Data Science
Learn how to actually DO a data science project from start to finish: frame a question, load and clean messy data, explore it, engineer features, split into train and test sets, build a baseline model, evaluate it honestly, avoid overfitting and data leakage, and communicate your findings. The complete workflow, with real runnable Python.
Every lesson runs real code in your browser, checked the moment you submit. No installing anything, no setup, no waiting.
Start Data Science freeWhat this course covers
- What Is Data Science?
- Asking the Right Question
- Kinds of Data
- Loading Data into a DataFrame
- Cleaning Messy Data
- Exploratory Data Analysis (EDA)
- Summary Statistics That Matter
- Distributions and Outliers
- Relationships and Correlation
- Feature Engineering
- Encoding Categorical Data
- Scaling and Normalizing Features
- Splitting Data: Train and Test
- Cross-Validation: Smarter Model Testing
- A Baseline Model
- Evaluating a Model
- Overfitting and Generalization
- Data Ethics: Bias, Fairness, and Responsibility
- Data Leakage: The Silent Killer
- Communicating Your Findings
- Capstone: An End-to-End Mini Project
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