Name: Leonid

Last Name: Bogachev

Role: Reader in Probability (Associate Professor), Department of Statistics, School of Mathematics

University of Leeds profile page: Dr Leonid Bogachev