Data Science For Dummies, 2nd Edition

Data Science For Dummies, 2nd Edition

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Book description

Your ticket to breaking into the field of data science!

Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios.

From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization.

It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

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Table of contents

    1. Cover
    2. Introduction
      1. About This Book
      2. Foolish Assumptions
      3. Icons Used in This Book
      4. Beyond the Book
      5. Where to Go from Here
      1. Chapter 1: Wrapping Your Head around Data Science
        1. Seeing Who Can Make Use of Data Science
        2. Analyzing the Pieces of the Data Science Puzzle
        3. Exploring the Data Science Solution Alternatives
        4. Letting Data Science Make You More Marketable
        1. Defining Big Data by the Three Vs
        2. Identifying Big Data Sources
        3. Grasping the Difference between Data Science and Data Engineering
        4. Making Sense of Data in Hadoop
        5. Identifying Alternative Big Data Solutions
        6. Data Engineering in Action: A Case Study
        1. Benefiting from Business-Centric Data Science
        2. Converting Raw Data into Actionable Insights with Data Analytics
        3. Taking Action on Business Insights
        4. Distinguishing between Business Intelligence and Data Science
        5. Defining Business-Centric Data Science
        6. Differentiating between Business Intelligence and Business-Centric Data Science
        7. Knowing Whom to Call to Get the Job Done Right
        8. Exploring Data Science in Business: A Data-Driven Business Success Story
        1. Chapter 4: Machine Learning: Learning from Data with Your Machine
          1. Defining Machine Learning and Its Processes
          2. Considering Learning Styles
          3. Seeing What You Can Do
          1. Exploring Probability and Inferential Statistics
          2. Quantifying Correlation
          3. Reducing Data Dimensionality with Linear Algebra
          4. Modeling Decisions with Multi-Criteria Decision Making
          5. Introducing Regression Methods
          6. Detecting Outliers
          7. Introducing Time Series Analysis
          1. Introducing Clustering Basics
          2. Identifying Clusters in Your Data
          3. Categorizing Data with Decision Tree and Random Forest Algorithms
          1. Recognizing the Difference between Clustering and Classification
          2. Making Sense of Data with Nearest Neighbor Analysis
          3. Classifying Data with Average Nearest Neighbor Algorithms
          4. Classifying with K-Nearest Neighbor Algorithms
          5. Solving Real-World Problems with Nearest Neighbor Algorithms
          1. Overviewing the Vocabulary and Technologies
          2. Digging into the Data Science Approaches
          3. Advancing Artificial Intelligence Innovation
          1. Chapter 9: Following the Principles of Data Visualization Design
            1. Data Visualizations: The Big Three
            2. Designing to Meet the Needs of Your Target Audience
            3. Picking the Most Appropriate Design Style
            4. Choosing How to Add Context
            5. Selecting the Appropriate Data Graphic Type
            6. Choosing a Data Graphic
            1. Introducing the D3.js Library
            2. Knowing When to Use D3.js (and When Not To)
            3. Getting Started in D3.js
            4. Implementing More Advanced Concepts and Practices in D3.js
            1. Designing Data Visualizations for Collaboration
            2. Visualizing Spatial Data with Online Geographic Tools
            3. Visualizing with Open Source: Web-Based Data Visualization Platforms
            4. Knowing When to Stick with Infographics
            1. Focusing on the Audience
            2. Starting with the Big Picture
            3. Getting the Details Right
            4. Testing Your Design
            1. Getting into the Basics of GIS
            2. Analyzing Spatial Data
            3. Getting Started with Open-Source QGIS
            1. Chapter 14: Using Python for Data Science
              1. Sorting Out the Python Data Types
              2. Putting Loops to Good Use in Python
              3. Having Fun with Functions
              4. Keeping Cool with Classes
              5. Checking Out Some Useful Python Libraries
              6. Analyzing Data with Python — an Exercise
              1. R’s Basic Vocabulary
              2. Delving into Functions and Operators
              3. Iterating in R
              4. Observing How Objects Work
              5. Sorting Out Popular Statistical Analysis Packages
              6. Examining Packages for Visualizing, Mapping, and Graphing in R
              1. Getting a Handle on Relational Databases and SQL
              2. Investing Some Effort into Database Design
              3. Integrating SQL, R, Python, and Excel into Your Data Science Strategy
              4. Narrowing the Focus with SQL Functions
              1. Making Life Easier with Excel
              2. Using KNIME for Advanced Data Analytics
              1. Chapter 18: Data Science in Journalism: Nailing Down the Five Ws (and an H)
                1. Who Is the Audience?
                2. What: Getting Directly to the Point
                3. Bringing Data Journalism to Life: The Black Budget
                4. When Did It Happen?
                5. Where Does the Story Matter?
                6. Why the Story Matters
                7. How to Develop, Tell, and Present the Story
                8. Collecting Data for Your Story
                9. Finding and Telling Your Data’s Story
                1. Modeling Environmental-Human Interactions with Environmental Intelligence
                2. Modeling Natural Resources in the Raw
                3. Using Spatial Statistics to Predict for Environmental Variation across Space
                1. Making Sense of Data for E-Commerce Growth
                2. Optimizing E-Commerce Business Systems
                1. Temporal Analysis for Crime Prevention and Monitoring
                2. Spatial Crime Prediction and Monitoring
                3. Probing the Problems with Data Science for Crime Analysis
                1. Chapter 22: 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 U.S. Census Bureau Data
                  5. Knowing 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
                  1. Making Custom Web-Based Data Visualizations with Free R Packages
                  2. Examining Scraping, Collecting, and Handling Tools
                  3. Looking into Data Exploration Tools
                  4. Evaluating Web-Based Visualization Tools
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                  Product information

                  • Title: Data Science For Dummies, 2nd Edition
                  • Author(s): Lillian Pierson, Jake Porway
                  • Release date: March 2017
                  • Publisher(s): For Dummies
                  • ISBN: 9781119327639

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