It is become a buzz as far as modern Information Technology era is concerned. It happens with ‘n’ numbers of advanced technologies that today’s individuals start talking about as the jargon having no understanding of what is meant by the modern technology, what falls in its scope & so on. We shall undertake such discussions in a bit of detail. The confusion starts the very moment you speak of data science as part of today’s technical scenario. When it comes with its various components. Whenever you speak about constituents of data science, you fundamentally speak about big data. This is when you also talk of numerous jobs that form part of Data Science and Machine Learning – what really is the Data Scientist’s role, what exactly is the Data Curator’s role, what exactly id the Data Librarian’s role & so forth. In today’s scenario when you speak of it as a field within itself, it essentially deals with large chunks of data.
Hadoop essentially refers to the big data & large quantities of the frameworks that are engaged to grapple with this large data. Moreover, there are ‘n’ numbers of frameworks that are existing & they happen to have their own pluses & minuses. Moreover, Hadoop is the most widespread & popular framework. Whenever we talk about data science, you speak about different kinds of analytics that you have operated on this large chunk of data – you really can’t escape Hadoop. Whenever you are undertaking statistical examination, you don’t need to worry about Hadoop and any of the framework for big data. However, Data Science happens to be a different animal. And, Hadoop is also developed in Java, so it will truly help if you to understand Java as well.
R is really a programming language for statistics. And, it remains one of the top languages for data science. You really can’t avoid R since when you speak of the different algorithms you have to apply over this large quantity of data in for you to be able to get to the insights of this data or in effect to enable certain machine learning algorithms over the top of it, you need to employ the services of R. this language has its root in data analysis, statistics, and data visualization. Nowadays, this language becomes the foremost choice of the new generation of analysts who are appreciated active open source community. And, the fact that the leading analysts rely on this language is that, it can download the software for free & the downloadable packages which are available to customize the tool.
- C & C++
These two languages have also been around for the decades. And, one can also mentioned these two in the machine learning job ads and it is one of the most popular languages of machine learning. There are various organizations that might be looking to add machine learning to their existing projects that are associated with these two languages and thus, they are looking for the expertise in this field. But, if you are looking for learning the first language, then these two are probably not such languages.
Nowadays, Python is one of the top languages to learn if you are exploring to boost your skills in the field of machine learning and data science. Python is the #1 choice among the data scientists and data analysts. And, this language certainly mentioned in numerous ads for analysts and data scientists. Thus, if you want to enhance your skills, Python is the foremost alternative.
In addition with all the above top languages for machine learning and data science, another burning thing that is being spoken about for quite a while now in the industry is the topic of Deep Learning. Deep learning in effect is a part of Machine Learning. The really powerful thing that Deep Learning gives us is due to its very highly accurate models that it can build and that combined with its capability to work with data of higher dimensions that was not feasible with the earlier models of machine learning. Even though you are enabled to solve a problem in data science with high dimensions using machine learning, the very accuracy was really not at acceptable levels. Deep learning has been changing this very problem for us.