Lillian Pierson - Data Science For Dummies

9 Part 5: The Part of Tens Chapter 19: Ten Phenomenal Resources for Open Data Digging Through data.gov Checking Out Canada Open Data Diving into data.gov.uk Checking Out US Census Bureau Data Accessing NASA Data Wrangling World Bank Data Getting to Know Knoema Data Queuing Up with Quandl Data Exploring Exversion Data Mapping OpenStreetMap Spatial Data Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications Scraping, Collecting, and Handling Data Tools Data-Exploration Tools Designing Data Visualizations Communicating with Infographics

10 Index

11 About the Author

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13 Connect with Dummies

14 End User License Agreement

1 Chapter 5TABLE 5-1: Business Analyst Stu’s Employee Data

2 Chapter 6TABLE 6-1: Popular Operators

3 Chapter 8TABLE 8-1: Types of Data Visualization, by Audience

4 Chapter 10TABLE 10-1 Operational Improvement Use Cases for Data Science, by IndustryTABLE 10-2 Theoretical Business Use Case for Automating Client InteractionsTABLE 10-3 Theoretical Technology Stack for Automating Client InteractionsTABLE 10-4: Theoretical Business Use Case for Real-Time Optimized Logistics Rout...TABLE 10-5 Theoretical Technology Stack for Real-Time Optimized Logistics Routin...TABLE 10-6 Theoretical Business Use Case for Increasing Content-Generation RatesTABLE 10-7 Theoretical Technology Stack for Increasing Content Generation Rates

5 Chapter 12TABLE 12-1 Theoretical Business Use Case for Analyzing Customer SentimentTABLE 12-2 Theoretical Business Use Case for Real-Time Decision SupportTABLE 12-3 Theoretical Technology Stack for Automating Client Interactions

6 Chapter 13TABLE 13-1: An Example of n- grams in Phrase and Entity Analysis

1 Chapter 2FIGURE 2-1: Popular sources of big data.

2 Chapter 3FIGURE 3-1: A example of a simple random sampleFIGURE 3-2: Unsupervised machine learning breaks down unlabeled data into subgr...FIGURE 3-3: Machine learning algorithms can be broken down by function.FIGURE 3-4: Neural networks are connected layers of artificial neural units.FIGURE 3-5: A deep learning network is a neural network with more than one hidd...

3 Chapter 4FIGURE 4-1: An example of a linear relationship between months and YouTube subs...FIGURE 4-2: An example of a non-linear relationship between watch time and % vi...FIGURE 4-3: Applying SVD to compress a sparse, clean dataset.FIGURE 4-4: Applying SVD to clean and compress a sparse, dirty dataset.FIGURE 4-5: You can use SVD to decompose data down to u, S, and V matrices.FIGURE 4-6: Linear regression used to predict home prices based on the number o...FIGURE 4-7: Spotting outliers with a Tukey boxplot. FIGURE 4-8: Using PCA to spot outliers. FIGURE 4-9: A comparison of patterns exhibited by time series.FIGURE 4-10 An example of an ARMA forecast model.

4 Chapter 5FIGURE 5-1: A simple scatterplot.FIGURE 5-2: A simple scatterplot, showing eyeballed estimations of clustering.FIGURE 5-3: KDE smoothing of the World Bank’s Income and Education data scatter...FIGURE 5-4: A schematic layout of a sample dendrogram.FIGURE 5-5: Using DBScan to detect outliers (in black) within the Iris dataset....FIGURE 5-6: A decision tree model predicts survival rates from the Titanic cata...FIGURE 5-7: Using the Continent feature to classify World Bank data.FIGURE 5-8: The distances between the employees’ tuples.FIGURE 5-9: Finding the average similarity between employees.FIGURE 5-10: How kNN works.

5 Chapter 6FIGURE 6-1: Sample output from Python’s MatPlotLib library.FIGURE 6-2: Time-series plot of monthly snow depth data.FIGURE 6-3: The relationship between atomic vectors, lists, and data frame obje...FIGURE 6-4: Linear regression coefficients from R, translated into a plain math...FIGURE 6-5: A scatterplot, generated in the ggplot2 package.FIGURE 6-6: A network diagram that was generated using the statnet package.

