Data Science and Analytics

Thanks to advancement of sensing, computation and communication technologies, data are generated and collected at unprecedented scale and speed. Virtually every aspect of many businesses is now open to data collection; operations, manufacturing, supply chain management, customer behavior, marketing, workflow procedures and so on. This broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from data and data-driven decision making. Data Science is the science and art of using computational methods to identify and discover influential patterns in data. The goal of Data Science is to gain insight from data and often to affect decisions to make them more reliable [1]. Data is necessarily a measure of historic information so, by definition, Data Science examines historic data. However, the data in Data Science can be collected a few years or a few milliseconds ago, continuously or in a one off process. Therefore, Data Science procedure can be based on real-time or near real-time data collection. The term Data Science arose in large part due to the advancements in computational methods; especially new or improved methods in machine learning, artificial intelligence and pattern recognition. In addition, due to increasing the computational capacities through cloud computing and distributed computational models, use of data for extracting useful information even in large volume is more