Data Science For Dummies. 3 Ed

Title Page....2 Copyright....3 Introduction....17 About This Book....19 Foolish Assumptions....20 Icons Used in This Book....20 Beyond the Book....21 Where to Go from Here....21 Part 1: Getting Started with Data Science....23 Chapter 1: Wrapping Your Head Around Data Science....25 Seeing Who Can Make Use of Data Science....26 Inspecting the Pieces of the Data Science Puzzle....29 Exploring Career Alternatives That Involve Data Science....35 Chapter 2: Tapping into Critical Aspects of Data Engineering....40 Defining Big Data and the Three Vs....40 Identifying Important Data Sources....45 Grasping the Differences among Data Approaches....46 Storing and Processing Data for Data Science....51 Part 2: Using Data Science to Extract Meaning from Your Data....62 Chapter 3: Machine Learning Means … Using a Machine to Learn from Data....64 Defining Machine Learning and Its Processes....64 Considering Learning Styles....67 Seeing What You Can Do....69 Chapter 4: Math, Probability, and Statistical Modeling....77 Exploring Probability and Inferential Statistics....78 Quantifying Correlation....84 Reducing Data Dimensionality with Linear Algebra....87 Modeling Decisions with Multiple Criteria Decision-Making....96 Introducing Regression Methods....99 Detecting Outliers....103 Introducing Time Series Analysis....107 Chapter 5: Grouping Your Way into Accurate Predictions....111 Starting with Clustering Basics....112 Identifying Clusters in Your Data....117 Categorizing Data with Decision Tree and Random Forest Algorithms....125 Drawing a Line between Clustering and Classification....127 Making Sense of Data with Nearest Neighbor Analysis....132 Classifying Data with Average Nearest Neighbor Algorithms....134 Classifying with K-Nearest Neighbor Algorithms....137 Solving Real-World Problems with Nearest Neighbor Algorithms....142 Chapter 6: Coding Up Data Insights and Decision Engines....145 Seeing Where Python and R Fit into Your Data Science Strategy....146 Using Python for Data Science....147 Using Open Source R for Data Science....166 Chapter 7: Generating Insights with Software Applications....188 Choosing the Best Tools for Your Data Science Strategy....189 Getting a Handle on SQL and Relational Databases....190 Investing Some Effort into Database Design....197 Narrowing the Focus with SQL Functions....201 Making Life Easier with Excel....206 Chapter 8: Telling Powerful Stories with Data....221 Data Visualizations: The Big Three....222 Designing to Meet the Needs of Your Target Audience....225 Picking the Most Appropriate Design Style....229 Selecting the Appropriate Data Graphic Type....233 Testing Data Graphics....251 Adding Context....253 Part 3: Taking Stock of Your Data Science Capabilities....256 Chapter 9: Developing Your Business Acumen....258 Bridging the Business Gap....258 Traversing the Business Landscape....261 Surveying Use Cases and Case Studies....268 Chapter 10: Improving Operations....277 Establishing Essential Context for Operational Improvements Use Cases....277 Exploring Ways That Data Science Is Used to Improve Operations....279 Chapter 11: Making Marketing Improvements....307 Exploring Popular Use Cases for Data Science in Marketing....307 Turning Web Analytics into Dollars and Sense....311 Building Data Products That Increase Sales-and-Marketing ROI....318 Increasing Profit Margins with Marketing Mix Modeling....320 Chapter 12: Enabling Improved Decision-Making....326 Improving Decision-Making....326 Barking Up the Business Intelligence Tree....328 Using Data Analytics to Support Decision-Making....331 Increasing Profit Margins with Data Science....338 Chapter 13: Decreasing Lending Risk and Fighting Financial Crimes....350 Decreasing Lending Risk with Clustering and Classification....350 Preventing Fraud Via Natural Language Processing (NLP)....352 Chapter 14: Monetizing Data and Data Science Expertise....362 Setting the Tone for Data Monetization....362 Monetizing Data Science Skills as a Service....366 Selling Data Products....371 Direct Monetization of Data Resources....373 Pricing Out Data Privacy....376 Part 4: Assessing Your Data Science Options....381 Chapter 15: Gathering Important Information about Your Company....383 Unifying Your Data Science Team Under a Single Business Vision....384 Framing Data Science around the Company’s Vision, Mission, and Values....386 Taking Stock of Data Technologies....390 Inventorying Your Company’s Data Resources....392 People-Mapping....398 Avoiding Classic Data Science Project Pitfalls....400 Tuning In to Your Company’s Data Ethos....403 Making Information-Gathering Efficient....405 Chapter 16: Narrowing In on the Optimal Data Science Use Case....409 Reviewing the Documentation....410 Selecting Your Quick-Win Data Science Use Cases....411 Picking between Plug-and-Play Assessments....416 Chapter 17: Planning for Future Data Science Project Success....429 Preparing an Implementation Plan....430 Supporting Your Data Science Project Plan....439 Executing On Your Data Science Project Plan....445 Chapter 18: Blazing a Path to Data Science Career Success....447 Navigating the Data Science Career Matrix....447 Landing Your Data Scientist Dream Job....450 Leading with Data Science....465 Starting Up in Data Science....468 Part 5: The Part of Tens....481 Chapter 19: Ten Phenomenal Resources for Open Data....484 Digging Through data.gov....485 Checking Out Canada Open Data....487 Diving into data.gov.uk....488 Checking Out US Census Bureau Data....490 Accessing NASA Data....491 Wrangling World Bank Data....492 Getting to Know Knoema Data....493 Queuing Up with Quandl Data....495 Exploring Exversion Data....497 Mapping OpenStreetMap Spatial Data....498 Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications....499 Scraping, Collecting, and Handling Data Tools....500 Data-Exploration Tools....502 Designing Data Visualizations....506 Communicating with Infographics....514 Index....520 About the Author....587 Advertisement Page....590 Connect with Dummies....592 End User License Agreement....594