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.
Text Analytics, also known as Text Mining, is a technique used to derive insights from text data. The field has picked up some traction in review and customer analysis, and provides businesses with deeper insights regarding their customers. Everyone who has used an e-commerce site understands how consumer reviews, or lack thereof, can have a direct impact on new consumers, and thus, the business.
Communication is of paramount importance in every aspect of our lives. Data science is no exception. However, the additional complexity that Data Scientists live with is that they are required to closely interact with diverse groups of people even within a single project lifecycle. To put things into perspective, see the diagram below that captures the project lifecycle of a typical analytics project as per the internationally accepted CRISP-DM framework, and some examples of the various type of people involved at each stage.