Data Science For Dummies (Converted)

438 Pages·1994·9.25 MB·English

by  Pierson L.

Data Science For Dummies®
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Library of Congress Control Number: 2014955780
ISBN 978-1-118-4155-6 (pbk); ISBN 978-1-118-84145-7 (ebk); ISBN 978-1-118-
84152-5
Data Science For Dummies®
Visit www.dummies.com/cheatsheet/datascience to view this
book’s cheat sheet.
Table of Contents
Cover
Foreword
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part I: Getting Started With Data Science
Chapter 1: Wrapping Your Head around Data Science
Seeing Who Can Make Use of Data Science
Looking at the Pieces of the Data Science Puzzle
Getting a Basic Lay of the Data Science Landscape
Chapter 2: Exploring Data Engineering Pipelines and
Infrastructure
Defining Big Data by Its Four Vs
Identifying Big Data Sources
Grasping the Difference between Data Science and Data Engineering
Boiling Down Data with MapReduce and Hadoop
Identifying Alternative Big Data Solutions
Data Engineering in Action — A Case Study
Chapter 3: Applying Data Science to Business and Industry
Incorporating Data-Driven Insights into the Business Process
Distinguishing Business Intelligence and Data Science
Knowing Who to Call to Get the Job Done Right
Exploring Data Science in Business: A Data-Driven Business Success Story
Part II: Using Data Science to Extract Meaning from Your Data
Chapter 4: Introducing Probability and Statistics
Introducing the Fundamental Concepts of Probability
Introducing Linear Regression
Simulations
Introducing Time Series Analysis
Chapter 5: Clustering and Classification
Introducing the Basics of Clustering and Classification
Identifying Clusters in Your Data
Chapter 6: Clustering and Classification with Nearest Neighbor
Algorithms
Making Sense of Data with Nearest Neighbor Analysis
Seeing the Importance of Clustering and Classification
Classifying Data with Average Nearest Neighbor Algorithms
Classifying with K-Nearest Neighbor Algorithms
Using Nearest Neighbor Distances to Infer Meaning from Point Patterns
Solving Real-World Problems with Nearest Neighbor Algorithms
Chapter 7: Mathematical Modeling in Data Science
Introducing Multi-Criteria Decision Making (MCDM)
Using Numerical Methods in Data Science
Mathematical Modeling with Markov Chains and Stochastic Methods
Chapter 8: Modeling Spatial Data with Statistics
Generating Predictive Surfaces from Spatial Point Data
Using Trend Surface Analysis on Spatial Data
Part III: Creating Data Visualizations that Clearly Communicate
Meaning
Chapter 9: Following the Principles of Data Visualization Design
Understanding the Types of Visualizations
Focusing on Your Audience
Picking the Most Appropriate Design Style
Knowing When to Add Context
Knowing When to Get Persuasive
Choosing the Most Appropriate Data Graphic Type
Choosing Your Data Graphic
Chapter 10: Using D3.js for Data Visualization
Introducing the D3.js Library
Knowing When to Use D3.js (and When Not To)
Getting Started in D3.js
Understanding More Advanced Concepts and Practices in D3.js
Chapter 11: Web-Based Applications for Visualization Design
Using Collaborative Data Visualization Platforms
Visualizing Spatial Data with Online Geographic Tools
Visualizing with Open Source: Web-Based Data Visualization Platforms
Knowing When to Stick with Infographics
Chapter 12: Exploring Best Practices in Dashboard Design
Focusing on the Audience
Starting with the Big Picture
Getting the Details Right
Testing Your Design
Chapter 13: Making Maps from Spatial Data
Getting into the Basics of GIS
Analyzing Spatial Data
Getting Started with Open-Source QGIS
Part IV: Computing for Data Science
Chapter 14: Using Python for Data Science
Understanding Basic Concepts in Python
Getting on a First-Name Basis with Some Useful Python Libraries
Using Python to Analyze Data — An Example Exercise
Chapter 15: Using Open Source R for Data Science
Introducing the Fundamental Concepts
Previewing R Packages
Chapter 16: Using SQL in Data Science
Getting Started with SQL
Using SQL and Its Functions in Data Science
Chapter 17: Software Applications for Data Science
Making Life Easier with Excel
Using KNIME for Advanced Data Analytics
Part V: Applying Domain Expertise to Solve Real-World Problems
Using Data Science
Chapter 18: Using Data Science in Journalism
Exploring the Five Ws and an H
Collecting Data for Your Story
Finding and Telling Your Data’s Story
Bringing Data Journalism to Life: Washington Post’s The Black Budget
Chapter 19: Delving into Environmental Data Science
