Data mining is meant to assist organizations in analyzing large data sets to make valid predictions to increase revenues, reduce costs / risks, enhance customer relationships and much more. This page will provide you with all necessary resources such as flagship articles, examples, steps, courses, tutorials, jobs and much more. We’ll keep adding more content to this page, so please check often. We also would like to hear from you on what other material you might want to learn so we can keep updating as per your needs.
Data Mining – What, Why, Who, Where, When?
This article will attempt to demystify the topic by asking simple questions: What, Why, Who, When, Where, How
What is Data Mining?
Simply put, it is the process of sifting through large data sets to identify and describe patterns, discover and establish relationships with an intent to predict future trends based on those patterns and relationships.
Why is data mining relevant now? Haven’t we been ‘mining’ data from time immemorial?
Yes and No. It is true that data was always analyzed to identify patterns and predict outcomes, the data that organizations had to deal with exploded in recent times with the advent of big data. As these large data sets make it almost impossible to identify those multi-dimensional patterns using traditional techniques or tools, data mining in its modern form, with the advent of latest tools and faster processing, automates the discovery of patterns, establishing relationships, and putting together predictive models thus making it efficient.
Data Mining Examples
Data mining is becoming pervasive across many sectors and examples can be found in many industries and across many functions. Some examples are listed below in: Telecommunications, Retail, Credit card companies, E-commerce, Human resources, educational institutions, crime agencies.
Telecom service Providers
Phone and telecom providers attempt to predict ‘churn’ i.e. customers switching their providers by mining through web interactions, customer service calls, billing information etc. to give a probability score and targeting coupons/incentives based on their probabilities.
Companies in retail industry segment customers by their ‘Recency, Frequency, Monetary’ purchases into RFM groups and target coupons and incentives based on RFM attributes. For example, recent customers spending less but frequently may be receiving loyalty, upsell and cross-sell offers whereas customers shopping infrequently but large amounts may receive incentives to shop more.
Data Mining Courses
There are many courses available on data mining. Luckily, some of these courses are free so you don’t have to pay anything to get started. We are listing our top 3 favorites below but the link below will take you to a much more exhaustive list of courses.
Data Mining Tutorials
In addition to courses, there are plenty of tutorials available on Data Mining. Some of these can be downloaded via PDF for a fee and others are free. We are listing the top 3 below and please visit the link for the rest.
Data Mining Jobs
There are plenty of data mining jobs available. Below are the top 3 jobs and click the link for remaining jobs.