Emerging technologies in recent years and major enhancements to Internet protocols and computing systems, have made the communication between different devices easier than ever before. According to various forecasts, around 5 25-50 billion devices are expected to be connected to the Internet by 2020. This has given rise to the newly developed concept of Internet of Things (IoT). IoT is a combination of embedded technologies regarding wired and wireless communications, sensor and actuator devices, and the physical objects connected to the Internet [1, 2]. One of the long-standing objectives of computing is to 10 simplify and enrich human activities and experiences (e.g., see the visions associated with “The Computer for the 21st Century” [۳] or “Computing for Human Experience” [۴]) IoT needs data to either represent better services to users or enhance IoT framework performance to accomplish this intelligently. In this manner, systems should be able to access raw data from different resources over 15 the network and analyze this information to extract knowledge. Since IoT will be among the greatest sources of new data, data science will make a great contribution to make IoT applications more intelligent. Data science is the combination of different fields of sciences that uses data mining, machine learning and other techniques to find patterns and new insights from 20 data. These techniques include a broad range of algorithms applicable in different domains. The process of applying data analytics methods to particular areas involves defining data types such as volume, variety, velocity; data models such as neural networks, classification, clustering methods and applying efficient algorithms that match with the data characteristics. By following our reviews, it 25 is deduced that: firstly, since data is generated from different sources with spe cific data types, it is important to adopt or develop algorithms that can handle the data characteristics, secondly, the great number of resources that generate data in real time are not without the problem of scale and velocity and thirdly, finding the best data model that fits the data is one of the most important 30 issues for pattern recognition and for better analysis of IoT data. These issues have opened a vast number of opportunities in expanding new developments. Big Data is defined as high-volume, high-velocity, and high variety data that demand cost-effective, innovative forms of information processing which enable enhanced insight, decision making, and process automation[5].