Data Science for Dummies, 2nd Edition

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. Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark 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. Title Page......Page 2 Copyright Page......Page 3 Table of Contents......Page 6 Foreword......Page 16 Introduction......Page 18 Foolish Assumptions......Page 19 Beyond the Book......Page 20 Where to Go from Here......Page 21 Part 1 Getting Started with Data Science......Page 22 Chapter 1 Wrapping Your Head around Data Science......Page 24 Seeing Who Can Make Use of Data Science......Page 25 Collecting, querying, and consuming data......Page 27 Applying mathematical modeling to data science tasks......Page 28 Applying data science to a subject area......Page 29 Assembling your own in-house team......Page 31 Leveraging cloud-based platform solutions......Page 32 Letting Data Science Make You More Marketable......Page 33 Chapter 2 Exploring Data Engineering Pipelines and Infrastructure......Page 34 Handling data velocity......Page 35 Dealing with data variety......Page 36 Identifying Big Data Sources......Page 37 Defining data science......Page 38 Defining data engineering......Page 39 Comparing data scientists and data engineers......Page 40 Digging into MapReduce......Page 41 Stepping into real-time processing......Page 43 Storing data on the Hadoop distributed file system (HDFS)......Page 44 Identifying Alternative Big Data Solutions......Page 45 Introducing NoSQL databases......Page 46 Identifying the business challenge......Page 47 Boasting about benefits......Page 49 Chapter 3 Applying Data-Driven Insights to Business and Industry......Page 50 Benefiting from Business-Centric Data Science......Page 51 Types of analytics......Page 52 Data wrangling......Page 53 Taking Action on Business Insights......Page 54 Business intelligence, defined......Page 56 Technologies and skillsets that are useful in business intelligence......Page 57 Defining Business-Centric Data Science......Page 58 Kinds of data that are useful in business-centric data science......Page 59 Making business value from machine learning methods......Page 60 Differentiating between Business Intelligence and Business-Centric Data Science......Page 61 Knowing Whom to Call to Get the Job Done Right......Page 62 Exploring Data Science in Business: A Data-Driven Business Success Story......Page 63 Part 2 Using Data Science to Extract Meaning from Your Data......Page 66 Defining Machine Learning and Its Processes......Page 68 Getting familiar with machine learning terms......Page 69 Learning with unsupervised algorithms......Page 70 Selecting algorithms based on function......Page 71 Using Spark to generate real-time big data analytics......Page 75 Chapter 5 Math, Probability, and Statistical Modeling......Page 78 Exploring Probability and Inferential Statistics......Page 79 Probability distributions......Page 80 Conditional probability with Naïve Bayes......Page 82 Ranking variable-pairs using Spearman’s rank correlation......Page 83 Decomposing data to reduce dimensionality......Page 84 Reducing dimensionality with factor analysis......Page 86 Modeling Decisions with Multi-Criteria Decision Making......Page 87 Turning to traditional MCDM......Page 88 Focusing on fuzzy MCDM......Page 89 Linear regression......Page 90 Ordinary least squares (OLS) regression methods......Page 91 Analyzing extreme values......Page 92 Detecting outliers with univariate analysis......Page 93 Detecting outliers with multivariate analysis......Page 94 Identifying patterns in time series......Page 95 Modeling univariate time series data......Page 96 Introducing Clustering Basics......Page 98 Getting to know clustering algorithms......Page 99 Looking at clustering similarity metrics......Page 102 Clustering with the k-means algorithm......Page 103 Estimating clusters with kernel density estimation (KDE)......Page 104 Clustering with hierarchical algorithms......Page 105 Dabbling in the DBScan neighborhood......Page 107 Categorizing Data with Decision Tree and Random Forest Algorithms......Page 108 Chapter 7 Modeling with Instances......Page 110 Reintroducing clustering concepts......Page 111 Getting to know classification algorithms......Page 112 Making Sense of Data with Nearest Neighbor Analysis......Page 114 Classifying Data with Average Nearest