Big data techniques for an it industy
There are various techniques like statistics and sentiment analysis that can be used on big-data for analytics. These techniques [7], not all necessarily, form a part of a business intelligence tool. This section holds a list of such techniques applicable over a range of industries:
Classification This technique is used to identify the set or category a particular data instance belongs to. Training datasets are used to determine the known sets or categories for classification. learn more on big data through big data online training
Cluster analysis
It is a method of combining objects into clusters (groups), such that objects in the same group are similar. No training data set is required to ascertain groups.
Crowdsourcing
It is a technique of collaboration of evaluations from a large group of people to solve a problem related to big data, where computations do not work well.
Data fusion and data integration
This technique is used to integrate and analyze large data from different sources, by applying transformation methods, to produce useful outcome.
Data mining
It is a technique of discovering patterns from big data using concepts of artificial intelligence, machine learning and statistics.
Machine learning
It is an algorithm for predicting more accurate results in the form of patterns (Pattern recognition) or models (Predictive modeling); with the capability to learn from training datasets and previously produced results.
Natural language processing (NLP)
NLP provides an efficient way to analyze and derive meaning by processing human-computer interactions.
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Optimization
It is a technique of selecting the best or optimal solution from a set of alternative solutions to a problem.
Regression
Regression is form of supervised learning used for establishing a relationship between dependents or outcome variables and the predictors or independent variables.
Sentiment analysis
It is a technique of measuring polarity (positive, negative or neutral) of subjective information contained in natural or human language.
Signal processing
It is a technique to analyze random signals (continuous or discrete) like sensor and radio signals etc., inherent in big data.
Spatial analysis
It is a technique to study data trends using its geographical, topological or geometric properties.
Statistics
This technique involves interpreting data, generally numeric, and its related computations to achieve more accurate analysis.
Simulation
This technique involves modeling real complex systems and to study actions and effects for predicting results for real systems.
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