Data Analytics for Beginners

Data Analytics for Beginners, it's quite a buzz these days. This article is meant for people who are yet to take their first step towards data analytics. Now a days some of the common terms that are being used very frequently these days. Especially if you are a part of the corporate world. If you are looking for say
different job portals. All you would know that these are the terms which are commonly and more frequently used these days.

• Business Analytics
• Business Intelligence
• Data Analytics
• Data Science

It's not important for us to get into a debate as to how these vary. But for a starter there is a good overlap between all of these. There is a starting point when you develop your career in a particular direction. You can become more specific about what you want to pursue but just for a quick introduction sake let me tell you.

Introduction of Data Analytics for Beginners:

Business Analytics:

Business Analytics is the use of analytics focus to business that's for decision making primarily for managers or stakeholders in the business.

Data Analytics:

Data Analytics is an overarching term which you can say supersedes the scenario of business and goes into the field of healthcare. But could go into a customer research could go into education. Data analytics is an overall term.

Business Analytics:

Business Analytics can be a subset of its business intelligence would be a mix of analytics. A lot of analysis which might come from your understanding of the processes. The way your processes flow so this is more to do with analytics plus your subject matter expertise.

Data Science:

I would say the vastest term among all thing is and it's very popular these days. This is a mix of multiple skills. We'll talk about that as we move one later parallel to these terms.

Big Data, there is another term which is quite popular and as is being used very frequently by people it is big data so what is a big data. Any data that's beyond your systems capacity to process could be a big data for your system.

When we talk about big data there are certain weeds which are important. At very high speed data generates in volumes. Data are store in large volume. Variety of data are generate from multiple sources. The data may not be in a given specific file format or structure. All the time it could be from different number of resources. There is something to do with the veracity. You can refer to the accuracy of the data. Big data is more technical to capture.

I would say technical owing to the aspects associated with its storage. The processing speed so the techniques the underlying techniques to work on the big data and approach from the analytic side of it would be the same. But there are certain technical elements get associated what's the burden.

For Example:

Gartner says that in 2018, data and analytics can't be ignored. Analytics will drive major innovation and disrupt established business models in the coming years. Technical professionals need to adapt their data and analytics architecture from end to end to meet the demand of analytics everywhere.

McKinsey says, there will be a shortage of talent necessary for organizations to take advantage of big data by 2018. The U.S alone could face a shortage of 140,000 to 190,000 people with deep analytic skills as well as 1.5 million managers and analysts with the know how to use the analysis of big data to make effective decisions.

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