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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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Understand Data power why quality everyone wants

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Information and data quality is new service work for data intense companies. I have seen not only in Analytics projects but in Mainframe projects, there is the Data Quality team. How incorrect data impact on us Information quality problems and their impact are all around us: A customer does not receive an order because of incorrect shipping information. Products are sold below cost because of wrong discount rates. A manufacturing line is stopped because parts were not ordered—the result of inaccurate inventory information. A well-known U.S. senator is stopped at an airport (twice) because his name is on a government "Do not fly" list. Many communities cannot run an election with results that people trust. Financial reform has created new legislation such as Sarbanes—Oxley.  Incorrect data leads to many problems. The role of Data Science is to use quality data for effective decisions. What is information Information is not simply data, strings of numbers, lis
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Poor Data Quality New Job Roles in Data Quality

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Data quality is on rising and important to organizations today. Since in Experian research it has found that poor data quality causing losses to the companies. Experian research suggests companies in the UK, the US, Australia, and western Europe have poorer quality data this year than last. The credit information company’s 2015 Global Data Quality Research among 1,239 organizations found a dramatic lack of data quality “ownership”, and 29% of respondents were still cleaning their data by hand. The number of organizations that suspect inaccurate data has jumped from 86% in 2014 to 92%. Also, respondents reckoned 26% of their data to be wrong, up from 22% in 2014 and 17% in 2013. Some 23% of respondents said this meant lost sales, up from 19% in 2013. Boris Huard, managing director of Experian Data Quality, said: “Getting your data strategy right is vital if you want to be successful in this consumer-driven, digitalized age.  It is encouraging that companies are increasing
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