Data Science The Executive Summary - a Technical Book for Non-Technical Professionals.

Data Science The Executive Summary - a Technical Book for Non-Technical Professionals.

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Bibliographic Details
Main Author: Cady, Field
Format: eBook
Language:English
Published: Newark : John Wiley & Sons, Incorporated, 2020.
Subjects:
Data mining.
Data mining
Online Access:Click for online access
Table of Contents:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Chapter 1 Introduction
  • 1.1 Why Managers Need to Know About Data Science
  • 1.2 The New Age of Data Literacy
  • 1.3 Data-Driven Development
  • 1.4 How to Use this Book
  • Chapter 2 The Business Side of Data Science
  • 2.1 What Is Data Science?
  • 2.1.1 What Data Scientists Do
  • 2.1.2 History of Data Science
  • 2.1.3 Data Science Roadmap
  • 2.1.4 Demystifying the Terms: Data Science, Machine Learning, Statistics, and Business Intelligence
  • 2.1.4.1 Machine Learning
  • 2.1.4.2 Statistics
  • 2.1.4.3 Business Intelligence
  • 2.1.5 What Data Scientists Don't (Necessarily) Do
  • 2.1.5.1 Working Without Data
  • 2.1.5.2 Working with Data that Can't Be Interpreted
  • 2.1.5.3 Replacing Subject Matter Experts
  • 2.1.5.4 Designing Mathematical Algorithms
  • 2.2 Data Science in an Organization
  • 2.2.1 Types of Value Added
  • 2.2.1.1 Business Insights
  • 2.2.1.2 Intelligent Products
  • 2.2.1.3 Building Analytics Frameworks
  • 2.2.1.4 Offline Batch Analytics
  • 2.2.2 One-Person Shops and Data Science Teams
  • 2.2.3 Related Job Roles
  • 2.2.3.1 Data Engineer
  • 2.2.3.2 Data Analyst
  • 2.2.3.3 Software Engineer
  • 2.3 Hiring Data Scientists
  • 2.3.1 Do I Even Need Data Science?
  • 2.3.2 The Simplest Option: Citizen Data Scientists
  • 2.3.3 The Harder Option: Dedicated Data Scientists
  • 2.3.4 Programming, Algorithmic Thinking, and Code Quality
  • 2.3.5 Hiring Checklist
  • 2.3.6 Data Science Salaries
  • 2.3.7 Bad Hires and Red Flags
  • 2.3.8 Advice with Data Science Consultants
  • 2.4 Management Failure Cases
  • 2.4.1 Using Them as Devs
  • 2.4.2 Inadequate Data
  • 2.4.3 Using Them as Graph Monkeys
  • 2.4.4 Nebulous Questions
  • 2.4.5 Laundry Lists of Questions Without Prioritization
  • Chapter 3 Working with Modern Data
  • 3.1 Unstructured Data and Passive Collection
  • 3.2 Data Types and Sources
  • 3.3 Data Formats
  • 3.3.1 CSV Files
  • 3.3.2 JSON Files
  • 3.3.3 XML and HTML
  • 3.4 Databases
  • 3.4.1 Relational Databases and Document Stores
  • 3.4.2 Database Operations
  • 3.5 Data Analytics Software Architectures
  • 3.5.1 Shared Storage
  • 3.5.2 Shared Relational Database
  • 3.5.3 Document Store + Analytics RDB
  • 3.5.4 Storage + Parallel Processing
  • Chapter 4 Telling the Story, Summarizing Data
  • 4.1 Choosing What to Measure
  • 4.2 Outliers, Visualizations, and the Limits of Summary Statistics: A Picture Is Worth a Thousand Numbers
  • 4.3 Experiments, Correlation, and Causality
  • 4.4 Summarizing One Number
  • 4.5 Key Properties to Assess: Central Tendency, Spread, and Heavy Tails
  • 4.5.1 Measuring Central Tendency
  • 4.5.1.1 Mean
  • 4.5.1.2 Median
  • 4.5.1.3 Mode
  • 4.5.2 Measuring Spread
  • 4.5.2.1 Standard Deviation
  • 4.5.2.2 Percentiles
  • 4.5.3 Advanced Material: Managing Heavy Tails
  • 4.6 Summarizing Two Numbers: Correlations and Scatterplots
  • 4.6.1 Correlations
  • 4.6.1.1 Pearson Correlation

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