Data Science Essentials For Dummies

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.

• Introduction 1About This Book 2Foolish Assumptions 3Icons Used in This Book 3Where to Go from Here 4Chapter 1: Wrapping Your Head Around Data Science 5Seeing Who Can Make Use of Data Science 6Inspecting the Pieces of the Data Science Puzzle 8Collecting, querying, and consuming data 9Applying mathematical modeling to data science tasks 11Deriving insights from statistical methods 11Coding, coding, coding — it’s just part of the game 12Applying data science to a subject area 12Communicating data insights 14Chapter 2: Tapping into Critical Aspects of Data Engineering 15Defining the Three Vs 15Grappling with data volume 16Handling data velocity 16Dealing with data variety 17Identifying Important Data Sources 18Grasping the Differences among Data Approaches 18Defining data science 19Defining machine learning engineering 20Defining data engineering 20Comparing machine learning engineers, data scientists, and data engineers 21Storing and Processing Data for Data Science 22Storing data and doing data science directly in the cloud 22Processing data in real-time 27Recognizing the Impact of Generative AI 27The reshaping of data engineering 28Tools and frameworks for supporting AI workloads 28Chapter 3: Using a Machine to Learn from Data 29Defining Machine Learning and Its Processes 29Walking through the steps of the machine learning process 30Becoming familiar with machine learning terms 30Considering Learning Styles 31Learning with supervised algorithms 31Learning with unsupervised algorithms 32Learning with reinforcement 32Seeing What You Can Do 32Selecting algorithms based on function 33Generating real-time analytics with Spark 36Chapter 4: Math, Probability, and Statistical Modeling 39Exploring Probability and Inferential Statistics 40Probability distributions 42Conditional probability with Naïve Bayes 44Quantifying Correlation 45Calculating correlation with Pearson’s r 45Ranking variable pairs using Spearman’s rank correlation 47Reducing Data Dimensionality with Linear Algebra 48Decomposing data to reduce dimensionality 48Reducing dimensionality with factor analysis 52Decreasing dimensionality and removing outliers with PCA 53Modeling Decisions with Multiple Criteria Decision-Making 54Turning to traditional MCDM 55Focusing on fuzzy MCDM 57Introducing Regression Methods 57Linear regression 57Logistic regression 59Ordinary least squares regression methods 60Detecting Outliers 60Analyzing extreme values 60Detecting outliers with univariate analysis 61Detecting outliers with multivariate analysis 62Introducing Time Series Analysis 64Identifying patterns in time series 64Modeling univariate time series data 65Chapter 5: Grouping Your Way into Accurate Predictions 67Starting with Clustering Basics 68Getting to know clustering algorithms 69Examining clustering similarity metrics 71Identifying Clusters in Your Data 72Clustering with the k-means algorithm 72Estimating clusters with kernel density estimation 74Clustering with hierarchical algorithms 75Dabbling in the DBScan neighborhood 77Categorizing Data with Decision Tree and Random Forest Algorithms 79Drawing a Line between Clustering and Classification 80Introducing instance-based learning classifiers 81Getting to know classification algorithms 81Making Sense of Data with Nearest Neighbor Analysis 84Classifying Data with Average Nearest Neighbor Algorithms 86Classifying with K-Nearest Neighbor Algorithms 89Understanding how the k-nearest neighbor algorithm works 90Knowing when to use the k-nearest neighbor algorithm 91Exploring common applications of k-nearest neighbor algorithms 92Solving Real-World Problems with Nearest Neighbor Algorithms 92Seeing k-nearest neighbor algorithms in action 92Seeing average nearest neighbor algorithms in action 93Chapter 6: Coding Up Data Insights and Decision Engines 95Seeing Where Python Fits into Your Data Science Strategy 95Using Python for Data Science 96Sorting out the various Python data types 98Putting loops to good use in Python 101Having fun with functions 103Keeping cool with classes 104Checking out some useful Python libraries 107Chapter 7: Generating Insights with Software Applications 115Choosing the Best Tools for Your Data Science Strategy 116Getting a Handle on SQL and Relational Databases 118Investing Some Effort into Database Design 123Defining data types 123Designing constraints properly 124Normalizing your database 124Narrowing the Focus with SQL Functions 127Making Life Easier with Excel 131Using Excel to quickly get to know your data 132Reformatting and summarizing with PivotTables 137Automating Excel tasks with macros 139Chapter 8: Telling Powerful Stories with Data 143Data Visualizations: The Big Three 144Data storytelling for decision-makers 145Data showcasing for analysts 145Designing data art for activists 146Designing to Meet the Needs of Your Target Audience 146Step 1: Brainstorm (All about Eve) 147Step 2: Define the purpose 148Step 3: Choose the most functional visualization type for your purpose 149Picking the Most Appropriate Design Style 150Inducing a calculating, exacting response 150Eliciting a strong emotional response 151Selecting the Appropriate Data Graphic Type 152Standard chart graphics 154Comparative graphics 157Statistical plots 161Topology structures 162Spatial plots and maps 164Testing Data Graphics 167Adding Context 168Creating context with data 169Creating context with annotations 169Creating context with graphical elements 169Chapter 9: Ten Free or Low-Cost Data Science Libraries and Platforms 171Scraping the Web with Beautiful Soup 171Wrangling Data with pandas 172Visualizing Data with Looker Studio 172Machine Learning with scikit-learn 172Creating Interactive Dashboards with Streamlit 173Doing Geospatial Data Visualization with Kepler.gl 173Making Charts with Tableau Public 173Doing Web-Based Data Visualization with RAWGraphs 174Making Cool Infographics with Infogram 174Making Cool Infographics with Canva 174Index 175