Remember all that times you spent in school learning about statistics and numbers, wondering to yourself whether it would ever come to use in the future. While the outreach of mathematics touches every corner and echelon of our lives, it presents a much better opportunity if its language is molded to provide some context. For a generation that generates petabytes of data every moment resulting from transactions, surveys, online interactions and a lot more, it becomes a grueling task to paint a rosy picture from those numbers.
Programming languages are the foremost requirement for creating any software. Between Syntax and semantics, it is essential to understand the importance and nuances of each language to apply the right one for the right software. We will look at the most popular languages these days and see if they would be a good fit for data science or not. Each year brings new technology, business complexities and innovations that spur on a new set of languages and frameworks.
Machine Learning requires correct interpretation and understanding of the problem. Top issues that people face are converting the real-world problems into machine adaptive problem. Identifying which machine learning would do the trick is the key. For newbies, learning the basic approaches of machine learning is of top most importance - classification, regression, clustering or recommendation which will give a model to create a problem statement, features and labels.