Category Archive: Sas course

Interactive Data Discovery and Predictive Analytics: Extract Useful Knowledge from Data

Interactive-Data-Discovery-and-Predictive-Analytics-Extract-Useful-Knowledge-from-Data

Impressive predictive analytics coupled with interactive data discovery technology enable rational SAS analysts to distinguish pertinent trends and interactions in datasets, and pan out questions from all dimensions. This smashing concoction of technologies also allows business users to exchange ideas with pundits, to create, modify and pick the best predictive models, constructively.

 

 

A comprehensive SAS solution might be the key to empower users in taking better business decisions, without wasting much time. This kind of interactive solution must involve ceaseless communication, giving enough room to even non-technical users to explore data visually, develop analytic models, and share fruitful results.  

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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 Many Category 5 Hurricanes Have We Had in the Atlantic?

With Hurricane Irma battering the pretty state of Florida while ripping through the Caribbean like a mammoth buzzsaw blade, we start wondering how often such rare Category 5 hurricanes occur. We know hurricanes of such magnitude are rare, but how much rare?

According to Wikipedia page information, hurricanes having wind speed greater than or equal to 157 mph are termed as category 5 hurricanes, enough to wreak havoc around. Using SAS Analytics, let’s start digging some data to unravel how many hurricanes of such great magnitude have hit the Atlantic coastal towns and cities with such dangerous wind speeds..

After indulging in a bit of research work, we came across weather.unisys.com website that contains exhaustive data about all the past hurricanes formed out of Atlantic. It turned out to be a good repository of data – we jotted down a bit of code and parsed the data into SAS data set. Next, we marked all the Atlantic hurricane paths on a map, and highlighted the line segments in bright red, where the wind speed fell under the Category 5 tab. So, come know how often they have taken place, along with their accurate position.

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Hit the above image to view the full-size interactive version of the map with HTML mouse-over text displaying the hurricane names in red for category 5 descriptions.

Take a look at the technical details of the code we used to draw the map:

  1. The map is created using SAS/Graph Proc GMap.
  2. We projected the map with the help of Proc GProject. Followed to that, we saved the projection parameters using the brand new Parmout=option. It was only then that we can project the hurricane paths individually, using GProject’s parmin=option. (Before the innovation of parmout/parmin parameters, we used to combine the map and hurricane paths, compile and project them together, and then divide the results into two separate datasets – of course the new functionality eases the things out).
  3. The paths of hurricane were plotted using regular ‘move’ and ‘draw’ Annotate functions.
  4. We first plotted the land areas (choropleth map), then covered (annotated) the hurricane tracks (while doing so, make sure the red lines lie on top for better visibility), and finally overlaid the country border contours on top again so as to make them prominent.
  5. As lines are incompatible with mouse-over text, we annotated circles using mouse-over text along the red hurricane paths. We outlined these circles at the very beginning (using when=’b’), hence they would become invisible later.

Have a look at the table we presented below. The table comprises of 34 Category 5 Atlantic hurricanes, derived from 150 years of data. You can also run your eyes through a snapshot image – click on the image to see the entire interactive table. And if you are interested in knowing more, hit each hurricane name and ask Google to give you information.

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Nota Bene: There might be some hurricanes under Category 5 domain we missed out. Kindly excuse us there, but we think we have nicely hit our main point of discussion. If you have anything to say us, scroll down and comment!

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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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INTCK and INTNX: All about SAS Dates and Computing Intervals between Dates

INTCK-and-INTNX
 

The INTCK and INTNX functions in SAS helps you compute the time between events. This technical blog is based on the timeline of living US presidents, sourced from a Wikipedia table. The table data shows the number of years and days between events.

 

So, let’s start.

 

LivingPresidents2

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The ABC of Summary Statistics and T Tests in SAS

The ABC of Summary Statistics and T Tests in SAS
 

Getting introduced to statistics for SAS training? Then, you must know how to create summary statistics (such as sample size, mean, and standard deviation) to test hypotheses and to figure confidence intervals. In this blog, we will show you how to furnish summary statistics (instead of raw data) to PROC TTEST in SAS, how to develop a data set that includes summary statistics and how to run PROC TTEST to calculate a two-sample or one-sample t test for the mean.

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Predictive Analytics: In conversation with Adam Bataran, Managing Director of GTM Global Salesforce Platforms at Bluewolf

To discuss about Predictive Analytics, we have Adam Bataran, Managing Director of GTM Global Salesforce Platforms at Bluewolf with us.

 

Follow the answers Mr. Bataran pitches to understand the entire concept better.

 
Predictive Analytics: In conversation with Adam Bataran, Managing Director of GTM Global Salesforce Platforms at Bluewolf
 

The question: What does predictive analytics mean and what value it imparts to the businesses today?

 

The answer: Predictive Analytics functions by implementing data, machine learning techniques and statistical algorithms to predict the future business outcomes and trends, based on past data and figures. It involves a number of distinct but advanced analytics disciplines and technologies – from deep data mining techniques and statistical analysis to predictive modeling and machine learning answers the most sought after question, “what will happen next?” or “how the customers will react to this?”.

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New and Improved Data Pane in SAS Visual Analytics Now Goes Painless

New-and-Improved-Data-Pane-in-SAS--Visual-Analytics-Now-Goes-Painless
 

It seems some good news is waiting for you – honing your data for effective reports are easier now with the 8.1 release of SAS Visual Analytics. In this technical blog, we will understand the structure of data pane, how it exhibits data from an active data source, and a handful number of tasks, which you might want to perform – like viewing measure details, adjusting data item properties and fabricating geographic data items, custom categories and hierarchies.

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Trends to Watch Out – Global Self-service Business Intelligence (BI) Market 2017

Gartner says – By 2020, the global BI and Analytics market is expected to flourish to USD 22.8 billion.

 

Trends to Watch Out - Global Self-service Business Intelligence (BI) Market 2017

 

The Global Self-Service Business Intelligence (BI) Market Research Report 2017 provides a comprehensive, detailed analysis of Self-Service BI industry, including the present Self-Service BI market trends and norms. It mainly focuses on the market of big continents, like North America, Europe and Asia, coupled with countries like Germany, US, China and Japan.

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Using Predictive Modelling to Determine How Well Michael Jordan Performed Under Pressure

Using Predictive Modelling to Determine How Well Michael Jordan Performed Under Pressure

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