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Why the Job Market is Going Gaga over Big Data

We will start off this post with a little bit of trivia.

  • The advertised median salary on offer for technically inclined professionals with expertise in Big Data, which today is a highly sought-after skill is no less than $124,000 inclusive of compensation and bonuses.
  • Cisco, IBM and Oracle together had 26,488 positions that were open during the previous year which required expertise in Big Data.
  • EMC or Dell required 25.1% of all positions in Big Data to have analytics tracks.
  • Data Warehousing, VMWare and developing programming expertise in Python are the fastest growing skill sets that are in demand by companies that are on an expansion of their development teams in Big Data.

why the job market is going gaga over big data

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Decades On, SAS is Still the Market Leader

In the 2016 February report by Gartner, SAS bagged the top slot in its execution ability and was once again placed in the quadrant of leaders in the Magic Quadrant for Advanced Analytics Platforms.According to the description, as provided by Gartner, advanced analytics involves various sorts of data analysis through the use of quantitative methods of great sophistication like machine learning, statistics, simulation, data mining in its both predictive and descriptive forms as well as optimization.


Decades On , SAS Still The Leader


The goal is come up with insights that are unlikely to be discovered through approaching business intelligence traditionally like query and reporting.

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SAS Popularity Continues to Surge Upwards

SAS experienced its 40th successive year of growth as customers vied for the software in order to stay ahead of the fraud, risk and security challenges. The operating revenue of SAS stood at 3.16 billion US Dollars which was an increase of 6.4%. The sales in software also surged to 12% indicating that the SAS popularity is unlikely to abate in the coming years and it will cement its position as the market leader in the analytics sector.



Making its Presence Felt Globally

The growth of total revenue recorded was healthy across the world. The percentage growth touched double digits in most of the regions. It was no doubt facilitated by the new abilities of SAS in fraud, risk and intelligence about security.Much of the SAS revenue was fuelled by the governmental, financial and insurance sectors. The industries that led the way with greatest growth were manufacturing, banking, services and retail.

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Elementary Character Functions in SAS

Basically the number of functions present in the SAS program amount to three. They are Character Functions, Numeric Functions and Date and Time Functions. In this post we are going to take a brief look at Character functions of a basic nature.


Elementary Character Function  in SAS


Character Functions

Suppose that there is this program with the following lines of command:

Data Len_func ; input name $ ; cards; Sandeep Baljeet
data Len_func; set Len_func ; Len=length(name);
Len_N=lengthn(name); Len_C=lengthc(name); run;
proc print; run;


  • The function called LENGTH returns the character value’s length.
  • The function LENGTHN is more or less identical to the LENGTH function. The sole difference between the two lies in the fact that for a value missing character it returns the length that equals to 0 whereas LENGTH returns a value of 1.
  • The function LENGTHC returns to the program the storage length of particular strings.


The ABC of Summary Statistics and T Tests in SAS – @Dexlabanalytics.


Again let us consider the following lines of code:

Data case ; input name $ ;cards;
sandeep baljeet neeta
New_U=upcase(name); New_P=propcase(name); 
proc print; run;


Data Preparation using SAS – @Dexlabanalytics.


Here the following functions are introduced:

  • The function UPCASE converts all of the letters to the uppercase.
  • The function PROPCASE serves to capitalize the first letter of all words and converts the remaining to lowercase.
  • As might be guess from the convention conformed to while naming the function, LOWCASE transforms all letters to their lowercase counterparts.


In the following program commands:

Data AMOUNTS; input NAME $20.; cards;


Here’s why SAS Analytics Is a Must-Have IT Skill to Possess – @Dexlabanalytics.


Here we can see the following syntax:

  1. Compress (Variable, ”want to remove”);
  2. Compbl (Variable)
  • The function COMPRESS removes blanks by default. It can also remove a particular specified character value as indicated by the code. In the example cited the character value ‘-‘is compressed.
  • On the other hand the COMPBL function serves to result in a single blank from multiple ones.


