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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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SAS career these are different job roles

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What is SAS?
Originating in 1966 at North Carolina State University, SAS is a proprietary 4th generation programming language specifically designed for data analysis. Data analysis includes data management, ETL, descriptive statistics, plots and graphics, inferential statistics, data mining, forecasting, etc.. In addition to the SAS programming language, SAS Institute, its parent company, also sells solutions built on top of the SAS language.

 SAS career
Many people assumptions about SAS: Some people confuse SAS with the common database query language called “SQL”, thinking that both languages manage data in some way. While SAS may be used for database querying (reading/writing data to databases), this is only a tiny fraction of SAS’s capabilities. To give some scope, the entire SQL language is available in SAS as but one “procedure” (think of a procedure as a bundle of functionality).
SKILL Set: What It Is: Pronounced “sass,” this software helps workers perform a variety of tasks, including business forecasting, project management and statistical analysis. 
Different roles in SAS Jobs:
  1. Business analyst
  2. Clinical data programmer
  3. Data Analyst
  4. Data Quality Steward
  5. Data Scientist
  6. Data warehouse architect
  7. Database administrator
  8. Database programmer
  9. Developer
  10. ETL specialist
  11. Financial analyst
  12. IT Manager
  13. Marketing analyst
  14. Platform Administrator
  15. Programmer
  16. Project Manager
  17. Quality analyst
  18. Report programmer
  19. Reporting Analyst
  20. Securities analyst
  21. Statistician
  22. Statistics programmer
  23. Systems/network programmer

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