Big Data Analytics and Machine Learning have moved way beyond the boundary of buzzword terminology and are now common terms in the technology industry. Business is all about competition and if organisations need to stay ahead of it, new technologies have to be adopted. This is the reason you will find companies welcoming technologies like big data analytics and machine learning into their business functioning.
Both machine learning and big data analytics come under the umbrella of data science. Though they have a connection, there are still some unique identities that separate them in terms of definition and application. This article will help you to understand which field of expertise to choose Big Data Analytics or Machine Learning and how it will help you.
This article covers the following topics:
To explain briefly, big data is a term for a set of large amount of data which is too complex to be processed using traditional methods. Big data analytics refers to the activity of studying these large sets of data using specialised software and analytical tools developer specifically for that purpose. With big data analytics, organisations are able to examine untapped information from the huge amounts of structured or unstructured data it has in its data reserve.
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Machine learning is a science which deals with the creation of algorithms and programs which predicts results or take actions in order to optimise a system based upon the data that is constantly generated. This data can also be big data and through machine learning, programs or algorithms can be created which learn from the information present in the data to predict future possible patterns.
You will find both similarities and differences when you compare between big data analytics and machine learning. However, the major differences lie in their application.
- Big data analytics as the name suggest is the analysis of patterns or extraction of information from big data. So, in big data analytics, analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs but still produce desirable outputs.
- Most data analysis activities which do not involve expert task can be done through big data analytics without the involvement of machine learning. However, if the computational power required is beyond human expertise, then machine learning will be required.
- Normal big data analytics is all about cleaning and transforming data to extract information, which then can be fed to a machine learning system in order to enable further analysis or predict outcomes without the requirement of human involvement.
Also Read>> Top Big Data Interview Questions & Answers
Which one should you chose to learn?
Big data analytics and machine learning can go hand-in-hand and it would benefit a lot to learn both. Both fields offer good job opportunities as the demand is high for professionals across industries while there is a lack of skilled professionals; machine learning professionals being in more demand when compared with big data analysts. When it comes to salary, both profiles enjoy similar packages and if you have skills in both of them, you are a hot property in the field of analytics.
However, if you do not have the time to learn both, you can go for whichever you are interested in.