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Data Cleansing using Python

Data Cleansing using Python

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Data Cleansing using Python Exam

Python for Data Cleaning is all about improving the quality of data so it’s ready for business insights and decision-making. Many datasets contain errors, empty spaces, duplicates, or incorrect details. Python’s libraries provide automated and efficient ways to fix these problems, making the data neat and consistent.
Think of it as polishing raw material before turning it into a product. Python acts like a toolkit that removes errors, fills gaps, and makes sure the data is ready for deeper analysis. Clean and organized data is the backbone of reliable analytics and is crucial for anyone working with numbers, trends, or predictions.


Who should take the Exam?

This exam is ideal for:

  • Data Analysts
  • Data Engineers
  • Business Intelligence Analysts
  • Junior Data Scientists
  • Database Administrators
  • Students interested in analytics careers

Skills Required

  • Python variables, loops, functions.
  • Data handling and quality management
  • Logical and problem-solving mindset

Course Outline

  • Domain 1 - Introduction to Data Cleansing
  • Domain 2 - Python Basics Refresher
  • Domain 3 - Working with Data in Python
  • Domain 4 - Handling Missing Data
  • Domain 5 - Dealing with Duplicates and Errors
  • Domain 6 - Data Formatting and Standardization
  • Domain 7 - Advanced Data Cleaning

 

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Data Cleansing using Python FAQs

Clean data improves the accuracy of reports, analytics, and AI models.

It’s the process of fixing errors, missing values, and inconsistencies in data using Python tools.

Pandas, NumPy, and sometimes libraries like OpenPyXL or Regex.

Basic Python knowledge helps, but beginners can learn along the way.

Analysts, data engineers, students, and anyone working with datasets.

Real-world cases like cleaning sales, finance, and social media datasets.

Python automates tasks, handles bigger data, and ensures consistency.

Yes, clean data is the foundation of reliable data science projects.

Yes, with Pandas and NumPy, it works efficiently on big datasets.

Yes, you’ll cover cleaning for numeric, text, and date-time fields.