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Data Analysis with Python

Use NumPy, pandas, Matplotlib, and scikit-learn to build a complete workflow from data preparation, transformation, and visualization to regression, classification, clustering, and time-series analysis.

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Table of Contents

Chapter 1: The Data Analysis Workflow

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1.1 Questions, Evidence, and Decisions
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1.2 The Data Analysis Lifecycle
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1.3 Observations, Variables, and Datasets
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1.4 Setting Up Python and Jupyter

Chapter 2: NumPy Foundations

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2.1 Arrays, Shapes, Axes, and Data Types
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2.2 Indexing, Slicing, and Boolean Masks
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2.3 Vectorization and Broadcasting
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2.4 Aggregation, Missing Values, and Numerical Stability

Chapter 3: NumPy for Practical Analysis

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3.1 Reshaping, Stacking, and Splitting Arrays
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3.2 Sorting, Searching, and Conditional Selection
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3.3 Random Sampling and Reproducibility
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3.4 Performance, Memory, Copies, and Views

Chapter 4: pandas Foundations

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4.1 Series, DataFrame, Index, and Schema
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4.2 Selecting Rows and Columns Safely
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4.3 Filtering, Sorting, and Assigning Values
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4.4 Importing and Exporting Tabular Data

Chapter 5: Cleaning Real-World Data

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5.1 Understanding and Handling Missing Data
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5.2 Duplicates and Entity Resolution
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5.3 Strings, Categories, Dates, and Units
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5.4 Identifying and Handling Outliers

Chapter 6: Transforming and Reshaping Data

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6.1 Vectorized Column Transformations
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6.2 Using map and apply Correctly
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6.3 Binning, Ranking, and Categorical Encoding
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6.4 Reshaping with melt, pivot, and stack

Chapter 7: Visualization with Matplotlib

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7.1 Figures, Axes, and the Object-Oriented API
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7.2 Line, Bar, Scatter, and Distribution Plots
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7.3 Labels, Scales, Annotations, and Uncertainty
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7.4 Layouts, Styles, and Reusable Plotting Functions

Chapter 8: The Machine-Learning Workflow

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8.1 Prediction Problems and Model Families
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8.2 Features, Targets, and Preprocessing
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8.3 Training, Validation, and Test Sets
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8.4 Pipelines, Cross-Validation, and Data Leakage

Chapter 9: Regression Analysis

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9.1 Linear Regression Intuition and Assumptions
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9.2 Feature Encoding and Scaling
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9.3 MAE, RMSE, and R-Squared
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9.4 Regularization, Diagnostics, and Interpretation

Chapter 10: Classification Analysis

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10.1 Logistic Regression and Decision Boundaries
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10.2 Decision Trees and Ensemble Models
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10.3 Confusion Matrix, Precision, Recall, and F1
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10.4 Thresholds, Evaluation Curves, and Calibration

Chapter 11: Unsupervised Learning and Time Series

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11.1 Scaling, Distance, and Clustering
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11.2 Dimensionality Reduction with PCA
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11.3 Time Indexes, Resampling, and Rolling Windows
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11.4 Trend, Seasonality, Validation, and Forecasting Baselines

SUITABLE AUDIENCE

Learners comfortable with Python syntax, functions, collections, and file handling who want a structured introduction to data analysis and machine learning

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