Data Science. Field Cady

      1  Cover

      2  Data Science: The Executive Summary

      3  Copyright

      4  Dedication

      5  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

      6  2 The Business Side of Data Science 2.1 What Is Data Science? 2.2 Data Science in an Organization 2.3 Hiring Data Scientists 2.4 Management Failure Cases

      7  3 Working with Modern Data 3.1 Unstructured Data and Passive Collection 3.2 Data Types and Sources 3.3 Data Formats 3.4 Databases 3.5 Data Analytics Software Architectures Notes

      8  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 4.6 Summarizing Two Numbers: Correlations and Scatterplots 4.7 Advanced Material: Fitting a Line or Curve 4.8 Statistics: How to Not Fool Yourself 4.9 Advanced Material: Probability Distributions Worth Knowing

      9  5 Machine Learning 5.1 Supervised Learning, Unsupervised Learning, and Binary Classifiers 5.2 Measuring Performance 5.3 Advanced Material: Important Classifiers 5.4 Structure of the Data: Unsupervised Learning 5.5 Learning as You Go: Reinforcement Learning

      10  6 Knowing the Tools 6.1 A Note on Learning to Code 6.2 Cheat Sheet 6.3 Parts of the Data Science Ecosystem 6.4 Advanced Material: Database Query Crash Course

      11  7 Deep Learning and Artificial Intelligence 7.1 Overview of AI 7.2 Neural Networks 7.3 Natural Language Processing 7.4 Knowledge Bases and Graphs

      12  Postscript

      13  Index

      14  End User License Agreement

      1 Chapter 2Table 2.1 Data science work can largely be divided into producing human‐under...Table 2.2 Data engineers specialize in creating software systems to store and...Table 2.3 BI analysts generally lack the ability to create mathematically com...Table 2.4 Software engineers create products of a scale and complexity far gr...

      2 Chapter 6Table 6.1 These functions – which are present in most SQL‐like languages – ta...Table 6.2 Common SQL aggregation functions.

      3 Chapter 7Table 7.1 Feature of regular expressions.

      1 Chapter 2Figure 2.1 The process of data science is deeply iterative, with the questio...

      2 Chapter 4Figure 4.1 Anscombe's quartet is a famous demonstration of the limitations o...Figure 4.2 Mean, median, and mode are the most common measures of central te...Figure 4.3 Box‐and‐whisker plots capture the median, the 25% and 75% percent...Figure 4.4 Box‐and‐whisker plots allow you to visually compare several data ...Figure 4.5 The histograms of two datasets, plotted for comparison on (a) a n...Figure 4.6 In both of these plots the correlation between x and y will be cl...Figure 4.7 This dataset will have ordinal correlation of 1, since y consiste...Figure 4.8 Residuals measure the accuracy of a model. Here the gray points a...Figure 4.9 A degenerative form of “curve fitting” is used as a base of compa...Figure 4.10 Large residuals can come from two sources: either that data