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Data Science
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Data Science The Executive Summary - a Technical Book for Non-Technical Professionals.
Saved in:
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
Holdings
Description
Table of Contents
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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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