Data Science For Dummies®

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Table of Contents

1. Cover
2. Title Page
3. Copyright
4. Introduction
   1. About This Book
   2. Foolish Assumptions
   3. Icons Used in This Book
   4. Beyond the Book
   5. Where to Go from Here
5. Part 1: Getting Started with Data Science
   1. Chapter 1: Wrapping Your Head Around Data Science
      1. Seeing Who Can Make Use of Data Science
      2. Inspecting the Pieces of the Data Science Puzzle
      3. Exploring Career Alternatives That Involve Data Science
   2. Chapter 2: Tapping into Critical Aspects of Data Engineering
      1. Defining Big Data and the Three Vs
      2. Identifying Important Data Sources
      3. Grasping the Differences among Data Approaches
      4. Storing and Processing Data for Data Science
6. Part 2: Using Data Science to Extract Meaning from Your Data
   1. Chapter 3: Machine Learning Means … Using a Machine to Learn from Data
      1. Defining Machine Learning and Its Processes
      2. Considering Learning Styles
      3. Seeing What You Can Do
   2. Chapter 4: Math, Probability, and Statistical Modeling
      1. Exploring Probability and Inferential Statistics
      2. Quantifying Correlation
      3. Reducing Data Dimensionality with Linear Algebra
      4. Modeling Decisions with Multiple Criteria Decision-Making
      5. Introducing Regression Methods
      6. Detecting Outliers
      7. Introducing Time Series Analysis
   3. Chapter 5: Grouping Your Way into Accurate Predictions
      1. Starting with Clustering Basics
      2. Identifying Clusters in Your Data
      3. Categorizing Data with Decision Tree and Random Forest Algorithms
      4. Drawing a Line between Clustering and Classification
      5. Making Sense of Data with Nearest Neighbor Analysis
      6. Classifying Data with Average Nearest Neighbor Algorithms
      7. Classifying with K-Nearest Neighbor Algorithms
      8. Solving Real-World Problems with Nearest Neighbor Algorithms
   4. Chapter 6: Coding Up Data Insights and Decision Engines
      1. Seeing Where Python and R Fit into Your Data Science Strategy
      2. Using Python for Data Science
      3. Using Open Source R for Data Science
   5. Chapter 7: Generating Insights with Software Applications
      1. Choosing the Best Tools for Your Data Science Strategy
      2. Getting a Handle on SQL and Relational Databases
      3. Investing Some Effort into Database Design
      4. Narrowing the Focus with SQL Functions
      5. Making Life Easier with Excel
   6. Chapter 8: Telling Powerful Stories with Data
      1. Data Visualizations: The Big Three
      2. Designing to Meet the Needs of Your Target Audience
      3. Picking the Most Appropriate Design Style
      4. Selecting the Appropriate Data Graphic Type
      5. Testing Data Graphics
      6. Adding Context
7. Part 3: Taking Stock of Your Data Science Capabilities
   1. Chapter 9: Developing Your Business Acumen
      1. Bridging the Business Gap
      2. Traversing the Business Landscape
      3. Surveying Use Cases and Case Studies
   2. Chapter 10: Improving Operations
      1. Establishing Essential Context for Operational Improvements Use Cases
      2. Exploring Ways That Data Science Is Used to Improve Operations
   3. Chapter 11: Making Marketing Improvements
      1. Exploring Popular Use Cases for Data Science in Marketing
      2. Turning Web Analytics into Dollars and Sense
      3. Building Data Products That Increase Sales-and-Marketing ROI
      4. Increasing Profit Margins with Marketing Mix Modeling
   4. Chapter 12: Enabling Improved Decision-Making
      1. Improving Decision-Making
      2. Barking Up the Business Intelligence Tree
      3. Using Data Analytics to Support Decision-Making
      4. Increasing Profit Margins with Data Science
   5. Chapter 13: Decreasing Lending Risk and Fighting Financial Crimes
      1. Decreasing Lending Risk with Clustering and Classification
      2. Preventing Fraud Via Natural Language Processing (NLP)
   6. Chapter 14: Monetizing Data and Data Science Expertise
      1. Setting the Tone for Data Monetization
      2. Monetizing Data Science Skills as a Service
      3. Selling Data Products
      4. Direct Monetization of Data Resources
      5. Pricing Out Data Privacy
8. Part 4: Assessing Your Data Science Options
   1. Chapter 15: Gathering Important Information about Your Company
      1. Unifying Your Data Science Team Under a Single Business Vision
      2. Framing Data Science around the Company’s Vision, Mission, and Values
      3. Taking Stock of Data Technologies
      4. Inventorying Your Company’s Data Resources
      5. People-Mapping
      6. Avoiding Classic Data Science Project Pitfalls
      7. Tuning In to Your Company’s Data Ethos
      8. Making Information-Gathering Efficient
   2. Chapter 16: Narrowing In on the Optimal Data Science Use Case
      1. Reviewing the Documentation
      2. Selecting Your Quick-Win Data Science Use Cases
      3. Picking between Plug-and-Play Assessments
   3. Chapter 17: Planning for Future Data Science Project Success
      1. Preparing an Implementation Plan
      2. Supporting Your Data Science Project Plan
      3. Executing On Your Data Science Project Plan
   4. Chapter 18: Blazing a Path to Data Science Career Success
      1. Navigating the Data Science Career Matrix
      2. Landing Your Data Scientist Dream Job
      3. Leading with Data Science
      4. Starting Up in Data Science
9. Part 5: The Part of Tens
   1. Chapter 19: Ten Phenomenal Resources for Open Data
      1. Digging Through data.gov
      2. Checking Out Canada Open Data
      3. Diving into data.gov.uk
      4. Checking Out US Census Bureau Data
      5. Accessing NASA Data
      6. Wrangling World Bank Data
      7. Getting to Know Knoema Data
      8. Queuing Up with Quandl Data
      9. Exploring Exversion Data
      10. Mapping OpenStreetMap Spatial Data
   2. Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications
      1. Scraping, Collecting, and Handling Data Tools
      2. Data-Exploration Tools
      3. Designing Data Visualizations
      4. Communicating with Infographics
10. Index
11. About the Author
12. Advertisement Page
13. Connect with Dummies
14. End User License Agreement

