The data science and big data in real life [part 1] Data Science is the science that studies methods of recording, storing, and analyzing data to effectively extract useful information. There is one big difference between data and information. Data does not relevant meaning and can not be understood. On the other hand, information is the organization of the data with the intention of passing some meaning. So, the goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. This term is relatively new that why we can find some confusions about what this does. However, the main process can be described by: Business understanding: First of all, the flow of the business and all its characteristics need to be known. Data understanding: We need to understand all the details about every data, for example, type and range Data Preparation: After we have understood the data, we need to prepare this data, separate similar data, correct the wrong data. Modelling: with the data organized, we need to “design” what we will do with these and how we will transform the data into information. Evaluation: Before we develop the system, we need to analyze the model and evaluate if this is good or not. Deployment. If the model is good, we will deploy it. If it is not good, we need to do some steps again. One important concept in data science is Big Data, that means a big set of data which traditional systems can´t handle yet.