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Python Set Operations Explained: From Theory to Real-Time Applications

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A  set  in Python is an unordered collection of unique elements. It is useful when storing distinct values and performing operations like union, intersection, or difference. Real-Time Example: Removing Duplicate Customer Emails in a Marketing Campaign Imagine you are working on an email marketing campaign for your company. You have a list of customer emails, but some are duplicated. Using a set , you can remove duplicates efficiently before sending emails. Code Example: # List of customer emails (some duplicates) customer_emails = [ "alice@example.com" , "bob@example.com" , "charlie@example.com" , "alice@example.com" , "david@example.com" , "bob@example.com" ] # Convert list to a set to remove duplicates unique_emails = set (customer_emails) # Convert back to a list (if needed) unique_email_list = list (unique_emails) # Print the unique emails print ( "Unique customer emails:" , unique_email_list) Ou...

2 Top Skills You Need For E-commerce

The following skills are must for every data analytics engineer to be successful in e-commerce companies. Many software engineers are struggling to get this information. I am giving here both technical and general skills.

skills e-commerce

1# Technical Skills for Analytics Career

What skills they need to be successful in their analytics career. The skills required to get an entry into the Analytics job are here for your reference.

I have told in my previous posts that there are many branches in analytics. You need to apply domesticated techniques to extract actionable knowledge.

2# General Skills

I have selected the following mindset and skills that you need to get a job in data analytics. These are proven skills. Set a clear goal to acquire these skills.
  1. Strong interpersonal, oral and written communication and presentation skills; 
  2. Ability to communicate complex findings and ideas in plain language 
  3. Being able to work in teams towards a shared goal; 
  4. Ability to change direction quickly based on data analysis; 
  5. Enjoying discovering and solving problems; 
  6. Proactively seeking clarification of requirements and direction; take responsibility when needed; 
  7. Being able to work in a stressful situation when insights in (new) data sets are required quickly.

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