List of Tables

1. Chapter 5
   1. TABLE 5-1: Business Analyst Stu’s Employee Data
2. Chapter 6
   1. TABLE 6-1: Popular Operators
3. Chapter 8
   1. TABLE 8-1: Types of Data Visualization, by Audience
4. Chapter 10
   1. TABLE 10-1 Operational Improvement Use Cases for Data Science, by Industry
   2. TABLE 10-2 Theoretical Business Use Case for Automating Client Interactions
   3. TABLE 10-3 Theoretical Technology Stack for Automating Client Interactions
   4. TABLE 10-4: Theoretical Business Use Case for Real-Time Optimized Logistics Rout...
   5. TABLE 10-5 Theoretical Technology Stack for Real-Time Optimized Logistics Routin...
   6. TABLE 10-6 Theoretical Business Use Case for Increasing Content-Generation Rates
   7. TABLE 10-7 Theoretical Technology Stack for Increasing Content Generation Rates
5. Chapter 12
   1. TABLE 12-1 Theoretical Business Use Case for Analyzing Customer Sentiment
   2. TABLE 12-2 Theoretical Business Use Case for Real-Time Decision Support
   3. TABLE 12-3 Theoretical Technology Stack for Automating Client Interactions
6. Chapter 13
   1. TABLE 13-1: An Example of n-grams in Phrase and Entity Analysis

