In the era of big data, large quantities of data are being accumulated. The amount of data collected is said to double every nine months. Seeking knowledge from massive data is one of the most desired attributes of Data Mining. In general, there is a huge gap from the stored data to the knowledge that could be construed from the data. This transition will not occur automatically, that is where Data Mining comes into picture. In Exploratory Data Analysis, some initial knowledge is known about the data, but Data Mining could help in a more in‐depth knowledge about the data. Courses on Database systems give methods to extract information, but they fail to extract knowledge that is actionable. Manual data analysis has been around for some time now, but it creates a bottleneck for large data analysis. Fast developing computer science and engineering techniques and methodology generates new demands. Data mining techniques are now being applied to all kinds of domains, which are rich in data. Although data mining is partly based on statistical methods, data mining methods give a lot more than the statistical methods. Data mining methods are to a large extent based on machine learning methods. The difference is data mining is meant for huge data whereas machine learning is usually done over relatively small‐sized data. Huge data brings completely a new set of problems to be solved. This course aims to introduce you to the exciting and ever‐evolving world of data analytics and mining.
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