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   "source": "```{index} chapter topics\n```\n\n(chapter-topics)=\n\n# Chapter Topics\n\nThe chapters progress from Python fundamentals and data handling with NumPy and pandas to data visualization, simulation-based inferential statistics, and introductory machine learning. Preview, Lab, and Homework activities accompany each chapter.\n\n| Chapter | Topics |\n| --- | --- |\n| [1. Introduction](../01-intro/0100-data-science.ipynb) | What data science is; the data science process; data science careers and tools; programming languages and levels of abstraction; interpreted and compiled languages; expressions and statements; number systems and character encoding. |\n| [2. Python Basics](../02-python/0200-python.ipynb) | Input, output, and f-strings; variables and built-in data types; operators and precedence; conditionals and logical operators; `while` and `for` loops; lists, indexing, and slicing; dictionaries and counting; user-defined functions and scope. |\n| [3. NumPy Arrays](../03-numpy/0300-numpy.ipynb) | Creating arrays with `array`, `arange`, and `reshape`; array attributes; indexing and slicing 2-D arrays; vectorized operations and universal functions; aggregations along an axis; broadcasting; masking and fancy indexing; sorting and random number generation. |\n| [4. Pandas](../04-pandas/0400-pandas.ipynb) | Creating and indexing Series; reading CSV, Excel, HTML, and SQL data; creating and inspecting DataFrames; selecting data with `loc`, `iloc`, and Boolean indexing; adding and dropping rows and columns; detecting and handling missing data; combining datasets with `concat`, `merge`, and `join`; grouping and aggregation. |\n| [5. Visualization](../05-visualization/0500-visualization.ipynb) | Choosing a plotting library and chart type; the pandas `plot` method; line plots and histograms; scatter, bar, and box plots; area, KDE, and hexagonal bin plots; style sheets, color maps, and figure size. |\n| [6. Matplotlib](../06-matplotlib/0600-matplotlib.ipynb) | Stateful and object-oriented syntax; labels, titles, and legends; multiple plots with subplots; manual axes placement; figure size, resolution, and saving; `rcParams` and style sheets; axis ranges, colors, and line styles; scatter, bar, histogram, and box plots. |\n| [7. Seaborn](../07-seaborn/0700-seaborn.ipynb) | Comparing Seaborn with pandas and Matplotlib; tidy and wide data formats; figure-level and axes-level APIs; distribution, relational, and categorical plots; faceting with `FacetGrid`; pair plots, joint plots, and heatmaps; themes and styling. |\n| [8. Probability and Statistics](../08-prob-stats/0800-probability-statistics.ipynb) | Populations and samples; variables, observations, and distributions; descriptive statistics and measures of center; normal, binomial, and uniform distributions; the law of large numbers; sampling distributions; standard error and sample size. |\n| [9. Hypothesis Testing](../09-test-hypothesis/0900-hypothesis-testing.ipynb) | Assessing a model with data; choosing a test statistic; simulating a statistic under a model; distance between distributions; null and alternative hypotheses; p-values and conventional cut-offs; statistical bias and data quality. |\n| [10. Two Samples](../10-two-samples/1000-two-samples.ipynb) | Random sampling; A/B testing; test statistics for comparing two groups; permutation tests; p-values for two-sample tests; randomized controlled trials and causality. |\n| [11. Estimation](../11-estimation/1100-estimation.ipynb) | Percentiles and quartiles; populations, parameters, and estimates; bootstrap resampling; the bootstrap distribution of a sample median; bootstrap confidence intervals for medians, means, and proportions; limits of the bootstrap percentile method. |\n| [12. Regression](../12-regression/1200-regression.ipynb) | The correlation coefficient; association versus causation; standard units; the regression line and the regression effect; fitted values and slope; root mean squared error; least squares; residual diagnostics for nonlinearity and heteroscedasticity. |\n| [13. Multiple Regression](../13-multiple-regression/1300-multiple-regression.ipynb) | Machine-learning framing; validation and evaluation; exploratory data analysis; train/test split; training a linear regression model with scikit-learn; regression evaluation metrics; interpreting multiple linear regression; nearest neighbors for regression. |\n| [14. Classification](../14-classification/1400-classification.ipynb) | Classification problems; nearest neighbor classifiers; decision boundaries; k-nearest neighbors; training and test sets; classifier accuracy; distance between rows with multiple attributes; implementing a k-NN classifier. |\n| [15. Clustering](../15-clustering/1500-clustering.ipynb) | Clustering as a management tool; similarity, features, and scale; common clustering methods; the k-means workflow; choosing k with elbow and silhouette checks; describing and interpreting clusters. |\n\nSet up your environment with [Course Tooling](../appendices/0101-tooling.ipynb) and [Jupyter Setup](../appendices/jupyter/0200-intro-jupyter.ipynb) in the appendices before the first lab."
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