Data Science Essentials for Dummies - Lillian Pierson

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Résumé :

Introduction 1

About This Book 2

Foolish Assumptions 3

Icons Used in This Book 3

Where to Go from Here 4

Chapter 1: Wrapping Your Head Around Data Science 5

Seeing Who Can Make Use of Data Science 6

Inspecting the Pieces of the Data Science Puzzle 8

Collecting, querying, and consuming data 9

Applying mathematical modeling to data science tasks 11

Deriving insights from statistical methods 11

Coding, coding, coding - it's just part of the game 12

Applying data science to a subject area 12

Communicating data insights 14

Chapter 2: Tapping into Critical Aspects of Data Engineering 15

Defining the Three Vs 15

Grappling with data volume 16

Handling data velocity 16

Dealing with data variety 17

Identifying Important Data Sources 18

Grasping the Differences among Data Approaches 18

Defining data science 19

Defining machine learning engineering 20

Defining data engineering 20

Comparing machine learning engineers, data scientists, and data engineers 21

Storing and Processing Data for Data Science 22

Storing data and doing data science directly in the cloud 22

Processing data in real-time 27

Recognizing the Impact of Generative AI 27

The reshaping of data engineering 28

Tools and frameworks for supporting AI workloads 28

Chapter 3: Using a Machine to Learn from Data 29

Defining Machine Learning and Its Processes 29

Walking through the steps of the machine learning process 30

Becoming familiar with machine learning terms 30

Considering Learning Styles 31

Learning with supervised algorithms 31

Learning with unsupervised algorithms 32

Learning with reinforcement 32

Seeing What You Can Do 32

Selecting algorithms based on function 33

Generating real-time analytics with Spark 36

Chapter 4: Math, Probability, and Statistical Modeling 39

Exploring Probability and Inferential Statistics 40

Probability distributions 42

Conditional probability with Na?ve Bayes 44

Quantifying Correlation 45

Calculating correlation with Pearson's r 45

Ranking variable pairs using Spearman's rank correlation 47

Reducing Data Dimensionality with Linear Algebra 48

Decomposing data to reduce dimensionality 48

Reducing dimensionality with factor analysis 52

Decreasing dimensionality and removing outliers with PCA 53

Modeling Decisions with Multiple Criteria Decision-Making 54

Turning to traditional MCDM 55

Focusing on fuzzy MCDM 57

Introducing Regression Methods 57

Linear regression 57

Logistic regression 59

Ordinary least squares regression methods 60

Detecting Outliers 60

Analyzing extreme values 60

Detecting outliers with univariate analysis 61

Detecting outliers with multivariate analysis 62

Introducing Time Series Analysis 64

Identifying patterns in time series 64

Modeling univariate time series data 65

Chapter 5: Grouping Your Way into Accurate Predictions 67

Starting with Clustering Basics 68

Getting to know clustering algorithms 69

Examining clustering similarity metrics 71

Identifying Clusters in Your Data 72

Clustering with the k-means algorithm 72

Estimating clusters with kernel density estimation 74

Clustering with hierarchical algorithms 75

Dabbling in the DBScan neighborhood 77

Categorizing Data with Decision Tree and Random Forest Algorithms 79

Drawing a Line between Clustering and Classificati...

Biographie:

Lillian Pierson, PE, is the founder and fractional CMO at Data-Mania, as well as a globally recognized growth leader in technology. To date, she has helped educate approximately 2 million professionals on how to leverage AI, data strategy, and data science to drive business growth....