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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.

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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.

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The Basics Of The Banking Business And Lending Risks:

The Basics Of The Banking Business And Lending Risks:
 

Banks, as financial institutions, play an important role in the economic development of a nation. The primary function of banks had been to channelize the funds appropriately and efficiently in the economy. Households deposit cash in the banks, which the latter lends out to those businesses and households who has a requirement for credit. The credit lent out to businesses is known as commercial credit(Asset Backed Loans, Cash flow Loans, Factoring Loans, Franchisee Finance, Equipment Finance) and those lent out to the households is known as retail credit(Credit Cards, Personal Loans, Vehicle Loans, Mortgages etc.). Figure1 below shows the important interlinkages between the banking sector and the different segments of the economy:

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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       */
run;

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.

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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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Historians Make Use of Predictive Modeling

Predictive modeling figures at the top of the list of new techniques put in to use by researchers in order to make out key archeological sites. The methodology used is not that complex. It makes predictions on the location of archeological sites having for its basis the qualities that are common to the sites already known. And the best news is that it works like a charm. A group of archeologists working in the company Logan Simpson which operates out of Utah discovered no less than 19 individual archeological sites containing many biface blades as well as stone points in addition to other artifacts that belong to the Paleoarchaic Period which ranges from 7,000 to 12,000 years ago.

 

Predictive Modelling

 

The location of the site is about 160 km or 100 miles from Las Vegas, Nevada. The group of researchers also came across lakes and streams that disappeared long before. According to archeologists the sites were perhaps put into used by a number of groups of gatherers and hunters in the ancient times. The sites are scattered widely and also are scarce and could herald an understanding of the human activity that took place throughout the length and breadth of the Great Basin as a warmer climate prevailed after the end of the Ice Age. Their remoteness ensured that they remain unfound when traditional methods are employed.

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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.

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