Quants就是宽客的意思，数据、模型量化工程师。
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What Are Analytics?
>A extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact based managmeent to drive decisions and add value.
(***key words: data->stat\quant->explain->predict->optimizing management&decision***)
>Analytics according to methods and purpose.
*1 Descriptive Analytics: involve gathering, organizing, tabulating, and depicting data and then describing the characteristics about what is being studied. It might be useful, but doens't tell you anything about why the results or what will happen.
*2 Predictive Analytics: use data from the past to predict the futures. Identifying association among variables and predicting the likelihood of a phenomenon--say, that a customer will respond to a particular product Ads by purchasing it--on the basis of the identified relationship. In fact, the presence of causal relationships is not always necessary to make accurate predictions.
*3 Prescriptive analytics: include methods such as experimental design and optimization. It suggests a course of action. Experimental design tries to answer the questions of why somenthing happened by conducting experiments. To make causal inferences with confidence in causal research, researchers must manipulate one or more independent variables and effectively control other extraneous variables. If the test group performs substantially better than the control group--then the decision maker should apply that condition broadly.
*4 Optimization: attempts to identify the ideal level of a particular varible in its relationship to another. For example, we might be interested in identifying the price of a product that is most likely to lead to high profitability for a product. Similarly, optimization approaches could identify the level of inventory in a warehouse that is most likely to avoid stock=outs in a retail organization.
>Analytics classifies by the process employed and the type of data that are collected and analyzed
*1 Qualitative analysis: amid to gather an in-depth understanding of the underlying reasons and motivations for a phenomenon. Usaually unstructured data is collected form a small number of nonrepresentative cases and analyzed nonostatistically.
*2 Quantitative analytics refers to the systematic empirical investigation of phenomena via statistical, mathematical, or computational techniques, Structured data is collected from a large number of representative cases and analyzed statistically.
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Jargon:
Statistics: The science of collection, organization, analysis, interpretation, and presentation of data
Forecasting: The estimation of some variable of interest at some specified future point in time as a function of past data.
Data mining: The automatic or semiautomatic extraction of previously unknown, interesting patterns in large quantities of data through the use of computational algorithmic and statistical techniques.
Text mining: The process of deriving patterns and trends from text in a manner similar to data mining.
Optimization: The use of mathematical techniques to find optimal solutions with regard to some criteria while satisfying constraints.
Experimental design: The use of test and control groups, with random assignment of subjects or cases to each group, to elicit the cause and effect relationships in a particular outcome.
More Jargon:
Small data: structured, quantitative and small volumes(TB)
Big data: unstructured data in large volumes(tenth PB)
*In what situation shall we apply analytics: The decisions that work well with analytics are those that are made repeatedly, allow for some time to do the analysis, and are important enough to justify an investment.*