For expert guidance, you will be well advised to enroll yourself in a SAS course from a reputed SAS Training institute. You may consider DexLab Analytics if you are in the vicinity of Delhi or noida.


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Delimiters And Delimited Data in SAS

In this blog post we will delve into the world of delimiters as found in the SAS system of data analysis tools. Delimiters are an essential part of SAS without whose guidance SAS would be in some blind inspite of all the data that surrounds it as the data supplied to it either internally or as an external file.


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What are Delimiters?

Delimiters are essentially symbols to SAS that lets SAS know that the data is separate. They distinguish one set or category of data from others. This should give you an idea about how essential a part of data analytics, a delimiter plays.

How are the Delimiters Symbolized?

SAS accepts the following symbols and key strokes as delimiters:

  •  ,
  •  :
  • Tab
  •  ~
  • &
  •  –

How to Import Delimited Data?

This command imports data infile

Data claim_data;

infile datalines dlm = “,”;

Input sex $ name $ claim_amount ; datalines;








data claim_data ;

Infile E:\Project FT\SAS\Course Material\Class 1\Claim Data comma.txt’ dlm = “,”;

Input sex $ name $ claim_amount ;


What are the Functions of Delimiters?

An INFILE option, the DSD or delimiter-sensitive data serves varied functions. They are as follows:

  • The default delimiters are changed to wanted ones from the default blank.
  • In case a row contains two delimiters, SAS interprets that there is an instance of a case of missing value.
  •  Delimiters also strip the quotes within which character values are placed.

So the command would boil down to:

data claim_data ;

Infile E:\Project FT\SAS\Course Material\Class 1\Claim Data comma.txt’ dsd;

Input sex $ name $ claim_amount ;


How to Read Data from an External CSV file?

Our next task is to read data from external CSV files. In order to do so we have to input the following:

proc import datafile = “E:\Project FT\SAS\Course Material\Class 1\exam_results.csv”

dbms = csv replace out = class_10_result; Getnames = yes; run;

In a Nutshell

What are Format, Informat and DSD?

  • Informat : This command instructs SAS how exactly to going about reading the data.
  • Format : This instructs SAS about the exact way in which to show the details.
  • DSD : This defines how data is separated by a delimiter.

Munich Re Bets its Big Data on SAS

Munich Re which one of the leading reinsurers in the world, has opted to deploy SAS in order to achieve the goal of its Big Data strategy. Business units and specialist departments across verticals are all set to use the SAS platform in order to carry out critical functions like forecasts, analyses, pattern recognition and simulations.


The SAS software suite automates the whole process of acquisition as well as analysis of content derived from complex contracts as well as claim notifications. Having access to a large pool of data the company is better placed to innovate by making use of Big Data analytics. This will let it offer new and customized offers or proposals, Put in place for access throughout the world, the Analytics platform from SAS comes into play by accessing a considerable number of internal and external sources of data. Its flagship in-memory tech makes it possible to analyze huge data quantities of data interactively so as to be able to find new correlations that would otherwise be impossible to recognize in the absence of highly advanced tools for analytics. The in-database processing model allows development and management of data models to be directly run from the database itself. This in simple terms translates to that the analyses are our in the platform SAP HANA or its open-source counterpart, the Hadoop framework. These tools enable analysis of unstructured text data in massive quantities.

The factors which turned the decision for Munich Re in favor of SAS were the speed at which the analyses were carried out, the upward graph in the tech graph, the performance of the team for SAS overall and the ability of the system to deliver and deploy results swiftly.

The CEO for Munich Re Torsten Jeworrek attributed the success of their analysis of data to it and added that it contributed significantly to the value gotten by their customers. He also forecasted that with the adaptation of these new technologies the ability of Munich Re to combine the customer data and compare it with the expert knowledge and findings of the company.

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