Data Science for Dummies, 2nd Edition

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