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This document outlines various techniques used for data mining and predictive analytics including social network analysis, clustering, sentiment analysis, entity extraction, statistical regression, decision trees, random forests, gradient boosting, deep learning and neural networks. It also references the Cross Industry Standard Process for Data Mining model.
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Cloudera presentation
- 2. Blood
- 7. Social Network Analysis Clustering Sentiment Analysis Entity Extraction Predictive Analytics
- 9. Modelling Statistical Regression Linear Logistic Decision Tree Ensemble Random Forest Gradient Boost Deep Learning Neural Networks
Editor's Notes
- It is hard almost impossible to find someone with all those skills It is important to note that the data scientist must have some domain expertise to effectively do their job There is a world wide shortage of Data Scientists
- There are two types of people in the world – people who can deal with incomplete datasets.
- The DIKW model or DIKW pyramid is an often used method, with roots in knowledge management, to explain the ways we move from data (the D) to information (I), knowledge (K) and wisdom (W) with a component of actions and decisions. Simply put, it’s a model to look at various ways of extracting insights and value from all sorts of data, big, small, smart, fast and slow. It is often depicted as a hierarchical model in the shape of a pyramid and also known as the data-information-knowledge-wisdom hierarchy, among others. Data is seen as an asset within an organization and, if exploited correctly, can be used to gain a competitive advantage over their rivals. Beliefs, Desires and Intentions (Bratman, 1999) methodology based upon Michael Bratman’s theory of human practical reasoning). Michael Bratman’s theory can be applied to data analysis where the data modeler should take the following into consideration: Beliefs provide the inference rules to process and manipulate the incoming data. These rules can be captured and reused for new data sets or reapplied to existing data sets.
- Cross Industry Standard Process for Data Mining, commonly known by its acronym CRISP-DM, is a data mining process model that describes commonly used approaches that data mining experts use to tackle problems.
- Social Network Analysis SNA Predictive Analytics Sentiment Analysis Customer Churn Text Analytics – Entity Extraction
- Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed." Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.
- In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution. A related but distinct approach is necessary condition analysis[1] (NCA), which estimates the maximum (rather than average) value of the dependent variable for a given value of the independent variable (ceiling line rather than central line) in order to identify what value of the independent variable is necessary but not sufficient for a given value of the dependent variable. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However this can lead to illusions or false relationships, so caution is advisable;[2] for example, correlation does not imply causation.
- The Australasian Data Mining Conference is devoted to the art and science of intelligent data mining: the meaningful analysis of (usually large) data sets to discover relationships and present the data in novel ways that are compact, comprehensible and useful for researchers and practitioners. Melbourne, August 2017 Charles Joseph Minard's vectorized map (1869) displaying the movements and the number of Napoleonic troops during the Russian campaign (1812-1813), as well as the temperature on the return path. About