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Practical.Data.Science.with.Python.3.(2019).pdf

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    Table of Contents
    About the Author
    About the Technical Reviewer
    Acknowledgments
    Introduction
    Chapter 1: Introduction to Data Science
    Main Phases of a Data Science Project
    Brown Cow Model Case Study
    Big Data
    Big Data Example: MOOC Platforms
    How to Learn Data Science
    Domain Knowledge Attainment—Example
    Programming Skills Attainment—Example
    Overview of the Anaconda Ecosystem
    Managing Packages and Environments
    Sharing and Reproducing Environments
    Summary
    References
    Chapter 2: Data Engineering
    E-Commerce Customer Segmentation: Case Study
    Creating a Project in Spyder
    Downloading the Dataset
    Exploring the Dataset
    Finding Associations Between Features
    Incorporating Custom Features
    Automating the Steps
    Inspecting Results
    Persisting Results
    Parquet Engines
    Restructuring Code to Cope with Large CSV Files
    Public Data Sources
    Summary
    References
    Chapter 3: Software Engineering
    Characteristics of a Large-Scale Software System
    Software Engineering Knowledge Areas
    Rules, Principles, Conventions, and Standards
    Context Awareness and Communicative Abilities
    Reducing Cyclomatic Complexity
    Cone of Uncertainty and Having Time to Ask
    Fixing a Bug and Knowing How to Ask
    A Better Fix
    Scenario 1: The Developer Doesn’t Speak the Language of Business
    Scenario 2: The Developer Does Speak the Language of Business
    A More Advanced Fix
    Scenario 1: The Developer Doesn’t Speak the Language of Business
    Scenario 2: The Developer Does Speak the Language of Business
    Handling Legacy Code
    Understanding Bug-Free Code
    Understanding Faulty Code
    The Importance of APIs
    Fervent Flexibility Hurts Your API
    The Socio-* Pieces of Software Production
    Funny Elevator Case Study
    First Optimization Attempt
    Second Optimization Attempt
    Teammate- and Business-Friendly Variant
    Summary
    References
    Chapter 4: Documenting Your Work
    JupyterLab in Action
    Experimenting with Code Execution
    Managing the Kernel
    Connecting to a Notebook’s Kernel
    Descending Ball Project
    Problem Specification
    Model Definition
    Path Finder’s Implementation
    Interaction with the Simulator
    Test Automation
    Refactoring the Simulator’s Notebook
    Document Structure
    Wikipedia Edits Project
    Abstract
    Motivation
    Drawbacks
    Conclusion
    Summary
    References
    Chapter 5: Data Processing
    Augmented Descending Ball Project
    Version 1.1
    Boundaries and Movement
    Path Finding Engine
    Retrospective of Version 1.1
    Version 1.2
    Enhancing the Input Subsystem
    Enhancing the Output Subsystem
    Retrospective of Version 1.2
    Version 1.3
    Establishing the Baseline
    Performance Optimization
    Retrospective of Version 1.3
    Abstractions vs. Latent Features
    Compressing the Ratings Matrix
    Summary
    References
    Chapter 6: Data Visualization
    Visualizing Temperature Data Case Study
    Showing Stations on a Map
    Plotting Temperatures
    Closest Pair Case Study
    Version 1.0
    Version 2.0
    Analysis of the Running Time
    Version 3.0
    Enquiry of Algorithms Evolution
    Interactive Information Radiators
    The Power of Domain-Specific Languages
    Summary
    References
    Chapter 7: Machine Learning
    Exposition of Core Concepts and Techniques
    Overfitting
    Underfitting and Feature Interaction
    Collinearity
    Residuals Plot
    Regularization
    Predicting Financial Movements Case Study
    Data Retrieval
    Data Preprocessing
    Discovering Trends in Time Series
    Transforming Features
    Streaming Amounts
    Feature Engineering
    Implementing Streaming Linear Regression
    Summary
    References
    Chapter 8: Recommender Systems
    Introduction to Recommender Systems
    Simple Movie Recommender Case Study
    Introduction to LensKit for Python
    Summary
    References
    Chapter 9: Data Security
    Checking for Compromise
    Introduction to the GDPR
    Machine Learning and Security
    Membership Inference Attack
    Shadow Training
    Poisoning Attack
    Summary
    References
    Chapter 10: Graph Analysis
    Usage Matrix As a Graph Problem
    Opposite Quality Attributes
    Partitioning the Model into a Bipartite Graph
    Scalable Graph Loading
    Social Networks
    Summary
    References
    Chapter 11: Complexity and Heuristics
    From Simple to Complicated
    Counting the Occurrences of a Digit
    Estimating the Edge Betweenness Centrality
    The Count of Divisible Numbers
    From Disorder to Complex
    Exploring the KDD Cup 1999 Data
    Cynefin and Data Science
    Summary
    References
    Chapter 12: Deep Learning
    Intelligent Machines
    Intelligence As Mastery of Symbols
    Manual Feature Engineering
    Modeling the Aggregated State
    Tying All Pieces Together
    Machine-Based Feature Engineering
    Implementation from Scratch
    Implementation with PyTorch
    Summary
    References
    Index
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