Objective
Develop practical skills for preparing, cleaning, and exploring datasets.
Topics Covered
- Data Cleaning & Preprocessing
- Removing duplicates and correcting inconsistencies
- Handling Missing Data & Outliers
- Improving dataset reliability and quality
- Data Transformation & Feature Engineering
- Preparing data for analysis and modeling
- Exploratory Data Analysis (EDA)
- Understanding patterns, distributions, and relationships
- Descriptive Statistics & Summarization
- Mean, median, variance, and data interpretation basics
Activity
Clean and explore a real-world dataset using analytical tools and techniques.
