Descriptive Statistics

Descriptive statistics are the first step in any data analysis pipeline. Before building complex machine learning models, you must understand the shape, center, and spread of your data. This module covers the fundamental techniques to summarize and visualize datasets effectively.

1. Learning Objectives

By the end of this module, you will be able to:

  1. Distinguish between Mean, Median, and Mode and choose the appropriate metric for skewed data.
  2. Quantify data variability using Variance, Standard Deviation, and Interquartile Range (IQR).
  3. Identify outliers using statistical methods and visualize them with Box Plots.
  4. Perform Exploratory Data Analysis (EDA) using Histograms and Scatter Plots to uncover hidden patterns.
  5. Implement these concepts in Python using NumPy, Pandas, and Matplotlib.

2. Module Contents

1. Central Tendency

Understand the “center” of your data. We explore the arithmetic mean, median, and mode, and demonstrate why the median is often more robust in real-world scenarios like analyzing API latency or salary distributions.

2. Spread & Outliers

Measure how “spread out” your data is. Learn about Variance and Standard Deviation for normal distributions, and why the Interquartile Range (IQR) is critical for detecting anomalies and outliers in noisy datasets.

3. EDA Techniques

Master the art of visual storytelling. We cover Histograms for distribution analysis, Box Plots for summary statistics, and Scatter Plots for correlation. Includes a deep dive into Anscombe’s Quartet to prove why summary statistics alone are dangerous.

Review & Cheat Sheet

Review the key takeaways, test your knowledge with interactive flashcards, and grab a quick reference cheat sheet for all the formulas and Python code snippets covered in this module.

Module Chapters

Chapter 02

Spread and Outliers

Knowing the center (mean/median) of your data is only half the story. Two datasets can have the exact same mean but look completely different.

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