Data Science Essentials For Dummies

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