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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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The 5 Skills You need to Start Data Analytics Career

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Data analytics is the job role hot in demand in each organization. Digital skills such as Mobile development, Full-stack development, Data Science, and Cloud computing are successful because they are very user-friendly to the end-users.


skills in-demand

Digital devices enabled digital technologies to cause to generate more data. You need different kinds of tools to analyze data in different formats.

You need the right tools. Else you cannot predict the user's mind. User search data is the source of big retail markets. Based on these search words, they start selling the products.

The motto behind data analytics is to get the benefit all stakeholders. Let us take cloud computing the main advantage is cost-effectiveness and scalability.

5 Skills you need for data analyst job

  1. R Programming
  2. SAS
  3. Excel
  4. Tableau
  5. QlikView

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