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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
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Career Opportunities to Write Algorithms

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Many participants in the Analytics seminar expressed opportunity in preparing algorithms for predictive analytics. You Need Algorithms Why Using these algorithms, businesses can make better data-driven decisions by extracting actionable patterns and detailed statistics from large, often cumbersome data sets. Many business people small to big expecting some kind of algorithms. So that they can save their precious time in predictive analytics. As per IBM What are Good Benefits of Right  Algorithm Transform data into predictive insights to guide front-line decisions and interactions.  Predict what customers want and will do next to increase profitability and retention.  Maximize the productivity of your people, processes and assets.  Detect and prevent threats and fraud before they affect your organization.  Measure the social media impact of your products, services and marketing campaigns.  Perform statistical analysis including regression analysis, cluster analysis and
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