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Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.
Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:
In sales and trading, quantitative analysts work to determine prices, manage risk, and identify profitable opportunities.Historically this was a distinct activity from trading but the boundary between a desk quantitative analyst and a quantitative trader is increasingly blurred, and it is now difficult to enter trading as a profession without at least some quantitative analysis education.
The use of operations analytics is not confined to the field of information technology. Data from business intelligence, finance, science, weather, and even current events are combined and then analyze together to extract valuable insight from it, and this in turn, drives quick decision making in almost every conceivable use.
While there are numerous analysis tools in the market, Big Data analytics is the most common and advanced technology that has led to the following hypothesis: Data analytic tools used to analyze data collected from numerous data sources determine the quality and reliability of data analysis.
The financial applications of Big Data range from investing decisions and trading (processing volumes of available price data, limit order books, economic data and more, all at the same time), portfolio management (optimizing over an increasingly large array of financial instruments, potentially selected from different asset classes), risk ...
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
An alternative data set can be compiled from various sources such as financial transactions, sensors, mobile devices, satellites, public records, and the internet. [3] [8] [9] [10] Alternative data can be compared with data that is traditionally used by investment companies such as investor presentations, SEC filings, and press releases. [11]
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