Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.
Data Science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, operations research,information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining,database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing. Methods that scale to big data are of particular interest in data science, although the discipline is not generally considered to be restricted to such big data, and big data technologies are often focused on organizing and preprocessing the data instead of analysis. The development of machine learning has enhanced the growth and importance of data science.
There is a clear separation between someone who is in Business Intelligence vs. Data Science. A Data Scientist creates the software intelligence to assist in research and interprets the information while the Business Intelligence Officer, also known as a BIO, is one who translates the outcome of what the Data Scientist statistically created. Many times the Data Scientist is the one that does both - creates the software along with providing visual interpretation. Data Scientist are well rounded in computer programming languages like Java, Python, R, Scala, C programming (along with influences) and structured query language (SQL) to communicate with relational Database management. Another language you may want to observe is Julia.
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