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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

SAS- Get the Right Training, Go get the Job

 sas career options

sas career options
Many people think analytics is about gathering data using software tools and creating dashboards and reports. However, analytics is much more. Analytics goes beyond data; its primary goal is to enable business decisions based on that data. This involves working with stakeholders to understand the gaps in the business and using this knowledge as a guide to manipulate data, derive useful insights, and make recommendations – all key actions to increase revenue and lower costs.
Wherever you sit in your organization, what’s most important is the bottom line. And so whether you lead business or IT units or are in the trenches, the analytics profession has likely crossed your mind. What does it entail? Who are true analysts? How does one become an analyst?
Those of you specifically in a data management, data warehousing or business intelligence role may wonder how to further develop your analytics career. On the surface, an “analytics career” can be quite broadly defined, and the transition to it can seem very confusing. However, the structured approach we describe in this article will make it easy to choose your path – and give managers and leaders an appreciation for the developmental steps to success.

Don’t expect to learn analytics from blogs and social chatter. There is a lot of information published online. Do your own due diligence.
Don’t view conferences as a solution for training

Analytics is hot field- many jobs available. To land a great analytics job, consider networking via LinkedIn. Use LinkedIn Jobs as well as LinkedIn analytics groups and highlight your analytics skills using tags. Also consider key job portals, such as Craigslist, icrunchdata, Indeed, Dice and Monster.

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