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Data Science

A Guide to Data Science...

Data science is a field surrounding processes and systems to extract knowledge from data in structured or unstructured forms, this can be a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics. Data science pulls techniques and theories from many fields such as mathematics, statistics and computer science. It also includes signal processing, probability data models, machine learning, statistical learning, data mining, database, data engineering, visualization, predictive analytics and data warehousing.

Abilities to scale to big data are of interest in data science and big data technologies often just focus on organizing the data instead of analysing it. Data science affects many fields such as machine translation, search engines and the digital economy. It also affects and contributes to biological sciences, medical informatics, health care, social sciences and the humanities as well as heavily affecting and influencing economics, business and finance.

Regarding business, data science is important to competitive intelligence, and its activities include data mining and data analysis. Although the term "data science" is widely used in business, many academicians see no difference between data science and statistics. Many think the data-scientist is a sexed up term for a statistician as statistics is a branch of science.

Data is now increasingly cheap, we are collecting and analysing data that was created over centuries and collecting lots of new types of data from the web, mobile devices and transactions. Data science has emerged and it is thought that about 90 percent of the data in today’s world has been created in the past two years. New technologies are emerging to organize and allow us to analyse this huge influx of data. It is possible now to spot patterns and regularities in data that allows us to advance in many fields such as scholarship, the human condition and improve commercial and social value.

The rise of data science can help us understand phenomena such as physical, biological, human, social and economic behaviour. Although expertise in data science is holding us back from really bringing value to data. Working with data science requires distinctive new skills sets and tools, data is now to large in number to manipulate with traditional databases or statistical tools and is more varied than past data. Digital text and blog data, is typically messy and unstructured. There are challenging issues of privacy, security, and ethics. Data science is at the intersection of social science and statistics, information and computer science and design.

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