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Here’s why SAS Analytics Is a Must-Have IT Skill to Possess

Without the great Analytical surge, everything was looking fit and fine. The economy was performing well. The IT industry was looking stable. The tech honchos were playing fine. And then IT happened! Data Analytics snatched the dazzling limelight all to itself.

Here’s why SAS Analytics Is a Must-Have IT Skill to Possess

It’s true once in a while, our market needs a good shaking, or else things tend to get sluggish and slow. Over time, the industries start decreasing in efficiency and business houses crumples. Therefore, the change induced by Big Data Analytics is one for good: it started pulling back the market to its former position. From medical science to military to security, the reach of Big Data Analytics can be witnessed everywhere.



The evolution of analytics is largely consistent and covers a wide span of industries. It’s not like it suddenly came into a lot of focus, its advancement was slow and steady. Now, it has strived to become extremely important to store, interpret, analyze and develop crucial insights – social media is deriving maximum benefits out of analytics, while customizing their products to make more money from advertisements. On the other hand, the service-oriented companies love to manipulate data that is generated through myriad social channels to trigger customer base.


How Data Preparation Changed Post Predictive Analytics Model Implementation

Data scientists assembling predictive models and formulating machine learning algorithms need to spare more time on data preparation work upfront than is required in traditional analytics applications.


How Data Preparation Changed Post Predictive Analytics Model Implementation


In today’s business sphere, the drive to structure big data architectures that would stand on predictive analytics models, data mining and machine learning applications is fast modifying the pattern of the data pipeline, along with the data preparation steps necessary to fuel it.


What is Truly Efficient? Understanding Stratified Random Sample:

What is Truly Efficient?  Understanding Stratified Random Sample:

We have discussed several times the efficiency of various techniques for selecting a simple random sample from an expansive dataset. With PROC SURVEYSELECT will do the job easily…

proc surveyselect data=large out=sample
	 method=srs   /* simple random sample */
	 rate=.01;   /* 1% sample rate       */

However, let us assume that our data includes a STATE variable, and one would want to guarantee that a random sample includes the precise proportion of observations from each of the states of America.


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.


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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Data Preparation using SAS

Data Preparation using SAS

Before doing any data analysis, there are tasks which are critical to the success of the data analysis project. That critical task is known as data preparation. You may have heard that in the last years the data production is expanding at an astonishing pace. Experts now point to a 4300% increase in annual data generation by 2020. This can be due to the switch from analog to digital technologies and the rapid increase in data generation by individuals and corporations alike. The most of the data generated in the last few years are unstructured.