Data Science vs Data Analytics

  Data Science is the interdisciplinary field of study that uses scientific methods, processes and algorithms for the extraction of knowledge. It also provides a deep understanding of structured and unstructured data and its application in different domains. Data science comprises scientific thinking, statistical methods, computer engineering and innovative technologies, an intersection between machine learning translational research and software development. Those disciplines themselves, combine areas such as computer science and mathematics. In simplified terms, data science is the deep understanding of the available raw data. It is currently a lucrative field with an abundance of job positions, rapidly growing and emerging. It is said that 90% of the world’s data was developed in the last 2 years. Data Science applies to various domains such as banking and finance. Different technologies, techniques and concepts are used for its application like linear and logistic regression, clustering, machine learning etc. Data needs to be captured, communicated, maintained, processed and analysed. There is a lot of data in the world but without science behind it, it is useless. For example, data is important in improving customer experiences in businesses. In companies and industries, data science helps in improving customer experiences, increasing efficiency and company planning. These insights are performed by the data scientists of the organisation. There is a high scope of growth in this field. Some tools used by data scientists include SQL. Tableau, excel etc. Data analytics involves analysing raw data to make conclusions about the given information. It uses various automated processes such as mechanical processes and algorithms to increase the efficiency of a business. In contrast to data science, data analytics is more specific and concentrated than the former. There are other differences as well. Data science mostly deals with machine learning whereas data analytics mostly uses statistics and historical data to make predictions. We can say that data analytics is a subset of data science that studies a particular section in-depth while data science covers broad domains. In data analytics, the study is performed by a data analyst. Data analysts mostly collect data and identify trends while data scientists generally design data and automated systems to design frameworks. In terms of the tools they use, data analysts use statistical software such as excel while data scientists use Python, Java and machine learning to analyse the information.

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