Neighbor Algorithms......Page 115 Classifying with K-Nearest Neighbor Algorithms......Page 118 Understanding how the k-nearest neighbor algorithm works......Page 119 Knowing when to use the k-nearest neighbor algorithm......Page 120 Seeing k-nearest neighbor algorithms in action......Page 121 Seeing average nearest neighbor algorithms in action......Page 122 Chapter 8 Building Models That Operate Internet-of-Things Devices......Page 124 Learning the lingo......Page 125 Spark streaming for the IoT......Page 127 Digging into the Data Science Approaches......Page 128 Geospatial analysis......Page 129 Advancing Artificial Intelligence Innovation......Page 130 Part 3 Creating Data Visualizations That Clearly Communicate Meaning......Page 132 Chapter 9 Following the Principles of Data Visualization Design......Page 134 Data showcasing for analysts......Page 135 Designing to Meet the Needs of Your Target Audience......Page 136 Step 1: Brainstorm (about Brenda)......Page 137 Step 3: Choose the most functional visualization type for your purpose......Page 138 Inducing a calculating, exacting response......Page 139 Eliciting a strong emotional response......Page 140 Choosing How to Add Context......Page 141 Creating context with graphical elements......Page 142 Standard chart graphics......Page 144 Comparative graphics......Page 147 Statistical plots......Page 151 Topology structures......Page 152 Spatial plots and maps......Page 155 Choosing a Data Graphic......Page 157 Introducing the D3.js Library......Page 158 Knowing When to Use D3.js (and When Not To)......Page 159 Getting Started in D3.js......Page 160 Bringing in the HTML and DOM......Page 161 Bringing in the JavaScript and SVG......Page 162 Bringing in the web servers and PHP......Page 163 Implementing More Advanced Concepts and Practices in D3.js......Page 164 Getting to know chain syntax......Page 168 Getting to know scales......Page 169 Getting to know transitions and interactions......Page 170 Chapter 11 Web-Based Applications for Visualization Design......Page 174 Designing Data Visualizations for Collaboration......Page 175 Visualizing and collaborating with Plotly......Page 176 Talking about Tableau Public......Page 178 Visualizing Spatial Data with Online Geographic Tools......Page 179 Making pretty maps with OpenHeatMap......Page 180 Mapmaking and spatial data analytics with CartoDB......Page 181 Making pretty data graphics with Google Fusion Tables......Page 183 Using iCharts for web-based data visualization......Page 184 Using RAW for web-based data visualization......Page 185 Making cool infographics with Infogr.am......Page 187 Making cool infographics with Piktochart......Page 189 Chapter 12 Exploring Best Practices in Dashboard Design......Page 190 Focusing on the Audience......Page 191 Starting with the Big Picture......Page 192 Getting the Details Right......Page 193 Testing Your Design......Page 195 Chapter 13 Making Maps from Spatial Data......Page 196 Getting into the Basics of GIS......Page 197 Spatial databases......Page 198 File formats in GIS......Page 199 Map projections and coordinate systems......Page 202 Querying spatial data......Page 204 Buffering and proximity functions......Page 205 Using layer overlay analysis......Page 206 Reclassifying spatial data......Page 207 Getting to know the QGIS interface......Page 208 Adding a vector layer in QGIS......Page 209 Displaying data in QGIS......Page 210 Part 4 Computing for Data Science......Page 216 Chapter 14 Using Python for Data Science......Page 218 Sorting Out the Python Data Types......Page 220 Lists in Python......Page 221 Dictionaries in Python......Page 222 Putting Loops to Good Use in Python......Page 223 Having Fun with Functions......Page 224 Keeping Cool with Classes......Page 225 Checking Out Some Useful Python Libraries......Page 227 Saying hello to the NumPy library......Page 228 Peeking into the Pandas offering......Page 230 Bonding with MatPlotLib for data visualization......Page 231 Learning from data with Scikit-learn......Page 232 Installing Python on the Mac and Windows OS......Page 233 Loading CSV files......Page 235 Calculating a weighted average......Page 236 Drawing trendlines......Page 239 Chapter 15 Using Open Source R for Data Science......Page 242 R’s Basic Vocabulary......Page 243 Delving into Functions and Operators......Page 