Markov chains are a way of stochastically modelling a series of events where the outcome probability of an event depends only only on the event that preceded it. This post gives an overview of some of the theory of Markov chains and gives a simple example implementation using python. Using Markov Chains to Model The… Read More »
Monto Carlo simulation is a technique for approximating future behaviour based on randomly sampled numbers. By sampling from different probability distributions it is possible to use Monte Carlo simulation for a range of different situations including physical systems, computer games or finance. This post gives a simple example of Monte Carlo simulation to give some… Read More »
Confidence intervals are the range often provided alongside other statistics. As their name suggests, they somehow convey a degree of uncertainty in the statistic quoted, but what actually are confidence intervals? And how can we get an intuitive understanding of them? This post goes into some of the theory behind confidence intervals and points you… Read More »
Data science can mean different things to different people, but we can try to define it by the techniques a data scientist tends to use. A recent online poll gave a top 10 of algorithms and methods used by Data Scientists. This post goes through that top 10 list of data science techniques to flesh… Read More »
From Statistics To Data Science If you have a background in statistics, you may be in a good position to capitalise on the current demand for data scientists. Data science jobs and careers are often well paid, and practioners are in high demand. One way to move from statistics to data science would be to… Read More »