Modeling Environmental-Human Interactions with Environmental Intelligence
Modeling Natural Resources in the Raw
Using Spatial Statistics to Predict for Environmental Variation across Space
Chapter 20: Data Science for Driving Growth in E-Commerce
Making Sense of Data for E-Commerce Growth
Optimizing E-Commerce Business Systems
Chapter 21: Using Data Science to Describe and Predict Criminal
Activity
Temporal Analysis for Crime Prevention and Monitoring
Spatial Crime Prediction and Monitoring
Probing the Problems with Data Science for Crime Analysis
Part VI: The Part of Tens
Chapter 22: Ten Phenomenal Resources for Open Data
Digging through Data.gov
Checking Out Canada Open Data
Diving into data.gov.uk
Checking Out U.S. Census Bureau Data
Knowing NASA Data
Wrangling World Bank Data
Getting to Know Knoema Data
Queuing Up with Quandl Data
Exploring Exversion Data
Mapping OpenStreetMap Spatial Data
Chapter 23: Ten (or So) Free Data Science Tools and Applications
Making Custom Web-Based Data Visualizations with Free R Packages
Checking Out More Scraping, Collecting, and Handling Tools
Checking Out More Data Exploration Tools
Checking Out More Web-Based Visualization Tools
About the Author
Cheat Sheet
Advertisement Page
Connect with Dummies
End User License Agreement
Foreword
We live in exciting, even revolutionary times. As our daily interactions move from the
physical world to the digital world, nearly every action we take generates data.
Information pours from our mobile devices and our every online interaction. Sensors
and machines collect, store and process information about the environment around us.
New, huge data sets are now open and publicly accessible.
This flood of information gives us the power to make more informed decisions, react
more quickly to change, and better understand the world around us. However, it can be
a struggle to know where to start when it comes to making sense of this data deluge.
What data should one collect? What methods are there for reasoning from data? And,
most importantly, how do we get the answers from the data to answer our most
pressing questions about our businesses, our lives, and our world?
Data science is the key to making this flood of information useful. Simply put, data
science is the art of wrangling data to predict our future behavior, uncover patterns to
help prioritize or provide actionable information, or otherwise draw meaning from
these vast, untapped data resources.
I often say that one of my favorite interpretations of the word “big” in Big Data is
“expansive.” The data revolution is spreading to so many fields that it is now
incumbent on people working in all professions to understand how to use data, just as
people had to learn how to use computers in the 80’s and 90’s. This book is designed to
help you do that.
I have seen firsthand how radically data science knowledge can transform
organizations and the world for the better. At DataKind, we harness the power of data
science in the service of humanity by engaging data science and social sector experts to
work on projects addressing critical humanitarian problems. We are also helping drive
the conversation about how data science can be applied to solve the world’s biggest
challenges. From using satellite imagery to estimate poverty levels to mining decades
of human rights violations to prevent further atrocities, DataKind teams have worked
with many different nonprofits and humanitarian organizations just beginning their data
science journeys. One lesson resounds through every project we do: The people and
organizations that are most committed to using data in novel and responsible ways are
the ones who will succeed in this new environment.
Just holding this book means you are taking your first steps on that journey, too.
Whether you are a seasoned researcher looking to brush up on some data science
techniques or are completely new to the world of data, Data Science For Dummies will
equip you with the tools you need to show whatever you can dream up. You’ll be able
to demonstrate new findings from your physical activity data, to present new insights
from the latest marketing campaign, and to share new learnings about preventing the
spread of disease.
We truly are on the forefront of a new data age, and those that learn data science will be
able to take part in this thrilling new adventure, shaping our path forward in every
field. For you, that adventure starts now. Welcome aboard!
Jake Porway
Founder and Executive Director of DataKind™