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.
Are you wondering about the hype around Data Science? Data says that it’s not a hype anymore. Glassdoor released its Report of 50 Best jobs in America in 2017. With a job score of 4.8 out of 5, a job satisfaction score of 4.4 out of 5, and a median base salary of $110,000, Data Scientist jobs came in first for the second year in a row.
IBM predicts demand for Data Scientists will soar 28% by 2020.