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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]
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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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