This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.
Data mining is the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. In this article, I want to share the 10 mining techniques that I believe any data scientists should learn to be more effective while handling big datasets.