Linked e-resources
Details
Table of Contents
Intro; 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 DatasetExploring 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 AskA 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 VariantSummary; 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 MovementPath 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
Downloading the DatasetExploring 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 AskA 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 VariantSummary; 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 MovementPath 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