Programming Languages for Data Analysis

Programming Languages for information AnalysisR and Python for info AnalysisAbstractThis paper arguees the comparison between the pop programming languages for Data analysis. Although there are plenty of choices in programming languages for Data science like Java, R Language, Python and so forth With a whole lot of research carried come out of the closet to know the strengths of these languages, we are going to discuss any two of these. Data Analytics has been the most important and trusted tool for business and markets. Data Analytics is nowadays qualification use of SAAS (Software As a Service).For this literature review, two popular languages (R and python) have been studied and evaluated the characteristics to decide which one provide be the right language for information analysis. Both Languages shows their own strength and weakness and based on that, to understand the selective information based processing environments in the Distributed File Systems.Keywords-Programmin g language Data analytics R Python, Big DataFor an industry to grow in a market is non an easy task. With the help of Data Analytics, it suffer grow bigger and better. It discharge help to deliver quick corporate results and a value to business. The major challenge with the selective information is to process it and then make decisions worth value. Data Crunching requires kosher tools and mightily analysis. Out of all languages, we choose two popular language i.e R language and Python for data analysis.We are going to discuss the need of using a programming language in Data Analysis and list some of the characteristics of these two languages. In the end, we will conclude which language performs and delivers in the firmament of Data Analysis.While carrying out research in Data Analytics, we came across multiple programming languages apart from R and Python which are described below-Julia Not a well-recognized language but hackers surely talk of Julia. It is said to be faster than R upgradable than Python. 5Java In comparison to R and Python, Java seems less fitting in terms of Data Visualization but can be the first choice for the prototype of the statistical system. 6MATLAB Became popular and was apply onward the disengage of python and R.To be good fit as a programming language we should consider different aspects of data analysis. For this review purpose we will broadly shed light on them as follow-Collection of Raw Data Data is available in variety of format. Programming languages were evaluated in terms of support for various data formats and expertness in handling them.Data processing Once imported into program, datasets might require cleansing in terms of missing values, unrelated or unornamented data values etc. Capabilities to deal with such data were evaluated for programming languagesData Exploration Simplicity of applying commonly used statistical methods like grouping, grade recognition, switching and sorting is evaluated for programming languages.Data Analysis Availability of special purpose in-built functions and various methods of machine learning and deep analysis are used as evaluation measures.Data Visualization Visualization is important aspect of data analytics. Visualization capabilities of programming languages were evaluated on the basis of ease of creation, simplicity and share-out in various formats.In addition to these capabilities we will discuss a bit about history and accolades of e rattling programming language. We will withal discuss popular choices for IDE (Integrated Development Environment) for these1 language.Introduced in 1995, by Ross Ihaka and Robert Gentleman, R is implementation of S programming language (Bell Labs). Latest version is 3.1.3 which was released in March, 2015. Rs architectural design and growing is maintained by R-foundation and R-Core Group. 1Rs software environment is written primarily in C, FORTRAN, and R. RStudio is very popular IDE used to perform data analysis using R. uncreated used for academic research, R is rapidly expanding into enterprise market. 1A. Collection of Raw DataYou can Import data from variety of formats like excel, CSV, and from text files. DataFrames, primal data structure in R, can import files from SPSS or MiniTab. Basically R can handle data from most common sources without glitch.Where R is not so great at is data collection from web. Lot of work is being carried to address this limitation. To name hardly a(prenominal), Rvest package will perform staple fiber web-scraping piece of music magrittr will parse the information on webpages. 13B. Data ProcessingIt is very easy to reshape dataframe in R. Tasks like adding new columns, populating missing values etc. can be done with just one line of code. Many new packages like reshape2 allow substance abusers to manipulate data frames to fit the criteria set per requirements. 3C. Data ExplorationR is built by statisticians. For exploratory work its easy for be ginners. Many models can be written with very few lines of codes. With R, users will be able to build probability distributions and apply statistical methods for machine learning. For advance work in analytics, optimization and analysis, users may have to rely on tertiary party packages. 