Data Science - Explained
What is Data Science?
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What is Data Science?
Data science is defined as a collection of processes, concepts, techniques and tools that is directed towards getting useful information from a variety of complex data. Data science focuses on retrieving useful or relevant information and insights from existing data. The data is often complex in nature, it might be structured, unstructured or a combination of both categories. Data science also integrates statistical methods and computation that will enhance the collection and interpretation of data for the purpose of decision making.
How is Data Science Used?
The data analyzed and interpreted using data science are complex data. This is because these data are assembled from different sources, the data can also be structured and unstructured. Analysts draw data from different places using different techniques. For example, data can be collected or drawn from the internet, social media platforms, cell phones, and even through surveys and questionnaires. Data science is used in Big Data, a new field of study. It focuses on analyzing a pool of data from diverging sources to obtain useful information that can influence decision making. In recent times, companies collaborate and combine their technological initiatives to facilitate data science. Especially with the increase in the number of unstructured data that can impede decision making. Data science was first mentioned in 1960, it was at this time that it was used as an alternative phrase for 'computer science'. However, after 15 years, data science was used as a term to define methods of data processing that are used in different business sectors and for different applications. Data science became an independent discipline as at 2001. Also, in 2012, the Harvard Business Review popularized the term by calling it a lucrative job in the 21st century. Tools, processes and techniques from various industries or disciplines are used by data science to extract useful insights from a large base of complex data. The insights and relevant information extracted using data science techniques are used for decision making. Tools from disciplines such as analytics, statistics, mining, machine learning and programming are used in data science. Tools from each of these disciplines sum up to what we know as data science techniques. Data science uses algorithms to discover trends and identify relevant information from the complex database. There are other important disciplines that data science draws tools and knowledge from, examples are data engineering and data architecture. A professional in the field of data science is called a data scientist. Such an individual is an expert when it comes to analyzing and interpreting a large number of complex data for the purpose of extracting useful results or information. Data scientists are knowledgeable in the field of analytics, programming, machine learning, computation, and statistics. They also require additional knowledge from data administration, architecture and engineering. Generally, data scientists analyze big data or complex data with the aim of identifying trends and patterns that will be useful in decision making. Many industries, companies and businesses and optimized their performance become more effective and efficient through the information provided by data scientists. Data science has played a major role in helping business identify the key needs of their customers and how they can meet these needs effectively. Through data science, companies are able to address the needs of customers competently and efficiently. Financial institutions also detect fraud actions through the information they derive from data science. Effective managerial decisions that are customers-minded are also birthed through the useful information provided by data science.