| Attribute | Detail |
|---|---|
| Format | Online, self-paced course |
| Level | Basic / Beginner |
| Duration | 2–3 Weeks |
| Certification | e-Certification |
| Fee | ₹199 / $20 |
| Tools | Python Data Wrangling Data Cleaning Data Transformation Datasets |
About the Data Wrangling with Python Course
The Data Wrangling with Python course is a free, beginner-friendly self-paced program designed to help learners understand how to clean, organize, and prepare raw data for analysis using Python.
In real-world scenarios, data is often incomplete, inconsistent, or unstructured. This course introduces simple techniques to handle missing values, correct errors, transform data formats, and prepare datasets for meaningful analysis. It is an ideal starting point for learners who want to build practical data skills before moving into data science or machine learning.
Program Highlights
• Free beginner-level data wrangling course
• Online self-paced learning format
• Simple explanation of data cleaning and preparation
• Covers missing data, filtering, and transformation basics
• Real-world examples using messy datasets
• Suitable for students and first-time learners
• e-Certification upon successful completion
Course Curriculum
Module 1: Introduction to Data Wrangling
- What is Data Wrangling?
- Why Data Cleaning is Important
- Raw Data vs Clean Data
- Applications in Data Science and Analytics
Module 2: Working with Data in Python
- Understanding Datasets: Rows and Columns
- Loading Data in Python
- Exploring Data Structure
- Basic Data Inspection
Module 3: Data Cleaning Basics
- Handling Missing Values
- Removing Duplicates
- Fixing Incorrect Data Formats
- Filtering and Selecting Data
Module 4: Data Transformation
- Sorting and Grouping Data
- Changing Data Types
- Reshaping and Combining Data
- Preparing Data for Analysis
Module 5: Applications and Next Steps
- Data Preparation for Analytics and ML
- Use Cases in Business and Research
- Career Path in Data Science
- Mini Practice Exercise
Tools, Techniques, or Platforms Covered
Python Data Wrangling Data Cleaning Data Transformation Datasets
Real-World Applications
- Cleaning real-world messy datasets
- Preparing data for machine learning models
- Organizing business and research data
- Handling missing and inconsistent values
- Supporting data-driven decision-making
Who Should Attend & Prerequisites
- This course is suitable for students, beginners, freshers, and professionals who want to learn how to prepare and clean data using Python.
- It is also useful for learners from engineering, computer science, business, research, and non-technical backgrounds interested in data.
Certification
