Data Analysis – Intermediate Class

Class Outline:

Review of Basics:

1. Quick Recap:
   • Review key concepts from the beginner class.
   • Ensure a solid foundation for the intermediate topics.

Advanced Data Cleaning:

1. Handling Messy Data:
   • Dealing with inconsistent data formats.
   • Techniques for handling messy, real-world datasets.
2. Advanced Data Transformation:
   • Feature engineering.
   • Handling categorical variables (encoding, one-hot encoding).

Advanced Exploratory Data Analysis (EDA):

1. Advanced Visualization:
   • Seaborn and Plotly for interactive visualizations.
   • Advanced plotting techniques (e.g., heatmaps, violin plots).
2. Correlation and Dimensionality Reduction:
   • Understanding correlation matrices.
   • Principal Component Analysis (PCA) for dimensionality reduction.

Statistical Inference:

1. Advanced Hypothesis Testing:
   • One-way and Two-way ANOVA.
   • Multiple comparisons and corrections.
2. Regression Analysis:
   • Simple and multiple linear regression.
   • Logistic regression for classification problems.

Machine Learning Basics:

1. Introduction to Machine Learning:
   • Overview of supervised and unsupervised learning.
   • Bias-variance tradeoff.
2. Model Evaluation:
   • Cross-validation.
   • Metrics for regression and classification (e.g., RMSE, MAE, precision, recall).

Data Analysis Tools (Advanced):

1. Python Libraries:
   • Advanced usage of Pandas and NumPy.
   • Introduction to Scikit-Learn for machine learning

Practical Projects:

1. Hands-On Project:
   • Applying regression or classification to a real-world problem.
   • Tuning model parameters.
2. Data Storytelling:
   • Communicating findings effectively.
   • Creating compelling data-driven narratives.