Data Science For Dummies, 3rd Edition

Data Science For Dummies, 3rd Edition

by Lillian Pierson
Released September 2021
Publisher(s): For Dummies
ISBN: 9781119811558

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Book description

Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help

What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is.

Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects.

Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.

Data Science For Dummies demonstrates:

  • The only process you’ll ever need to lead profitable data science projects
  • Secret, reverse-engineered data monetization tactics that no one’s talking about
  • The shocking truth about how simple natural language processing can be
  • How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise 

Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

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

Product information

  • Title: Data Science For Dummies, 3rd Edition
  • Author(s): Lillian Pierson
  • Release date: September 2021
  • Publisher(s): For Dummies
  • ISBN: 9781119811558