With growing data, the tech jargon and terms associated with it also tend to grow, thus this makes most of the people ponder upon these terms and get the real meaning and distinction in various similar sounding terms.
Hence after reading this series of articles you’ll be able to get deep insights on various terms and how they differ from each other.
Today we will focus on the two most popular terms you tend to hear in this world of big data, i.e. Data Mining and Data Analytics. If you are an amateur in this industry you’ll get confused and think both of them are similar, believe me, the two things are as different as cheese and chalks.
So Let’s start with Data Mining first:
Data mining is a process to structure the raw data and formulate or recognise the various patterns in the data through the mathematical and computational algorithms, data mining helps to generate new information and unlock the various insights. The data is first placed into a data warehouse to do the required the required extraction of data to produce meaningful relationships and patterns. There is two type of data mining one is descriptive, which gives information about existing data of the organisation, while the other is predictive: which makes forecasts based on the data.
Data mining is a pattern discovery task against a pool of data; therefore it requires classical and advance components of artificial intelligence, pattern distribution and traditional statistics, the point to be noted that data mining is done without any preconceived hypothesis, hence the information that comes from the data is not to answer specific questions of the organisation.
Data mining also helps in exploring trends from the data.
We have seen may fortune 500 companies spending millions on data analytics, although we have also figured out that every other company working and providing analytics services which have label this industry.
Data analytics is the art of exploring the facts from the data with specific to answer specific questions, i.e. there is a test hypothesis framework for data analytics. The techniques used in analytics also are same as used in business analytics & business intelligence.
One needs various tools to get right data analytics, like data visualisation tool and need to know languages like Python or R to perform robust data analytics.
Hence concluding it we can see that the data analytics has it’s rooted from business analytics or business intelligence models while data mining uses more of scientific and mathematical techniques to come up with patterns and trends. Data mining is basically close with machine learning.
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