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IN THIS PART … Find out how Python is being used for data science projects. Look at your open-source data science options. (Can you say “R”?) See how SQL can be put to new (data science) uses. Explore old programs (Excel) and new (KNIME).
IN THIS CHAPTER Chapter 14 Using Python for Data Science Taking on data types Looking at how loops work Forming simple user-defined functions Defining classes in Python Exploring data science Python libraries Practicing data analysis for yourself Although popular programming languages like Java and C++ are good for developing stand-alone desktop applications, Python’s versatility makes it an ideal programming language for processing, analyzing, and visualizing data. For this reason, Python has earned a reputation of excellence in the data science field, where it has been widely adopted over the past decade. In fact, Python has become so popular that it seems to have stolen ground from R — the other free, widely adopted programming language for data science applications. Python’s status as one of the more popular programming languages out there can be linked to the fact that it’s relatively easy to master and it allows users to accomplish several tasks using just a few lines of code. In this chapter, I first introduce you to the fundamental concepts of programming with Python (such as data types, function, and classes), and then I present the information you need to know to set up a standard Python working environment in Jupyter. I also introduce some of the best Python libraries for manipulating data, performing statistical computations, creating data visualizations, and completing other related tasks. Lastly, I walk you through some scenarios designed to illustrate how Python can best help you analyze data. You can use Python to do anything, from simple mathematical operations to data visualization and even machine learning and predictive analytics. Here’s an example of a basic math operation in Python (Figure 14-1 shows an example — taken from Python’s MatPlotLib library — of a more advanced output): >>> 2.5+3 5.5
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Data Science For Dummies ® , 2nd E
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Data Science For Dummies® To view
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Choosing a Data Graphic Chapter 10:
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Getting to Know Knoema Data Queuing
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costs. I’ve worked hard to make s
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Foreword We live in exciting, even
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Part 1 Getting Started with Data Sc
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Chapter 1 Wrapping Your Head around
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and the well-being of their busines
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quantitative description of the wor
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Alternatives Organizations and thei
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support enhancements, finance and b
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Because the three Vs of big data ar
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FIGURE 2-1: Popular sources of big
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Defining data engineering If engine
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volumes of data in-batch — where
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there yet. Real-time, stream-proces
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estrictive. MPP is quicker and easi
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The company had the following three
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Decrease financial risks. A busines
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Granularity is a measure of a datas
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you. Unless you’re a data technol
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The term multivariate refers to mor
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management. Making business value f
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dashboards and tabular data reports
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Part 2 Using Data Science to Extrac
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Chapter 4 Machine Learning: Learnin
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dataset composed of historical valu
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activation function is a mathematic
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applications have been known to imp
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out a smaller section of the datase
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To understand discrete and continuo
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Ranking variable-pairs using Spearm
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in shared variance — when a varia
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virtually riskless investments (U.S
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Logistic regression Logistic regres
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It is cumbersome to detect outliers
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FIGURE 5-4: A comparison of pattern
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Chapter 6 Using Clustering to Subdi
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FIGURE 6-1: A simple scatter plot.
