Full Description
An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool.
Included with this title:
LMS Cartridge: Import this title's instructor resources into your school's learning management system (LMS) and save time. Don't use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.
Contents
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
