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Only proprietary tool with end-to-end Analytics Solutions

Along with an easy-to-learn, flexible programming language, you get a web-based programming interface; ready-to-use programs for data manipulation, information storage and retrieval, descriptive statistics and reporting; a centralized metadata repository; and a macro facility that reduces programming time and maintenance headaches.


Integrate data across environments.

Based on an open, cross-platform architecture, Base SAS is hardware-agile and integrates into any computing environment infrastructure, enabling you to unify your computing efforts and get a single view of your data.

Read, format and analyze any data.

From small data issues to large complex data problems, programmers can read, format, analyze and report on data quickly, regardless of format.

Make programming fast and easy.

An intuitive and easy-to-learn programming language and packaged programs called procedures significantly reduce the amount of code needed to deliver information. SAS procedures encapsulate and deliver functionality with a few simple commands, increasing programmers’ productivity. SAS Studio, a web-based developer interface, lets you access data files, libraries and existing programs from any device that has a web browser – making SAS coding easier and more accessible than ever.

Deliver reports to mobile devices.

Base SAS provides maximum reporting flexibility. You can easily create reports in standard office formats such as RTFs, PDFs, Microsoft PowerPoint, HTML and an e-books format that can be read with iBooks® on the iPad® and iPhone®. Produce reports and visualizations of analytic results automatically from statistical procedures and deliver them on the platforms and applications people use most.

Maximize your computing resources.

Multithreaded, scalable and high-performance capabilities take advantage of parallel processing so you get the most from your computing resources and produce faster answers. Data manipulation routines can be executed on the data in-database or in-Hadoop, reducing the need to move data. This improves performance and security.

Combine SAS and Hadoop.

The Hadoop procedure lets you submit HDFS commands, MapReduce programs and Pig language code against Hadoop data from within SAS. Easily store large SAS data sets on Hadoop clusters and add the benefits of Base SAS security features such as encryption and password protection to Hadoop implementations.

Access industry-standard data security.

SAS/SECURETM is delivered as part of Base SAS and provides access to industry-standard data encryption, including the Advanced Encryption Standard (AES). So you can encrypt SAS data on disks, and increase security for stored passwords.




Talent: Required Vs Available(Scale of 10)

10 vs 7


Pay Scale (Scale of 10)



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Built for flexibility

Like Java, Python is built to handle high-traffic sites. It’s fast and efficient, with an emphasis on code readability. Python’s motto is “there should be one—and preferably only one—obvious way to do it.” That can mean there’s a bit of a learning curve as developers learn the ins and outs of Python syntax, but the upside is an ability to express concepts with fewer lines of code than would be possible in languages like C++ or Java.

Python’s other great strength is an extensive set of libraries that allow it to perform a wide array of tasks. In particular, the libraries NumPy and matplotlib enable Python to perform many of the analysis and plotting functionalities of MATLAB. These libraries have since been built upon by a number of other libraries that extend Python’s functionality even further.

In short, Python represents a compromise between R and Java, combining the sophistication of the former with the speed and scalability of the latter.


Workflow Integration.

Python’s flexibility makes it a popular choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. If you’re looking for a single tool to manage your entire data-related workflow, Python is a great option.

Machine Learning. The combination of specialized machine learning libraries (like scikit-learn, PyBrain, and TensorFlow) and general purpose flexibility makes Python uniquely suited to developing sophisticated models and prediction engines that plug directly into the production system.


Highly specialized data tasks.

Though the Python community is catching up, there are still hundreds of R packages that have no Python equivalents. If you’re looking for very specific capabilities, you might be better off with R.

Talent: Required Vs Available(Scale of 10)

8 vs 3


Pay Scale (Scale of 10)



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Fastest Programming ever

Scala runs within the Java Virtual Machine and provides a completely different approach to application development. It allows you to interact seamlessly with standard Java libraries while developing from a functional perspective. Initially intended for writing complex back-end systems that are resource-heavy and use shared artifacts, Scala now has more widespread uses.

We found Scala to be the best option for some of our clients’ enterprise mobile applications, but it’s not right for every situation. Before you can decide if you should be using Scala for your project, it’s important to understand the pros and cons of the language and its core strengths.


