One term that the IT industry is specially talking about for a few years now is big data. Considering the popularity it has received over the years, a lot of people are opting for the Data Science Training and Big Data classroom training programs. Before you too sign up for any of this, it is important here to understand the initial level basics of what big data is and how it is brought in use. This article here is going to start with defining the basics and then we will move towards the concepts that the big data comprises of.
Big data is a term used to define any kind of data that is not just huge in terms of its quantity but is completely complex and hard to formulate and understand as well. The main aim of big data is to comprehend theories and algorithms with the help of which data analysis becomes easy to understand and helps in taking decisions in a much better way. Since there are different work sectors that use different concepts of big data, it is important to understand that learning big data is not something that would only help the professionals working in the IT industry but instead it is something that would help you sort out your data and use it in much better way to diversify the kind of results you expect.
Just like the big data and its concepts are widely available, the areas of applications of big data are widely spread too. Some of the most common industries that fall under the categories of clients of big data are the institutes and organizations that provide financial services, the communication industry, the retail and fmcg market, digital areas where lead generation etc. is done and many more.
Just like data sciences, big data too involves a lot of math and statistical skills. Here, it is important that if you are learning big data tools or techniques, you should know about the basics of data analysis, basics of computer programming, statistics, mathematics, algorithm creation and application, etc.
Majorly when we talk about big data we are trying to figure out the ways through which you can process the data which is in millions of terabytes. This can be either processed data or unprocessed data and what we need to do here is prepare algorithms that can use this data and draw conclusions from it in the quickest way possible. Moreover, we also need to take care that while you do this, you use the maximum data you can.
There are a variety of languages and tools that have been developed to make this process easy. In addition to writing new algorithms as per you needs, you can use the codes and algorithms that are already available for use in the form of libraries and templates. This helps in reducing the work of an analyst as they can at least use some bit of what is already available.