| Description |
1 online resource (211 p.) |
| 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 |
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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 |
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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 |
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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 |
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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 |
| Notes |
Description based upon print version of record |
| Subject |
Data mining.
|
|
Data mining
|
| Form |
Electronic book
|
| ISBN |
9781119544166 |
|
1119544165 |
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