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Data Science is one of the fastest-growing industries today, requires staying updated with the latest trends in technology, tools, industry trends, and job opportunities, to stay afloat as a data practitioner. In this content, we discuss data science definition, techniques and data preparation process including extraction, transformation and loading process. Also, different types of data structure and roles of Big data in data science will be discussed in detail. Data Science may be defined as a multidisciplinary blend of data inference, computer science, mathematic, stat algorithm development, and technology to solve complex data analysis issues. Data science involves in many processes including the area of domain identification, data representation, and the technique to extraction of meaningful information from data sources to be used for business purposes. Data engineers are responsible for setting up the database and storage to facilitate the process of data mining, data munging and other processes. Designing data science involves business aspects for the to solve business problems and create useful knowledge and business value from existing data within the organization. Thus, the management level requires some knowledge of ‘data-analytic thinking’ that helps to understand meaning of data and the necessary techniques such as data mining and machine learning during the analytic process.