3Many popular packages like zoo (to work with time-series), caret (machine learning) represent strength of R. Python is loosely bind programming language with very wide user base.D. Data VisualizationVisualization is strong forte of R. R was built to perform statistical analysis and demonstrate the results. By default, R allows you to make basic charts and plot graphs which can be saved in variety of formats like jpeg or PDFs. With advance packages like ggvis, lattice and ggplot2 user can extend data visualization capabilities of R program. 13Created by Guido Van Rossum in 1991, Python is inspired by C, Modula-3 and in-perticular ABC. Python software foundation (PSF) is curator for Python language. Current version is 3.4.3/2.7.9 released in Feb 2015/Dec 2014. Python has been popular choice for programmer to build web and multitier applications. In context of data analytics, Python is majorly use by programmers to apply statistical techniques. Coding in python is easy because of clarified syntax. 4IPython Notebook and ANACONDA are popular IDEs used for data analysis using Python.A. Collection of Raw DataIn addition to excel, CSV and text data, python also supports JASON and semi-structured data formats like XML and YAML. Using certain libraries, users can import SQL tables into python program 4Python Request Library facilitates web scrapping, where user can get data from websites to analyze in depth. 2B. Data ProcessingTo uncover underlying information, Pandas library of python comes handy. Like R, data is held in DataFrames which can be used and reused throughout program without hampering performance. 2Users can apply standard methods of cleaning data or process data to fil l out incompelete information just like R.C. Data ExplorationPandas is very powerful library. Users will be able to group by datavalues and sort them according to timeseries. Comlex grouping clauses like time-series analysis to seconds can be performed on dataframes in python program.D. Data VisualizationUsing MetaPlotlib 2 library, user can plot basic graphs and chrats from available data-points. For advance visulization, Plot.ly can be used, which is another python library.Users can use powerful IDEs like Anaconda or IPython Notebook to create powerful visualization and convert them into various formats like HTML.In addition to their differences, there are few common positives about both Python and R which make them so popular among data analysts and statisticians.R and Python are distributed under open license which make them discharge to download and modify per users need. In contrast to other programming tools, like SAS and SPSS, which come with hefty price tag.Being open sour ce, many advancements in statistics will come to python and R first.6Both of them are widely loved and supported by big community of statisticians and developers. 6IDE like IPython Notebook will consolidate your datasets in one file, thereby simplifies your workflow.2R has rich ecosystem of cutting edge packages to string your work together which proves useful in particular to Data Analysis.3Python is more of worldwide purpose language. Its easy and intuitive, therefor it has simplified learning curve.Pythons testing framework guaranties reusability and reliability of code.R is language developed by statisticians for statisticians while python is easier to learn global purpose programming language.3Working through research in programming languages for data analytics, there are many other options which are listed below-Julia Though not yet widely recognized, data hackers talk fondly of Julia. It is regarded as faster than R and more scalable than Python.5Java Although java is not as undetermined as python and R in terms of visualization, it can be primary choice to build prototype for statistical system. 6KAFKA Developed by linked-in, KAFKA is highly regarded for its real-time analytics capabilities.6STORM Storm is framework written in SCALA which saw recent tides of popularity in Silicon ValleyMATLAB Excel Used by many statisticians before outburst of python and R.Special thanks to Prof. Oisin Creaner, for presenting this opportunity to dig out for various options available for programming in Data AnalyticsIhaka, R. and Gentleman, R., 1996. R a language for data analysis and graphics. Journal of computational and graphical statistics, 5(3), pp.299-314.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., 2011. Scikit-learn Machine learning in Python. The Journal of Machine Learning Research, 12, pp.2825-2830..Nasridinov, A. and Park, Y.H., 2013, Septe mber. Visual Analytics for Big Data Using R. In Cloud and Green Computing (CGC), 2013 Third International Conference on (pp. 564-565). IEEE.Sanner, M.F., 1999. Python a programming language for software integration and development. J Mol graphical record Model, 17(1), pp.57-61.Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A., 2012. Julia A fast dynamic language for technical computing. arXiv preprint arXiv1209.5145.Fan, W. and Bifet, A., 2013. Mining big data current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), pp.1-5.