SIZ Education | DATA MINING Complete Guidance

Hi everyone it's Sheham again. In this post I'll be discussing one of vast domain in the world of Computer Science, that is Data Mining. So let's mine deeper into it.

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What is Data Mining

Data mining is a process that involves methods from machine learning, statistics, and database systems to extract and discover patterns in large data sets. Data mining is an interdisciplinary subfield of computer science and statistics with the overarching goal of extracting information (via intelligent methods) from a data set and transforming it into a comprehensible structure for further use. It is an analytic process designed to explore data. Companies use data mining to transform raw data into useful information.

Types of Data Mining Techniques

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Data Mining Techniques are of various type. Some important techniques are are discussed below:

Regression
It involves predicting, also known as predictive power. We want to estimate the value of a given feature based on the values of other features in the data using regression analysis, which assumes a linear or nonlinear model of dependency.
Example: A house may be predicted to sell for a specific dollar amount, such as $100,000 to $200,000. A regression problem necessitates the estimation of a quantity.

Classification
Classification is another critical task to complete before diving into the hardcore modelling phase of your analysis.
Example: If patients are grouped based on their known medical data and treatment outcomes, this is referred to as classification.

Clustering
Clustering is a useful technique for determining object groupings so that objects in the same cluster are similar to each other but objects in different groups are not.
Example: Clustering can help businesses to manage their data better.

Applications of Data Mining

Here are some common applications of Data Mining

Fraud Detection
Customer Segmentation
Intrusion Detection
Future Healthcare

Skills you need to learn for Data Mining

It basically includes almost all the concepts of Artificial Intelligence and Maching Learning, with the additon of some other data analysis tools and core concepts .

Languages to learn: Python, Jave, R, Perl

Data Analysis Tools like: SQL, SAS, NoSQL, Hadoop, MATLAB

Textbooks

Data Mining and Analysis: Fundamental Concepts and Algorithms (by Mohammed Zaki and Wagner Meira Jr)
Data Mining: Practical Machine Learning Tools and Techniques (by Ian Witten, Eibe Frank, and Mark Hall)
Mining of Massive Datasets Book (by A. Rajaraman, J. Ullman)

So that's it for this post. Hope you like it
Thanks for reading
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