Data Cleansing using Python Practice Exam

Data Cleansing using Python Practice Exam

Data Cleansing using Python means fixing and preparing raw data so it can be used for analysis. Real-world data is often messy—it may have missing values, duplicates, spelling mistakes, or wrong formats. Python has powerful libraries like Pandas and NumPy that make it easy to clean, organize, and standardize data. With these tools, even large datasets can be transformed into accurate and usable information.
In simpler words, data cleansing with Python is like tidying up a messy room before inviting guests. Just like you sort, remove clutter, and arrange things neatly, Python helps clean up data so it becomes reliable for reports, analysis, and machine learning. Clean data ensures better decisions, more accurate insights, and improved performance of analytics and AI models.

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

Knowledge Gained

• Identifying and fixing missing or incorrect data
• Removing duplicates and inconsistencies
• Handling text, numeric, and date data cleaning
• Using Pandas and NumPy for efficient cleaning
• Preparing clean datasets for analytics and machine learning

Course Outline

The Data Cleansing using Python Exam covers the following topics - 

1. Introduction to Data Cleansing

• Importance of data quality
• Role of Python in data cleaning

2. Python Basics Refresher

• Variables, data types, and operators
• Lists, dictionaries, and loops
• Functions for modular programming

3. Working with Data in Python

• Reading CSV, Excel, and JSON files
• Introduction to Pandas DataFrames
• Basic data inspection techniques

4. Handling Missing Data

• Detecting null values
• Filling missing values
• Dropping incomplete rows/columns

5. Dealing with Duplicates and Errors

• Identifying duplicate records
• Removing duplicates
• Detecting and correcting errors

6. Data Formatting and Standardization

• Cleaning text data (trimming, case conversion, removing symbols)
• Formatting date and time fields
• Handling categorical data

7. Advanced Data Cleaning

• Outlier detection and handling
• Data normalization and scaling
• Data transformation techniques