For data-driven companies like Spotify or Netflix, probability can help predict what kind of music you might like to listen to or what film you might enjoy watching next.Īside from our preferences in entertainment, research has recently been focused on the ability to predict seemingly unpredictable events such as a pandemic, an earthquake, or an asteroid strike.
A common use of probability in predictive modelling is forecasting the weather, a practice which has been refined since it first arose in the 19 th century. Being able to predict the likelihood of something happening is important in numerous scenarios, from understanding how a self-driving car should react in a collision to recognising the signs of an upcoming stock market crash. When analysing data, probability is one of the most used statistical testing criteria. Statistics contribute to technologies like data mining, speech recognition, vision and image analysis, data compression, artificial intelligence, and network and traffic modelling. However, a foundational understanding of some basics in statistics supports strategy in exercises like hypothesis testing. Machine learning takes out a lot of the statistical methodology that statisticians would usually use. Add to this, 5.6 billion searches a day on Google alone and this means big data analytics is big business.Īlthough we may hear the phrase data analytics more than we hear reference to statistics nowadays, for data scientists, data analysis is underpinned by knowledge of statistical methods.
Our everyday usage of the internet and apps across our phones, laptops, and fitness trackers has created an explosion of information that can be grouped into data sets and offer insights through statistical analysis. It’s considered a mathematical science and it involves the collecting, organising, and analysing of data with the intent of deriving meaning, which can then be actioned.
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