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 exploratoryanalysis, 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.
Statistics Using Python is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, aiming to assist data scientists in their daily endeavors. While the book casts a wide net to cater to a broad audience, it ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs.
Features:
- Provides Python code samples to ensure readers can immediately apply what they learn
- Covers everything from basic data handling to advanced statistical concepts
- Features downloadable companion files with code samples and figures Includes two appendices
- An Introduction to Python
- Introduction to Pandas as refresher material
Target Audience:
This book primarily targets data scientists and enthusiasts who have a foundational understanding of statistics but wish to delve deeper. Whether you are a beginner wanting to grasp the basics or someone with intermediate knowledge aiming to broaden your statistical horizon, this book offers a structured approach to various concepts. The interleaving of foundational and advanced topics ensures readers can pace their learning according to their comfort and familiarity.
Author(s): Oswald Campesato
Publisher: Mercury Learning and Information
Year: 2023
Language: English
Pages: 273
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
Summary
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
Summary
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()
Summary
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
Summary
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)
Summary
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
Summary
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
Summary
Index
