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Chapter 1 - Inroduction to This Book - Debugged Codes
What is statistics and why learn it? - Statistics, data science, machine learning, etc. - Target audience - Prerequisites - Using the code with this book - AI assistance
Chapter 2 - What are (is?) data? - Debugged Codes
Is "data" singular or plural? - Where do data come from, what do they mean? - What do data look like? - Limitations of data - Accuracy, precision, resolution, range - Data types - From anecdotes to populations - Data management - The ethics of making up data
Chapter 3 - Visualizing data - Debugged Codes
Why visualize data? - How to visualize data - Bar plots - Pie charts - Box plots - Histograms - Lines vs. bars in a histogram - Violin plots - Linear vs. logarithmic axis scaling - Discretizing continuous data - Radial plots - Color
Chapter 4 - Descriptive statistics - Debugged Codes
Descriptive vs. inferential statistics - Data distributions - Central tendency - Measures of dispersion - Interquartile range (IQR) - QQ plots - Statistical "moments" - Histograms part 2: Number of bins
Chapter 5 - Simulating Data - Debugged Codes
Why simulate data? - Random data from distributions - Random elements of a set - Random permutations - Reproducing randomness - Running experiments with random numbers - The amazing world of data-simulations - Finding publicly available real datasets
Notes from the book-1
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Chapter 6 - Transformations - Debugged Codes
What, why, and how of data transformations - Z-score standardization - Min-max normalization - Z-scoring vs. min-max scaling - Percent change - Nonlinear data transformations - Interpreting transformed data
Chapter 7 - Data Quality Matters - Debugged Codes
Data quality influences data-driven decisions - Data cleaning phases - Assessing data quality - Improving data quality through transformations - What are outliers? - Identifying outliers - Analysis-based solutions to outliers - Missing data
Chapter 8 - Probability Theory - Debugged Codes
From descriptive to inferential statistics - What is probability? - Probability vs. proportion - Computing probabilities - Probability functions, mass, and density - Cumulative distribution function (cdf) - Expected value - Softmax
Chapter 9 - Sampling and Distribution - Debugged Codes
Sampling variability and its annoyances - Creating sample estimate distributions - Standard error of the mean - Random and representative sampling - The Law of Large Numbers - The Central Limit Theorem
Chapter 10 - Hypothesis Testing - Debugged Codes
Hypotheses - IVs, DVs, models, and other stats lingo - Can you prove a hypothesis? - Sample distributions under H0 and HA - Where do H0 distributions come from? - P-values: definition and misinterpretations - P-values and significance categorization - Type-I and Type-II errors - Various interpretations of "significant" - Multiple comparisons - Degrees of freedom
Notes from the book-2
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