Learning Outcomes By the end of this section, you should be able to: A data scientist or researcher is often interested in generating more advanced visual representations such as scatterplots, correlation maps, and three-dimensional (3D) representations of data. Visualizations or…
Learning Outcomes By the end of this section, you should be able to: Geospatial data refers to data that describes the geographic location, shape, size, and other attributes relative to a location on the Earth’s surface. Geospatial data can be captured,…
Learning Outcomes By the end of this section, you should be able to: We introduced probability distributions in Discrete and Continuous Probability Distributions. Recall that the binomial distribution and the Poisson distribution are examples of discrete probability distributions, and the normal distribution…
Learning Outcomes By the end of this section, you should be able to: Researchers are frequently interested in analyzing and interpreting data over time. Recall from Time and Series Forecasting that any set of data that consists of numerical measurements of the…
Learning Outcomes By the end of this section, you should be able to: We’ve seen that data may originate from surveys, experiments, polls, questionnaires, sensors, or other sources. Data may be represented as numeric values, such as age or salary,…
Figure 9.1 Data visualization techniques can involve collecting and analyzing geospatial data, such as measurements collected by the National Park Service during the High-Efficiency Trail Assessment Process (HETAP). (credit: modification of work “Trail Accessibility Assessments” by GlacierNPS/Flickr, Public Domain) Chapter Outline 9.1 Encoding…
Learning Outcomes By the end of this section, you should be able to: After data is collected and stored securely, it should be analyzed to extract insights from the raw data using authorized tools and approved procedures, including data validation…
Learning Outcomes By the end of this section, you should be able to: In today’s technology, industries produce massive amounts of data every day, and with the improvement in digital resources, analyzing and modeling the delivered data has evolved into…
Learning Outcomes By the end of this section, you should be able to: Data collection is an essential practice in many fields, and those performing the data collection are responsible for adhering to the highest ethical standards so that the best…
Figure 8.1 Balancing the ethical aspects of the evolving practice of data science requires a careful approach throughout the data science cycle (i.e., during collection, analysis, and use of data, as well as its dissemination). (credit: modification of work “Data Security Breach”…