Skip to 0 minutes and 4 seconds 15-40. “Well, she came here to defend her title, but is now serving to stay in the championship.”
Skip to 0 minutes and 20 seconds What went wrong here? Was it the conditions on the day? A lack of matchplay beforehand? Or a change in nutrition? These days we can use data to help answer these questions. It has been said that data is the new oil, and never before have we had such a rich abundance of data. It comes from numerous sources and takes many different forms. However, like oil, data in its unrefined form is of little use. The challenge is to gain useful insight from this increasing volume, velocity and variety of data. This is where Data Science comes in. Data Scientists are problem solvers. They ask the right questions, and refine the data to extract something useful.
Skip to 1 minute and 6 seconds With practical experience from their chosen field, they can bring new perspectives and insights. In the sporting world, for instance, performance data is used to develop equipment and improve techniques - to put our sporting champions at the top of their game. Take the Wimbledon lawn tennis championships. In the run up to the event, players use data science to prepare themselves. Studying past performances to reveal trends and patterns that might not be obvious. Identifying weaknesses in their game, and modelling and testing new training methods in order to improve their chances. During the tournament, thousands of points are played. Every serve, every volley, every ground stroke is meticulously recorded and processed by a large team of data scientists.
Skip to 2 minutes and 3 seconds Insights are derived, results returned, and infographics generated to explain every nuance of a match. Crucially, the data science team includes regional level tennis players who bring their own wealth of experience to complement and enhance the technology. “Just this serve for the championship.” Data Science emerged from technology disciplines but is primed to transform many different sectors, from telecommunications, logistics and retail, to healthcare and environmental protection. Data is a force for change and Data Scientists are in high demand. How will you apply your experience? What questions can you ask of your data? What fresh spin will you put on it to give your team the edge?
Welcome to the course
Welcome to our preparatory course for the MSc in Data Science and Artificial Intelligence.
This course will help you assess your understanding of data science and learn the basics involved. It will also help you to make an informed decision around choosing a Master’s in Data Science and Artificial Intelligence.
This week, we will explore:
- What it means to be a data scientist
- The importance of asking questions in data science
- How data is physically stored in a computer system
- The first steps in designing a program and writing Python code
Introduce yourself and describe why you are interested in data science.
Meet the team
Your lead educator is Mark Johnston, assistant professor in the School of Computing, Electronics and Mathematics at Coventry University.
You can follow Mark and see all the comments that he makes via his FutureLearn profile page.
This course is a collaborative effort between various institutions. The contributors are:
Paul Matthews, senior lecturer in information and data science in the Department of Computer Science and Creative Technologies at UWE Bristol.
Daniel McCluskey, research assistant for the Creative Computing school at the University of Gloucestershire.
William Sayers, senior lecturer and researcher in artificial intelligence techniques, at the University of Gloucestershire.
Michael Tautschnig, lecturer at Queen Mary University of London and senior security engineer at Amazon Web Services.
James Davenport, professor of information technology at the University of Bath.