List of Illustrations

1. Chapter 2
   1. FIGURE 2-1: Popular sources of big data.
2. Chapter 3
   1. FIGURE 3-1: A example of a simple random sample
   2. FIGURE 3-2: Unsupervised machine learning breaks down unlabeled data into subgr...
   3. FIGURE 3-3: Machine learning algorithms can be broken down by function.
   4. FIGURE 3-4: Neural networks are connected layers of artificial neural units.
   5. FIGURE 3-5: A deep learning network is a neural network with more than one hidd...
3. Chapter 4
   1. FIGURE 4-1: An example of a linear relationship between months and YouTube subs...
   2. FIGURE 4-2: An example of a non-linear relationship between watch time and % vi...
   3. FIGURE 4-3: Applying SVD to compress a sparse, clean dataset.
   4. FIGURE 4-4: Applying SVD to clean and compress a sparse, dirty dataset.
   5. FIGURE 4-5: You can use SVD to decompose data down to u, S, and V matrices.
   6. FIGURE 4-6: Linear regression used to predict home prices based on the number o...
   7. FIGURE 4-7: Spotting outliers with a Tukey boxplot.
   8. FIGURE 4-8: Using PCA to spot outliers.
   9. FIGURE 4-9: A comparison of patterns exhibited by time series.
   10. FIGURE 4-10 An example of an ARMA forecast model.
4. Chapter 5
   1. FIGURE 5-1: A simple scatterplot.
   2. FIGURE 5-2: A simple scatterplot, showing eyeballed estimations of clustering.
   3. FIGURE 5-3: KDE smoothing of the World Bank’s Income and Education data scatter...
   4. FIGURE 5-4: A schematic layout of a sample dendrogram.
   5. FIGURE 5-5: Using DBScan to detect outliers (in black) within the Iris dataset....
   6. FIGURE 5-6: A decision tree model predicts survival rates from the Titanic cata...
   7. FIGURE 5-7: Using the Continent feature to classify World Bank data.
   8. FIGURE 5-8: The distances between the employees’ tuples.
   9. FIGURE 5-9: Finding the average similarity between employees.
   10. FIGURE 5-10: How kNN works.
5. Chapter 6
   1. FIGURE 6-1: Sample output from Python’s MatPlotLib library.
   2. FIGURE 6-2: Time-series plot of monthly snow depth data.
   3. FIGURE 6-3: The relationship between atomic vectors, lists, and data frame obje...
   4. FIGURE 6-4: Linear regression coefficients from R, translated into a plain math...
   5. FIGURE 6-5: A scatterplot, generated in the ggplot2 package.
   6. FIGURE 6-6: A network diagram that was generated using the statnet package.
6. Chapter 7
   1. FIGURE 7-1: An example of how SQL is human-readable.
   2. FIGURE 7-2: A relationship between data tables that share a column.
   3. FIGURE 7-3: The full dataset that tracks employee sales performance.
   4. FIGURE 7-4: The sales performance dataset, filtered to show only Abbie’s record...
   5. FIGURE 7-5: Spotting outliers in a tabular dataset with conditional formatting ...
   6. FIGURE 7-6: Spotting outliers in a tabular dataset with color scales.
   7. FIGURE 7-7: Excel XY (scatter) plots provide a simple way to visually detect ou...
   8. FIGURE 7-8: Excel line charts make it easy to visually detect trends in data.
   9. FIGURE 7-9: A long dataset and a wide spreadsheet.
   10. FIGURE 7-10: Creating a wide data table from the long dataset via a PivotTable.
   11. FIGURE 7-11: Using a macro to insert empty cells between values.
7. Chapter 8
   1. FIGURE 8-1: This design style conveys a calculating and exacting feel.
   2. FIGURE 8-2: This design style is intended to evoke an emotional response.
   3. FIGURE 8-3: Data visualization versus data graphics.
   4. FIGURE 8-4: Types of data graphics, broken down by audience and data visualizat...
   5. FIGURE 8-5: An area chart in three dimensions.
   6. FIGURE 8-6: A bar chart showing the area of US states by their acreage, in thou...
   7. FIGURE 8-7: A line chart.
   8. FIGURE 8-8: A pie chart.
   9. FIGURE 8-9: A bubble chart.
   10. FIGURE 8-10: A packed circle diagram.
   11. FIGURE 8-11: A Gantt chart.
   12. FIGURE 8-12: A stacked chart.
   13. FIGURE 8-13: A tree map.
   14. FIGURE 8-14: A simple word cloud.
   15. FIGURE 8-15: A histogram.
   16. FIGURE 8-16: A scatterplot.
   17. FIGURE 8-17: A scatterplot matrix.
   18. FIGURE 8-18: A linear topology.
   19. FIGURE 8-19: A graph mesh network topology.
   20. FIGURE 8-20: A hierarchical tree topology.
   21. FIGURE 8-21: A Cloropleth map.
   22. FIGURE 8-22: A point map.
   23. FIGURE 8-23: A raster surface map.
   24. FIGURE 8-24: Here you see the importance of selecting effective data graphics.
   25. FIGURE 8-25: Using annotation to create context.
