Data science dug its roots deep in this digital world since 2010. Businessmen started to understand the use and importance of Data Science in their business. Data Science is the future of Artificial Intelligence. Traditionally, the data used to be in structured format which could be understood by using simple Business Intelligence {BI} Tools. But gradually, the data has been changed into Unstructured format. The Data has been generated from various sources like Text files, Financial logs, Multimedia Forms, etc. Data Science Tools will enable the user to easily understand derive real meaning from the data. Collecting proper data and transforming it into rich analysis is the key point to every Data Science strategy. Data science tools can reduce the errors and duplication and ensures accuracy of data and preserve the integrity of the data.
There are many tools of Data Science. Some of them are below.
· Python
· Apache Giraph
· Apache Hadoop
· Apache Storm
· R
· Tableau
· Keras
· Clojure
· DataRobot
Out of these many tools, few tools are very popular and recommended by most of the Data Scientists. Such as
1. APACHE HADOOP
Apache Hadoop is a collection of open- source software utilities that provide using a large network of multiple computers to solve problems involving massive amounts of data and computation. This software helps in expanding of storage and processing of Big Data. The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System {HDFS}, and a processing part which is a MapReduce programming model. This software launched on 1st April 2006 by Apache Software Foundation. The Apache Hadoop framework is mostly written in Java script.
Hadoop Distributed File System has five services i.e.
· Name Node
· Secondary Name Node
· Job Tracker
· Data Node
· Task Tracker
HDFS stores files across various machines. Hadoop works directly with any distributed file system that can be mounted by the underlying operating system by simply using a url, but this comes at a price- the loss of locality. To reduce network traffic, Hadoop needs to know which servers are closest to the data, information that Hadoop-specific file system bridges can provide. Thus, Apache Hadoop is one of the most usable and favourable tool of Data Science.
2. KERAS
Keras is an open- source neutral network library. It is written in Python that runs on top of Theano or Tenserflow. The advantage of keras is, it is designed to be modular, fast and easy to use. Keras does not handle low level computation, for that we use backend. Keras contains numerous implementations of commonly used neural- network building blocks like- Objectives, Layers, Optimizers, and a host of tools to make working with Image and Text makes it easier. It supports recurrent neural networks. Thus, Keras has become one of the important tool for Data Science.
Many other Data Science tools work their part to support the field and helpful in various ways to take its range further.
Source Link: