Following our last blog post where we discussed about the Fundamental Algorithms for Data Science (Part I), we covered regression, clustering and decision trees. Without further ado, here’re 3 additional key algorithms that you should be well aware of prior to embarking on your Data Science career!
Data visualisation algorithms are being used almost every software and even video games you interact with on a day-to-day basis! This includes your phone’s batter bar, a speedometer and the number of lives you’re left with in your video games. The primary reason why they’re adopted is because they provide a more intuitive, user-friendly visual representation of data. There is a wide range of techniques and algorithms used to represent data in a visual way. Although you might not want to hear this, but these are often done so using Mathematics concepts such as 2D/3D coordinates and trigonometry.
Don’t get this confused with the K-Means Clustering. This algorithm can be applied to both classification and regression problems. Hence, this also means that it is more widely used by Data Scientists and other programming professionals to solve classification problem in the Data Science industry. It is a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbours. The case is then assigned to the class with which it has the most in common. A distance function performs this measurement. The K-Nearest Neighbour is pretty much what you’d do in the real setting too. For example this COVID-19 contact tracing exercises, if you want to find information about who he/she may have potentially infected, it makes sense to start your investigation from friends and colleagues who he/she has just met with and the places he/she went. It is just that simple!
Principal Component Analysis (PCA)
This is one of the basic machine learning algorithms where it is mostly used as a tool in exploratory data analysis and for making predictive models. The PCA is often used to visualise genetic distance and relatedness between populations. Not only does it allow you to reduce the dimension of the data, it does so without compromising on the amount of information. It is found used in multiple areas, such as object recognition, computer vision, data compression, etc.
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