GHEBTECH

  Data Science - Big Data - Data Analytics These three terms are often heard frequently in the industry, and although their meanings share some similarities, they have profound differences.  Unstructured, structured, and semi-structured data are all dealt with in data science. Data cleansing, data preparation, data analysis, and other procedures are included. Statistics, mathematics, programming, and problem-solving are all part of data science, as is creative data capture, the capacity to view things in new ways, and data cleansing, preparation, and alignment. This umbrella phrase refers to a variety of strategies for collecting information and insights from data. Big Data is a term that refers to massive amounts of data that can't be processed efficiently with today's apps. Big Data processing begins with raw, unaggregated data that is often too large to fit in a single computer's memory. Big data is a buzzword for massive amounts of unstructured and organized data that c...

  The world we live in is data-driven. In fact, the amount of digital data available is rapidly increasing, and transforming how we live. After Hadoop and other frameworks solved the storage challenge, the focus on data has switched to processing this massive volume of data. When it comes to data processing, the terms Data Science, Big Data, and Data Analytics come to mind, and there has always been a misunderstanding between them. When it comes to data processing, the terms Data Science, Big Data, and Data Analytics come to mind, and there has always been confusion between them. Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics: Data science VS BigData VS Data Analytics Data Science is a collection of tools, algorithms, and machine learning techniques aimed at uncovering hidden patterns in raw data. It also entails solving an issue in a variety of approaches to arrive at a solution, as well as designing and constructing new data modeling and produc...