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Course Content


Exploring and analysing enormous chunks of data to find relevant patterns and trends is what data mining is all about. It can be used for a variety of purposes, including database marketing, credit risk management, fraud detection, spam email screening, and even determining user attitude.

Data science is a multidisciplinary strategy that combines analytical methodologies, subject expertise, and technology to uncover, extract, and surface patterns in data. Data mining, forecasting, machine learning, predictive analytics, statistics, and text analytics are all examples of this method.

Data Analytics is aimed to reveal the particular of extracted insights, whereas Data Science focuses on uncovering significant correlations between vast datasets. To put it another way, Data Analytics is a subset of Data Science that focuses on more detailed solutions to the issues that Data Science raises.

Some of the common tools that are used for data mining are:

  • Data visualization tools: These tools help users to understand the information that they have gathered through data mining. They use visuals to represent complex datasets and make them easy to digest.
  • Data analytics tools: These tools help organizations to gather insights from their data by using predictive modeling, machine learning, and artificial intelligence.
  • Data preparation software: This software are used for cleaning raw data before it can be analyzed by other tools in the toolkit.

Text mining, also known as information data mining, is the act of converting unstructured text into a structured format in order to uncover new insights and patterns.

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