Data Science Essentials For Dummies

Autorentext

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.

Klappentext

The pocket-sized guide to data science You've just picked up a concise little book that covers all the fundamentals of data science. Data Science Essentials For Dummies distills everything you need to know to analyze data, build models, identify patterns, and visualize your findings. No overwhelming details or unnecessary repetition-just straightforward, easy-to-find answers to your essential data science questions. Great for supplementing a course or as a quick reference while you work, this book puts you on the fast track to deriving more business value from data. **Inside...

• Finding succinct explanations
• Getting clear, effective examples
• Understanding data types
• Working with software
• Coding in Python and SQL
• Understanding machine learning
• Brushing up on essential math and statistics
• Using data to tell a story

Inhalt

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 Naïve 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 Classification 80

Introducing instance-based learning classifiers 81

Getting to know classification algorithms 81

Making Sense of Data with Nearest Neighbor Analysis 84

Classifying Data with Average Nearest Neighbor Algorithms 86

Classifying with K-Nearest Neighbor Algorithms 89

Understanding how the k-nearest neighbor algorithm works 90

Knowing when to use the k-nearest neighbor algorithm 91

Exploring common applications of k-nearest neighbor algorithms 92

Solving Real-World Problems with Nearest Neighbor Algorithms 92

Seeing k-nearest neighbor algorithms in action 92

Seeing average nearest neighbor algorithms in action 93

Chapter 6: Coding Up Data Insights and Decision Engines 95

Seeing Where Python Fits into Your Data Science Strategy 95

Using Python for Data Science 96

Sorting out the various Python data types 98

Putting loops to good use in Python 101

Having fun with functions 103

Keeping cool with classes 104

Checking out some useful Python libraries 107

Chapter 7: Generating Insights with Software Applications 115

Choosing the Best Tools for Your Data Science Strategy 116

Getting a Handle on SQL and Relational Databases 118

Investing Some Effort into Database Design 123

Defining data types 123

Designing constraints properly 124

Normalizing your database 124

Narrowing the Focus with SQL Functions 127

Making Life Easier with Excel 131

Using Excel to quickly get to know your data 132

Reformatting and summarizing with PivotTables 137

Automating Excel tasks with macros 139

Chapter 8: Telling Powerful Stories with Data 143

Data Visualizations: The Big Three 144

Data storytelling for decision-makers 145

Data showcasing for analysts 145

Designing data art for activists 146

Designing to Meet the Needs of Your Target Audience 146

Step 1: Brainstorm (All about Eve) 147

Step 2: Define the purpose 148

Step 3: Choose the most functional visualization type for your purpose 149

Picking the Most Appropriate Design Style 150

Inducing a calculating, exacting response 150

Eliciting a strong emotional response 151

Selecting the Appropriate Data Graphic Type 152

Standard chart graphics 154

Comparative graphics 157

Statistical plots 161

Topology structures 162

Spatial plots and maps 164

Testing Data Graphics 167

Adding Context 168

Creating context with data 169

Creating context with annotations 169

Creating context with graphical elements 169

Chapter 9: Ten Free or Low-Cost Data Science Libraries and Platforms 171

Scraping the Web with Beautiful Soup 171

Wrangling Data with pandas 172

Visualizing Data with Looker Studio 172

Machine Learning with scikit-learn 172

Creating Interactive Dashboards with Streamlit 173

Doing Geospatial Data Visualization with Kepler.gl 173

Making Charts with Tableau Public 173

Doing Web-Based Data Visualization with RAWGraphs 174

Making Cool Infographics with Infogram 174

Making Cool Infographics with Canva 174

Index 175