246 Iterating in R......Page 249 Observing How Objects Work......Page 251 Sorting Out Popular Statistical Analysis Packages......Page 253 Visualizing R statistics with ggplot2......Page 255 Analyzing networks with statnet and igraph......Page 256 Mapping and analyzing spatial point patterns with spatstat......Page 257 Chapter 16 Using SQL in Data Science......Page 258 Getting a Handle on Relational Databases and SQL......Page 259 Investing Some Effort into Database Design......Page 262 Designing constraints properly......Page 263 Normalizing your database......Page 264 Narrowing the Focus with SQL Functions......Page 266 Making Life Easier with Excel......Page 272 Using Excel to quickly get to know your data......Page 273 Reformatting and summarizing with pivot tables......Page 278 Automating Excel tasks with macros......Page 279 Using KNIME for Advanced Data Analytics......Page 281 Using KNIME to make the most of your social data......Page 282 Using KNIME for environmental good stewardship......Page 283 Part 5 Applying Domain Expertise to Solve Real-World Problems Using Data Science......Page 284 Chapter 18 Data Science in Journalism: Nailing Down the Five Ws (and an H)......Page 286 Who Is the Audience?......Page 287 Who comprises the audience......Page 288 What: Getting Directly to the Point......Page 289 Bringing Data Journalism to Life: The Black Budget......Page 290 When as the context to your story......Page 291 Where Does the Story Matter?......Page 292 Where should the story be published?......Page 293 Why your audience should care......Page 294 Finding stories in your data......Page 295 Scraping data......Page 296 Finding and Telling Your Data’s Story......Page 297 Spotting strange trends and outliers......Page 298 Examining context to understand the significance of data......Page 300 Emphasizing the story through visualization......Page 301 Creating compelling and highly focused narratives......Page 302 Chapter 19 Delving into Environmental Data Science......Page 304 Examining the types of problems solved......Page 305 Defining environmental intelligence......Page 306 Identifying major organizations that work in environmental intelligence......Page 307 Making positive impacts with environmental intelligence......Page 308 Dabbling in data science......Page 310 Modeling natural resources to solve environmental problems......Page 311 Using Spatial Statistics to Predict for Environmental Variation across Space......Page 312 Describing the data science that’s involved......Page 313 Addressing environmental issues with spatial statistics......Page 314 Chapter 20 Data Science for Driving Growth in E-Commerce......Page 316 Making Sense of Data for E-Commerce Growth......Page 319 Optimizing E-Commerce Business Systems......Page 320 Angling in on analytics......Page 321 Talking about testing your strategies......Page 325 Segmenting and targeting for success......Page 328 Chapter 21 Using Data Science to Describe and Predict Criminal Activity......Page 332 Temporal Analysis for Crime Prevention and Monitoring......Page 333 Crime mapping with GIS technology......Page 334 Going one step further with location-allocation analysis......Page 335 Analyzing complex spatial statistics to better understand crime......Page 336 Caving in on civil rights......Page 339 Taking on technical limitations......Page 340 Part 6 The Part of Tens......Page 342 Chapter 22 Ten Phenomenal Resources for Open Data......Page 344 Digging through data.gov......Page 345 Checking Out Canada Open Data......Page 346 Diving into data.gov.uk......Page 347 Checking Out U.S. Census Bureau Data......Page 348 Knowing NASA Data......Page 349 Wrangling World Bank Data......Page 350 Getting to Know Knoema Data......Page 351 Queuing Up with Quandl Data......Page 352 Exploring Exversion Data......Page 353 Mapping OpenStreetMap Spatial Data......Page 354 Chapter 23 Ten Free Data Science Tools and Applications......Page 356 Getting Shiny by RStudio......Page 357 Mapping with rMaps......Page 358 Scraping data with import.io......Page 359 Wrangling data with DataWrangler......Page 360 Looking into Data Exploration Tools......Page 361 Getting up to speed in Gephi......Page 362 Getting a little Weave up your sleeve......Page 364 Checking out Knoema’s data visualization offerings......Page 365 Index......Page 368 EULA......Page 0