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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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Business Vs Demographic Vs Product Analytics

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List of top analytics areas and their differences



1. Analytics in Business

  • Advertising Analytics
  • Brand Analytics
  • Promotion Analytics
  • Business-to-business marketing Analytics
  • Social Media Analytics
  • Tracking Studies

2. Demographic Analytics

  • Consumer Analytics
  • Concept Testing Data Mining
  • Customer Satisfaction Study Analytics
  • Demographic Analytics
  • Employee Satisfaction Analysis
  • Text Mining
  • Ethnographic Analytics
  • Media Testing
  • Opinion Polling and Predictive Analytics
  • Usage & Attitude Studies
  • Segmentation Analytics
  • Semiotic and Cultural Analysis

3. Product Analytics

  • Packaging and Design Effectiveness Analytics
  • New Product Development
  • Pricing Studies
  • Product Testing
  • Scenario Planning 

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