Simple and straightforward syntax.

Scala typically requires two-thirds less code than Java. The syntax is also more flexible. For example, you can leave out periods between method calls so the code is more human-readable and easier to understand.

Inherently immutable objects.

Scala’s programming language reduces many thread-safety concerns that spring up in traditional Java applications.

  • Highly functional. Scala treats functions as first-class citizens.
  • Fast implementation speed. It allows for quicker implementation and enhanced performance.
  • It is fun!Scala challenges strong engineers in a meaningful and entertaining manner, making development more fun.
  • Easy to solve concurrency issues.It has an Actor library to solve concurrency problems more rapidly.
  • XML support.Scala supports XML, which is beneficial if you have a need to encode documents in your products.

Limited community presence.

Since the language is new, there are limited online communities and resources to help you troubleshoot issues. Unlike the Grails or Rails communities, Scala is still gaining followers and experts. If your developers do not have the time to resolve issues themselves, Scala may not be a viable option for you.

Hard to learn.

Syntactically, Scala is significantly different from traditional Java. Furthermore, it presents a completely different programming paradigm—requiring a higher level, more sophisticated developer/engineer skill set to understand it. Thus, Scala developers are in low supply and costly.

Lack of ease of adoption.

When coding with a team, it’s rare to find everyone writing purely in Scala. You need a team of eager adopters for Scala to be feasible. Otherwise, you will end up with code that’s a hybrid of Scala and Java, which can cause problems. Ramp up time can be very slow and time consuming. And it’s very difficult to find/build a team of pure Scala developers.

Limited backward compatibility.

Each major new release of Scala is incompatible with the previous version. This leads to a lot of wheel-reinventing, headaches and product delays. If your development schedule is tight, you should avoid using Scala, for now, to stay on track.

Talent: Required Vs Available(Scale of 10)

8 vs 2


Pay Scale (Scale of 10)



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Beloved by data scientists

Originally developed by statisticians as an open-source alternative to expensive suites of statistical software like SAS and MATLAB, R is one of the most popular languages for data analysis. It’s been likened to Excel on steroids, able to sift through reams of data, execute sophisticated analyses, and produce publication-quality graphs and tables. What makes R special? In short, it’s a tool built with data analysis in mind.

As data science has become critical to many businesses, R’s popularity has skyrocketed. Organizations as large and diverse as Google, Facebook, Microsoft, Bank of America, and the National Weather Service have all turned to R for reporting, analysis, and visualization.

A key component of R is that, unlike object-oriented programming languages like Java or Python, R is a procedural language, meaning it relies on a series of step-by-step sub-routines to execute a programming task. The key difference here is that R uses procedures to operate on data, where object-oriented programming bundles procedures and data together as parts of objects. The advantage of procedural programming is that it gives clear visibility into complex operations with lots of dependencies, which can be important for many data analysis tasks. The tradeoff is that this often requires more lines of code than object-oriented languages.

Another benefit of R? It’s supported by a vibrant community of developers, especially academic statisticians and data scientists.


In-Depth Statistical Analysis.

Given that R was developed by and for statisticians, it’s no surprise that R is ideally suited to in-depth statistical analysis, whether you’re working with sensor data from an IOT device or elaborate financial models. What’s more, it’s very well supported by the statistics community through the CRAN repository, which contains literally thousands of packages that enable you to perform more elaborate analysis and visualization tasks.

High-Quality Reporting.

Well-produced images convey more than numbers alone, and R places a great emphasis on easily producing high-quality graphs and charts. On top of that, its basic capabilities can be extended with a number of packages, including ggplot2, ggvis, googleVis, and rCharts. The Shiny framework also allows you to turn those visuals into interactive web applications.



R was designed with data scientists in mind, not computers. As such, R is considerably slower than Python or Java.

Creating large-scale data products.

In these instances, data scientists will often prototype in R and then switch to a more flexible language like Java or Python for actual product development.

Ease of Learning.

If your background is in math or statistics, R’s array-oriented syntax can make implementation relatively straightforward. If you have programming experience, however, this approach is likely to seem counterintuitive.

Talent: Required Vs Available(Scale of 10)

7 vs 4


Pay Scale (Scale of 10)



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