   26. FIGURE 8-26: Using graphical elements to create context.
8. Chapter 9
   1. FIGURE 9-1: The data science Venn diagram.
   2. FIGURE 9-2: The mechanics by which traditional data roles increase business pro...
   3. FIGURE 9-3: The three layers of the business acumen cake.
   4. FIGURE 9-4: My STAR framework, for managing profit-forming data science project...
   5. FIGURE 9-5: Five routes by which data science impacts business.
   6. FIGURE 9-6: A simple business use case diagram.
9. Chapter 10
   1. FIGURE 10-1: AI-enabled robotics automation.
   2. FIGURE 10-2: Diagram of a debt-recovery business use case.
   3. FIGURE 10-3: Diagram of a logistics-routing business use case.
   4. FIGURE 10-4: Diagram of a content publishing business use case.
10. Chapter 11
    1. FIGURE 11-1: A channel scorecard.
11. Chapter 12
    1. FIGURE 12-1: The data-to-dollars flowchart.
    2. FIGURE 12-2: The business intelligence path within the data-to-dollars flowchar...
    3. FIGURE 12-3: An example of a modern, SaaS BI tool.
    4. FIGURE 12-4: The analytics path within the data-to-dollars flowchart.
    5. FIGURE 12-5: An example of analytics that report on real-time data.
    6. FIGURE 12-6: An example of analytics that report on external data.
    7. FIGURE 12-7: The data science path within the data-to-dollars flowchart.
    8. FIGURE 12-8: A business use case diagram for automated decision support AI for ...
12. Chapter 13
    1. FIGURE 13-1: NLP enabled affective computing.
    2. FIGURE 13-2: Sentiment analysis flowchart.
    3. FIGURE 13-3: An example of raw data ingested into an NLP system.
    4. FIGURE 13-4: An example of text normalization.
    5. FIGURE 13-5: An example of text tokenization.
    6. FIGURE 13-6: Forrester's estimated ROI for a client.
13. Chapter 14
    1. FIGURE 14-1: The three main ways to monetize data.
    2. FIGURE 14-2: The value proposition of personal data in advertising.
14. Chapter 15
    1. FIGURE 15-1: The classic power dynamic within a data science team.
    2. FIGURE 15-2: The need for business vision, mission, and values.
    3. FIGURE 15-3: My STAR framework, for managing profit-forming data science projec...
    4. FIGURE 15-4: An example of data architecture.
    5. FIGURE 15-5: A list of popular data technologies, broken down according to func...
    6. FIGURE 15-6: The structure of a data dictionary.
    7. FIGURE 15-7: The relationship between a dataset and the data dictionary that de...
    8. FIGURE 15-8: An example of data quality and silo issues.
    9. FIGURE 15-9: An organizational chart.
    10. FIGURE 15-10: Some foundations from which great data professionals emerge.
    11. FIGURE 15-11: Questions and recipients inside my question-and-asset request dat...
    12. FIGURE 15-12: Some filtered questions, ready to send to relevant finance person...
15. Chapter 16
    1. FIGURE 16-1: Assessing your company’s current state.
16. Chapter 17
    1. FIGURE 17-1: Recommending a plan of action.
    2. FIGURE 17-2: An example of reference architecture.
17. Chapter 18
    1. FIGURE 18-1: Potential roles that can take you from entry-level to senior-level...
    2. FIGURE 18-2: Potential roles that can take you from entry-level to senior-level...
    3. FIGURE 18-3: Potential roles that can take you from entry-level to senior-level...
    4. FIGURE 18-4: The four types of currency.
    5. FIGURE 18-5: Data scientists, broken down by degree types.
    6. FIGURE 18-6: Data scientists, broken down by field of study.
    7. FIGURE 18-7: A map from within Jake Vanderplas’s instructional coding demonstra...
    8. FIGURE 18-8: The four best business models for remote, self-funded data science...
    9. FIGURE 18-9: The four best business models for remote, self-funded data science...
18. Chapter 19
    1. FIGURE 19-1: The index of insect records in Knoema’s search.
    2. FIGURE 19-2: The index of Apple records in a Quandl search.
19. Chapter 20
    1. FIGURE 20-1: An ImageQuilts output from the Google Images search term data scie...
    2. FIGURE 20-2: A moderate-size graph on characters in the novel Les Misérables....
    3. FIGURE 20-3: A Gephi hairball graph of the US power grid.
    4. FIGURE 20-4: An interactive choropleth map in CARTO.
    5. FIGURE 20-5: An interactive bubble map in CARTO.
    6. FIGURE 20-6: A diagram from RAWCharts.
    7. FIGURE 20-7: A bar chart in Infogram.
    8. FIGURE 20-8: A bar chart in Piktochart.

Guide

1. Cover
2. Title Page
3. Copyright
4. Table of Contents
5. Begin Reading
6. Index
7. About the Author