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Best Practices for Handling Duplicate Elements in Python Lists

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Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions.  01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list:     if item not in unique_list:         unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All three methods will result in uni
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These 2 top skills you need to become an analyst

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Pools of master data present in repositories play a big role in data analytics. For example, data is already re-posited in data warehouses. Example, product data, customer data etc.
tech and soft skills

Big data needs mixed skills. For example - technical skills and some soft skills.

1# Technical Skills

One being able to administer software frameworks like:
  • Hadoop, 
  • expertise in databases like noSQL, 
  • Cassandra or HBase 
  • analytics programming languages and facilities like R or Pig.

2# Soft Kills

Ability of people to think broadly across the organization, to understand the bottom-line needs of the business, to know which analytics questions to pose to get to those bottom lines, and to measure and communicate results. Additional Technical Skills - SAS, Cognos

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