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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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Analyst and Data Scientist Career Options

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 The following Skillset needed to succeed as Analyst or Data Scientist career.

DTD Frame work: Understanding and hands on experience of Data to decisions frame work.

SQL Skills: Experience to pull data from multiple sources. Hands on experience of Teradata, Oracle and Hadoop skills also useful

Basic Statistics Techniques: Hands-on experience with basic statistical techniques: Profiling, Correlation analysis, Trend analysis, Sizing/Estimation, Segmentation

Business Side Experience: Working with all business stake holders. Communication and influencing others.

Advanced statistics: Hands-on comfort with advance techniques: Time Series, Predictive Analytics – Regression and Decision Tree, Segmentation (K-means clustering) and Text Analytics (optional)

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