An Introduction to Data Science With Python (eBook)

Jeffrey S. Saltz, Jeffrey Morgan Stanton (Autoren)

eBook Download: EPUB

2024
458 Seiten
Sage Publications, Inc (Verlag)
978-1-0718-5066-4 (ISBN)

Inhalt

Introduction - Data Science, Many Skills
What is Data Science?
The Steps in Doing Data Science
The Skills Needed to Do Data Science
Identifying Data Problems Through Stories
Case: Overall Context and Desired Actionable Insight
Chapter 1 - Begin at the Beginning With Python
Getting Ready to Use Python
Using Python in a Jupyter Notebook
Creating and Using Lists
Slicing Lists
The Virtual Machine
Shared Python Code Libraries: The Package Index
Chapter 2 - Rows and Columns
Creating Pandas DataFrames
Exploring DataFrames
Accessing Columns in a DataFrame
Accessing Specific Rows and Columns in a DataFrame
Generating DataFrame Subsets With Conditional Evaluations
A Quick Review
Chapter 3 - Data Munging
Reading Data From a CSV Text File
Removing Rows and Columns
Renaming Rows and Columns
Cleaning Up the Elements
Sorting and Grouping DataFrames
Grouping Within DataFrames
Chapter 4 - What’s My Function?
Why Create and Use Functions?
Creating Functions in Python
Defensive Coding
Classes and Methods
Chapter 5 - Beer, Farms, Peas, and Statistics
Historical Perspective
Sampling a Population
Understanding Descriptive Statistics
Using Descriptive Statistics
Using Histograms to Understand a Distribution
Normal Distributions
Chapter 6 - Sample in a Jar
Sampling in Python
A Repetitious Sampling Adventure
Law of Large Numbers and the Central Limit Theorem
Making Decisions With a Sampling Distribution
Evaluating a New Sample With Thresholds
Chapter 7 - Storage Wars
Accessing Excel Data
Working With Data From External Databases
Accessing a Database
Accessing JSON Data
Chapter 8 - Pictures vs. Numbers
A Visualization Overview
Basic Plots in Python
Using Seaborn
Scatterplot Visualizations
Chapter 9 - Map Magic
Map Visualizations Basics
Creating Map Visualizations With Folium
Showing Points on a Map
Chapter 10 - Linear Models
What is a Model?
Supervised and Unsupervised Learning
Linear Modeling
An Example—Car Maintenance
Partitioning Into Training and Cross Validation Datasets
Using K-Fold Cross Validation
Chapter 11 - Classic Classifiers
More Supervised Learning
A Classification Example
Supervised Learning With Naïve Bayes
Naïve Bayes in Python
Supervised Learning Using Classification and Regression Trees
Chapter 12 - Left Unsupervised
Supervised Versus Unsupervised
Data Mining Processes
Association Rules Data
Association Rules Mining
How the Association Rules Algorithm Works
Visualizing and Screening Association Rules
Chapter 13 - Words of Wisdom: Doing Text Analysis
Unstructured Data
Reading in Text Files
Creating the Word Cloud
Sentiment Analysis
Topic Modeling
Other Uses of Text Mining
Chapter 14 - In the Shallows of Deep Learning
The Impact of Deep Learning
How Does Deep Learning Work?
Deep Learning in Python—a Basic Example
Deep Learning Using the MNIST Data

Produkt-Details

• Erscheint lt. Verlag: 29.5.2024
• Verlagsort: Thousand Oaks
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Schlagworte: An Introduction to Data Science with Python • Big Data • Business Analytics • business research • Data Analysis • Jeffrey M. Stanton • Jeffrey S. Saltz • jupyter notebook • statistical software • Statistics
• ISBN-10: 1-0718-5066-0 / 1071850660
• ISBN-13: 978-1-0718-5066-4 / 9781071850664