Data Analytics

                                                         DATA ANALYTICS

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                                                    Fig : Data Analytics

DATA ANALYTICS (DA) :          

                   Analytics is the discovery of meaningful patterns in data.  Data analytics is used for increase productivity and business gain. This is conducted in business to consumer applications. Data is extracted to analyze behavioral data, patterns and techniques. Data analytics is also called as data analysis.

SOFTWARE USED FOR DATA ANALYSIS:.

                       

                    

                                                         Fig : Software tools for Data Analysis          

                     

                        1. Rapid Miner

                        2. Shogun

3. Weka Data Mining

4. Orange Data mining.

5. Data Melt.

6. Microsoft R

                        7. R Software Environment

 1.  RAPIDMINER:

               Rapid Miner is capable of manipulating, analyzing and modeling data. Rapid Miner makes data science teams more productive. Its unified data science platform. It speed up the building of complete analytical workflows  from data prep to machine learning to model validation to deployment  in a single environment.

2. SHOGUN:  

                Shogun is a open source toolbox. It is always written in C++. It gives data structures for machine learning problems.

                  Focus : On kernel machines/support vector machines for regression problems.

3. WEKA DATA MINING:           

                       Weka is open source software which is a group of machine learning algorithms which is used for data mining tasks. The algorithms are applied to a data set.

4. ORANGE DATA MINING:

                        Orange is open source data analysis. It gives interactive workflows to analyse data. Orange is load with different visualizations, from scatter plots, bar charts, trees, to dendrograms, networks and heat maps.

5. DATAMELT:

                Data Melt, is an environment for data analysis and data visualization. It is designed for analysis of data mining, statistical analyses and math computations. It can be used with different programming languages. Data Melt is not limited by a single programming language. Data analysis and statistical computations can be done using high-level scripting languages like Python as well as a lower-level language such as JAVA.

6. MICROSOFT R:

                  This is the most widely used statistics software. The installation of many packages include R packages released by Microsoft Corporation to further enhance your Microsoft R which is Support for Windows and Linux-based platforms.

7. R SOFTWARE ENVIRONMENT:

                   This is written in C.Lot of its modules are written in R. It’s a free software programming language. The R language is widely used among data miners for data analysis.

TYPES OF DATA ANALYTICS APPLICATIONS:

             1)      Exploratory Data Analysis (EDA):

                                To find patterns and relationships in data.

              2)      Confirmatory Data Analysis (CDA):

                                Statistical techniques to determine whether thesis about a data files are true or false.

USES:

                   1.  Data analytics technologies are mostly used in commercial industries to enable organizations to increase revenues, operational efficiency, productivity and business gain.

                    2.   DA is used by scientists and researchers to verify or prove false scientific models, theories and hypotheses.