Friday, June 28, 2013

Big Data Analytics : R and SAS programming

Big Data Analytics : Statistical R and SAS programming
When we discuss cost we can't avoid the constant bickering over the choice of the right statistical Software environment and its pros and cons - namely R or SAS. With respect to statistical analytics capability both SAS and R share the same respectability and we must agree on some occasions one leads the other. However, it is argued that some of the cutting edge latest techniques available in R are not available in SAS. Unfortunately, we are not going to add to the huge amount of information already available on this topic but would rather stick to providing clients worldwide a choice in choosing the software they are already invested in terms of time or money.
Market Equations India offers clients a combination of rich Industry experience and a committed group of intellectuals from business, science and mathematics disciplines that are passionate about analytics and are comfortable and current with programming using different statistical software including R, SAS, SPSS and MATLAB.

Our expertise with the techniques used in R programming includes:
  • Reading data from various source files
  • Evaluate the cumulative distribution function, the probability density function and the quintile function
  • Examining the distribution of a set of data: stem and leaf plot
  • One or two sample tests: box plot, t-test, F-test, two-sample Wilcoxon test, Two-sample Kolmogorov-Smirnov test
  • Grouping, loops and conditional execution: if statements, for loops, repeat, and while loops
  • Writing R functions
  • Statistical modelling: regression analysis and the analysis of variance, generalized linear models, nonlinear regression models
  • Creating data graphics: High-level plotting functions, Low-level plotting functions, Interactive graphics functions
  • Accessing and installing R packages
  • Debugging
  • Organizing and commenting R code
Our knowledge in R programming extends to its comprehensive list of concepts including:
Accessing built-in datasets, Additive models, Analysis of variance, Arithmetic functions and operators, Arrays, Binary operators, Box plots, Character vectors, Concatenating lists, Control statements, Customizing the environment, Data frames, Density estimation, Determinants, Diverting input and output, Dynamic graphics, Eigenvalues and eigenvectors, Empirical CDFs, Generalized linear models, Generalized transpose of an array, Generic functions, Graphics device drivers, Graphics parameters, Grouped expressions, Indexing of and by arrays, Indexing vectors, Kolmogorov-Smirnov test, Least squares fitting, Linear equations, Linear models, Lists, Local approximating regressions, Loops and conditional execution, Matrices, Matrix multiplication, Maximum likelihood, Missing values, Mixed models, Named arguments, Namespace, Nonlinear least squares, One- and two-sample tests, Ordered factors, Outer products of arrays, Probability distributions, QR decomposition, Quantile-quantile plots, Reading data from files, Regular sequences, Removing objects, Robust regression, Search path, Shapiro-Wilk test, Singular value decomposition, Statistical models, Student's t test, Tabulation, Tree-based models, Updating fitted models, Wilcoxon test, Workspace, Writing functions.
Case Study : Statistical R Programming:
Market Equations helps a United Kingdom (UK) based E-Retailer institutionalize Sales and Marketing Analytics by building a correlation model linking Facebook "likes" and "fan" growth to Sales, helping them allocate their marketing spends effectively into channels that maximize returns and reduce costs incurred in holding excess inventory and retain clients by eliminating the possibility of stock outs.
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