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Looking at clustering similarity me
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are separated by wide, sparse areas
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FIGURE 6-4: A schematic layout of a
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With DBScan, take an iterative, tri
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IN THIS CHAPTER Chapter 7 Modeling
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FIGURE 7-1: A classification of Wor
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located. What to do? One easy solut
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FIGURE 7-2: The distances between t
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the kNN algorithm has been ranked a
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Solving Real-World Problems with Ne
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Chapter 8 Building Models That Oper
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operating. Later in this chapter, y
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Taking on time series Most IoT sens
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in order to operate and drive thems
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IN THIS PART … Explore the princi
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order to help members of this audie
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What social, political, caused-base
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FIGURE 9-1: This design style conve
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to create a sense of relative persp
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KNOWING WHEN TO GET PERSUASIVE Pers
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FIGURE 9-6: A bar chart. Source: Ly
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same category. To ensure that it do
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FIGURE 9-12: A stacked chart. FIGUR
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Source: Lynda.com, Python for DS FI
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clustering and machine learning met
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Source: Lynda.com, Python for DS FI
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Though many data graphic types can
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Page 139 and 140: Just in case you’re not aware, HT
Page 141 and 142: Bringing in the web servers and PHP
Page 143 and 144: }, { position: 4, quantity: 10 }];
Page 145 and 146: var scale_y = d3.scale.linear() .do
Page 147 and 148: Chapter 11 Web-Based Applications f
Page 149 and 150: For all you techies out there, a co
Page 151 and 152: You can also use the dashboard to a
Page 153 and 154: FIGURE 11-3: Choropleth map in Open
Page 155 and 156: Map layers are spatial datasets tha
Page 157 and 158: FIGURE 11-6: A bar chart in iCharts
Page 159 and 160: fashion using a data-graphing appli
Page 161 and 162: need to produce documents in an inf
Page 163 and 164: clunky. These design elements consu
Page 165 and 166: nitty-gritty details. Even if every
Page 167 and 168: Chapter 13 Making Maps from Spatial
Page 169 and 170: FIGURE 13-2: Spatial data described
Page 171 and 172: FIGURE 13-4: A street and neighborh
Page 173 and 174: area, sum up the volume of snow tha
Page 175 and 176: Now that you know what types of coo
Page 177 and 178: FIGURE 13-8: Buffered features at t
Page 179 and 180: the values of an attribute in a vec
Page 181 and 182: FIGURE 13-11: Your new QGIS setup.
Page 183 and 184: FIGURE 13-13: A layer added into QG
Page 185 and 186: FIGURE 13-15: Layer properties in Q
Page 187: Part 4 Computing for Data Science
Page 191 and 192: programmed to do. Classes, on the o
Page 193 and 194: Sets in Python A set is another dat
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Page 197 and 198: Be sure to import the library into
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Page 201 and 202: FIGURE 14-2: Time-series plot of mo
Page 203 and 204: When you download your free Python
Page 205 and 206: import numpy as np import matplotli
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Page 209 and 210: Chapter 15 Using Open Source R for
Page 211 and 212: mode. Data frames are structured in
Page 213 and 214: handle that task: You simply define
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Page 217 and 218: information and use a linear regres
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Page 221 and 222: Therefore, knowing how to make sens
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Page 225 and 226: whether SQL should be pronounced
Page 227 and 228: Let the following scenario serve as
Page 229 and 230: Imagine that you have a text field
Page 231 and 232: into native R or Python data forms
Page 233 and 234: LISTING 16-3 A Full Outer JOIN SELE
Page 235 and 236: Chapter 17 Doing Data Science with
Page 237 and 238: FIGURE 17-1: The full dataset that
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FIGURE 17-3: Spotting outliers in a
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FIGURE 17-5: Excel XY (scatter) plo
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Automating Excel tasks with macros
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Absolute: After you start recording
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at its public workflow server (see
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IN THIS PART … Explore the impact
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Let me emphasize here, at the begin
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What: Getting Directly to the Point
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journalist you walk a fine line bet
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take tremendous value from consumin
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Because the library won’t budge o
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correlation coefficient of 0.86. Th
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Looking back to the World Bank Glob
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Chapter 19 Delving into Environment
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evolution of EI away from standard
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Non-relational database technologie
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in a lack of stable water resources
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and land cover. Through his recent
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concepts and methods you can use to
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narrative, and conversation. Custom
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Webtrends (http://webtrends.com): O
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click heat map data visualizations
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functionality for A/B split testing
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Segment Builder, check out the Goog
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geographic region. After you distin
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temporally relevant but not geo-ref
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FIGURE 21-1: A map product derived
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ehavior and information about prese
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the time). Officer Bob, on said str
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Part 6
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IN THIS PART … Find out all about
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national statistics, election resul
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yet available. Like Data.gov (discu
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Geology Engineering Some examples f
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FIGURE 22-1: The index of insect re
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When you work on collaborative proj
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and tools, you can create results t
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ideas behind web-scraping in Chapte
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Missing data can indicate a formatt
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FIGURE 23-3: A Gephi hairball graph
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Checking out Knoema’s data visual
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FIGURE 23-6: A map of Eurostat data
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Dedication I dedicate this book to
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Publisher’s Acknowledgments Acqui
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