Data Science Fundamentals
Learn data wrangling, EDA, feature engineering, and pipeline design. Bridge the gap between raw data and machine learning.
Level: Beginner · Category: Data Science · Estimated time: 5 hours
Prerequisites
- Python for AI
Lessons
- Data Acquisition & Formats — Loading data from CSV, JSON, SQL, APIs, and web scraping basics.
- Data Cleaning & Preprocessing — Handling missing values, duplicates, outliers, and data type conversions.
- Exploratory Data Analysis (EDA) — Statistical summaries, distributions, correlations, and visualization.
- Feature Engineering — Creating new features, encoding categoricals, scaling, and binning.
- Data Pipelines & Reproducibility — sklearn pipelines, versioning data, and reproducible workflows.
Topics covered
data-science, eda, feature-engineering, data-cleaning, pandas