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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Beginner's Tutorial on SaS Visual Analytics

SAS visual analytics is a completely new architecture from SAS. It has the capability to manage large amounts of data and bring it into memory to analyze it, explore it and publish reports. 
sas visual analytics
Although the data amounts are massive — up to 1.1 billion rows of data, the SAS LASR Analytic Server, to use its full name, was designed to be intuitive to users without an advanced degree in computer science.
pay scale for SAS visual analytics
A report from Simply hired.
All about SAS analytics Server - The SAS Analytic Server begins with an eight-blade server with 96 processor cores, 768 gigabytes memory and 4.8 terabytes (TB) of disk storage. 

The upper end of the reference configurations is 96 blades with 1,152 cores, 9.2 TB memory and 57.6 TB of disk storage, enough disk space to store the entire Library of Congress six times.
Where to Learn SAS Visual Analytics

Also read:  

The real SAS Visual analytics benefits

    1. The speed of in-memory architecture offers tremendous benefit. Organisations can explore huge data volumes and get answers to critical questions in near-real time. SAS Visual Analytics offers a double bonus: the speed of in-memory analytics plus self-service eliminates the traditional wait for IT-generated reports.
    2. Businesses today must base decisions on insight gleaned from data, and that process needs to be close to instantaneous.
    3. Despite being user-friendly, the server has been developed to make it easy for IT to manage the data and secure it without sacrificing usability, Guard said. It includes a visual analytics explorer for ad hoc analysis and discovery, he added.
    4. SAS Visual Analytics helps business users to visually explore data on their own. But it goes well beyond traditional query and reporting.
    5. Running on low-cost, industry-standard blade servers, its high-performance in-memory architecture delivers answers in seconds or minutes instead of hours or days.

    Where SAS differs

    SAS analytics differ from many business intelligence (BI) solutions which simply move data from a SQL database into memory. That does not support regressions or logistics models becase those capabilities are not built into databases.

    In banking, analysts may develop hundreds of models a year; with SAS they will be able to do it 10 to 20 times faster. The importance of changing models rapidly is incredibly important in the banking industry.

    A demo on SAS visual analytics:

    The computerWorld says-SAS also plans to broaden its user base by making its software more appealing beyond computer statisticians and data scientists. 

    To this end, the company has paired its data exploration software, called SAS Visual Analytics, with its software for developing predictive models, called SAS Visual Statistics. 

    The pairing can allow non-data scientists, such as line of business analysts and risk managers, to predict future trends based on current data.

    How companies will benefit

    1. With SAS Analytic Server companies can solve problems they had never dealt with before because they it offers speed of analysis at a large scale. Users don’t have to analyze samples; they can look at everything.
    2. AS Visual Analytics will let us quickly dig into our big data to uncover opportunities, and in time, to fully exploit them.”The SAS LASR Analytic Server, uses Hadoop (embedded Hadoop Distributed File System) as local storage at the server for fault tolerance. 
    3. SAS LASR Analytic Server has been tested on billions of rows of data and is extremely scalable, bypassing the known column limitations of many relational database management systems (RDBMS).

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