6 Chapter 7FIGURE 7-1: An example of how SQL is human-readable.FIGURE 7-2: A relationship between data tables that share a column.FIGURE 7-3: The full dataset that tracks employee sales performance.FIGURE 7-4: The sales performance dataset, filtered to show only Abbie’s record...FIGURE 7-5: Spotting outliers in a tabular dataset with conditional formatting ...FIGURE 7-6: Spotting outliers in a tabular dataset with color scales.FIGURE 7-7: Excel XY (scatter) plots provide a simple way to visually detect ou...FIGURE 7-8: Excel line charts make it easy to visually detect trends in data.FIGURE 7-9: A long dataset and a wide spreadsheet.FIGURE 7-10: Creating a wide data table from the long dataset via a PivotTable.FIGURE 7-11: Using a macro to insert empty cells between values.

7 Chapter 8FIGURE 8-1: This design style conveys a calculating and exacting feel.FIGURE 8-2: This design style is intended to evoke an emotional response.FIGURE 8-3: Data visualization versus data graphics.FIGURE 8-4: Types of data graphics, broken down by audience and data visualizat...FIGURE 8-5: An area chart in three dimensions. FIGURE 8-6: A bar chart showing the area of US states by their acreage, in thou...FIGURE 8-7: A line chart. FIGURE 8-8: A pie chart. FIGURE 8-9: A bubble chart.FIGURE 8-10: A packed circle diagram.FIGURE 8-11: A Gantt chart.FIGURE 8-12: A stacked chart.FIGURE 8-13: A tree map.FIGURE 8-14: A simple word cloud.FIGURE 8-15: A histogram. FIGURE 8-16: A scatterplot. FIGURE 8-17: A scatterplot matrix. FIGURE 8-18: A linear topology.FIGURE 8-19: A graph mesh network topology.FIGURE 8-20: A hierarchical tree topology.FIGURE 8-21: A Cloropleth map. FIGURE 8-22: A point map.FIGURE 8-23: A raster surface map.FIGURE 8-24: Here you see the importance of selecting effective data graphics.FIGURE 8-25: Using annotation to create context. FIGURE 8-26: Using graphical elements to create context.

8 Chapter 9FIGURE 9-1: The data science Venn diagram.FIGURE 9-2: The mechanics by which traditional data roles increase business pro...FIGURE 9-3: The three layers of the business acumen cake.FIGURE 9-4: My STAR framework, for managing profit-forming data science project...FIGURE 9-5: Five routes by which data science impacts business.FIGURE 9-6: A simple business use case diagram.

9 Chapter 10FIGURE 10-1: AI-enabled robotics automation.FIGURE 10-2: Diagram of a debt-recovery business use case.FIGURE 10-3: Diagram of a logistics-routing business use case.FIGURE 10-4: Diagram of a content publishing business use case.

10 Chapter 11FIGURE 11-1: A channel scorecard.

11 Chapter 12FIGURE 12-1: The data-to-dollars flowchart.FIGURE 12-2: The business intelligence path within the data-to-dollars flowchar...FIGURE 12-3: An example of a modern, SaaS BI tool.FIGURE 12-4: The analytics path within the data-to-dollars flowchart.FIGURE 12-5: An example of analytics that report on real-time data.FIGURE 12-6: An example of analytics that report on external data.FIGURE 12-7: The data science path within the data-to-dollars flowchart.FIGURE 12-8: A business use case diagram for automated decision support AI for ...

12 Chapter 13FIGURE 13-1: NLP enabled affective computing.FIGURE 13-2: Sentiment analysis flowchart.FIGURE 13-3: An example of raw data ingested into an NLP system.FIGURE 13-4: An example of text normalization.FIGURE 13-5: An example of text tokenization.FIGURE 13-6: Forrester's estimated ROI for a client.

13 Chapter 14FIGURE 14-1: The three main ways to monetize data.FIGURE 14-2: The value proposition of personal data in advertising.

14 Chapter 15FIGURE 15-1: The classic power dynamic within a data science team.FIGURE 15-2: The need for business vision, mission, and values.FIGURE 15-3: My STAR framework, for managing profit-forming data science projec...FIGURE 15-4: An example of data architecture.FIGURE 15-5: A list of popular data technologies, broken down according to func...FIGURE 15-6: The structure of a data dictionary.FIGURE 15-7: The relationship between a dataset and the data dictionary that de...FIGURE 15-8: An example of data quality and silo issues.FIGURE 15-9: An organizational chart.FIGURE 15-10: Some foundations from which great data professionals emerge.FIGURE 15-11: Questions and recipients inside my question-and-asset request dat...FIGURE 15-12: Some filtered questions, ready to send to relevant finance person...