Big Data Analytics Made Easy

What is big data
Big Data is any voluminous amount of Structured, Semi-structured and Unstructured data that has the potential to be mined for information where the Individual records stop mattering and only aggregates matter. Data becomes Big data when it is difficult to process using traditional techniques.

R is a programming language for statistical analysis and reporting. R is a simple programming language which includes many functions for Data Analytics, it has an effective data handling and storage facility. R provides graphical facilities for data analysis and reporting. I request you to please Install R and R studio which is freely downloadable. To work in R studio, you need to have even R at
the back end, so please go to site CRAN and Install latest version of R according to your Operating system. So Let’s Start (Rock and Roll) with R-Studio: When you first open the R-studio, you will see four windows.
1. Scripts: Serves as an area to write and save R code
2. Workspace: Lists the datasets and variables in the R environment
3. Plots: Displays the plots generated by the R code
4. Console Provides a history of the executed R code and the output.

Human Neural Network: The basic computational unit in the nervous system is the nerve cell or neuron. A neuron has 1. Dendrites (inputs) 2. Cell body 3.Axon (output). A neuron receives input from other neurons, Inputs gets summed up and, Once input exceeds a critical level, the neuron discharges an electrical pulse that travels through the body, down the axon, to the next neuron. This spiking event is also called depolarization and is followed by a refractory period, during which the neuron is unable to fire. The axon endings almost touch the dendrites or cell body of the next neuron. Transmission of an electrical signal from one neuron to the next is effected by neurotransmitters, chemicals which are released from the first neuron and which bind to receptors in the second. This link is called a synapse. The extent to which the signal from one neuron is passed on to the next depends on many factors, e.g. the amount of neurotransmitter available, the arrangement of receptors, amount of neurotransmitter reabsorbed, etc.

Applications of neural network:
1. Robotics – Navigation, Vision Recognition
2. Medicine – Storing medical records
3. Speech Recognition
4. Stock market prediction
5. Data compression
6. Image processing
7. Face recognition
8. Cab or truck position tracking
9. Signal Processing
10. Recognizing Handwritten characters.

Big Data Analytics Made Easy

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