Statistics Using Python.

Format:

Book

Author/Creator:

Campesato, Oswald.

Language:

English

Subjects (All):

Python (Computer program language)--Statistical methods.

Python (Computer program language).

Physical Description:

1 online resource (273 pages)

Edition:

1st ed.

Place of Publication:

Bloomfield : Mercury Learning & Information, 2023.

Summary:

This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors. The ability to extract meaningful insights from data requires a deep understanding of statistics. The book ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs. Topics include working with data and exploratory analysis, the basics of probability, descriptive and inferential statistics and their applications, metrics for data analysis, probability distributions, hypothesis testing, and more. Appendices on Python and Pandas have been included. From foundational Python concepts to the intricacies of statistics, this book serves as a comprehensive resource for both beginners and seasoned professionals.

Contents:

Front Cover

Half-Title Page

LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY

Title Page

Copyright Page

Dedication

Contents

Preface

CHAPTER 1: Working with Data

What is Data Literacy?

Exploratory Data Analysis (EDA)

Dealing with Data: What Can Go Wrong?

An Explanation of Data Types

Working with Data Types

What is Drift?

Discrete Data Versus Continuous Data

Binning Data Values

Correlation

Working with Synthetic Data

Summary

CHAPTER 2: Introduction to Probability

What is Set Theory?

Open, Closed, Compact, and Convex Sets (Optional)

Concepts in Probability

Set Theory and Probability

Coin Tossing Probabilities

Dice Tossing Probabilities

Card Drawing Probabilities

Container-Based Probabilities

Children-Related Probabilities

CHAPTER 3: Introduction to Statistics

Introduction to Statistics

Basic Concepts in Statistics

The Variance and Standard Deviation

The Moments of a Function (Optional)

Random Variables

Multiple Random Variables

Sampling Techniques for a Population

What is Bias?

Two Important Results in Probability

CHAPTER 4: Metrics in Statistics

The Confusion Matrix

The ROC Curve and AUC Curve

The sklearn.metrics Module (Optional)

Statistical Metrics for Categorical Data

Metrics for Continuous Data

MAE, MSE, and RMSE

Approximating Linear Data with np.linspace()

CHAPTER 5: Probability Distributions

PDF, CDF, and PMF

Two Types of Probability Distributions

Discrete Probability Distributions

Continuous Probability Distributions

Advanced Probability Functions

Non-Gaussian Distributions

The Best-Fitting Distribution for Data

CHAPTER 6: Hypothesis Testing

What is Hypothesis Testing?

Components of Hypothesis Testing

Test Statistics.

Working with p-values

Working with Alpha Values

Point Estimation, Confidence Level, and Confidence Intervals

What is A/B Testing?

The Lifespan of an A/B Test

Maximum Likelihood Estimation (MLE)

Appendix A: Introduction to Python

Tools for Python

Python Installation

Setting the PATH Environment Variable (Windows Only)

Launching Python on Your Machine

Identifiers

Lines, Indentation, and Multi-Line Statements

Quotation Marks and Comments

Saving Your Code in a Module

Some Standard Modules

The help() and dir() Functions

Compile Time and Runtime Code Checking

Simple Data Types

Working with Numbers

Working with Fractions

Unicode and UTF-8

Working with Strings

Slicing and Splicing Strings

Search and Replace a String in Other Strings

Remove Leading and Trailing Characters

Printing Text without New Line Characters

Text Alignment

Working with Dates

Exception Handling

Handling User Input

Python and Emojis (Optional)

Command-Line Arguments

Appendix B: Introduction to Pandas

What is Pandas?

A Pandas Data Frame with a NumPy Example

Describing a Pandas Data Frame

Boolean Data Frames

Data Frames and Random Numbers

Reading CSV Files in Pandas

The loc() and iloc() Methods

Converting Categorical Data to Numeric Data

Matching and Splitting Strings

Converting Strings to Dates

Working with Date Ranges

Detecting Missing Dates

Interpolating Missing Dates

Other Operations with Dates

Merging and Splitting Columns in Pandas

Reading HTML Web Pages

Saving a Pandas Data Frame as an HTML Web Page

Index.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781683928782

1683928784

9781683928799

1683928792

OCLC:

1414458397