San Jose State University Dat

Answer 1

Visualizing the Data

For this module’s presentation, I will be presenting my perspective on how selecting a particular color for a website, visualization, webpage, or presentation is important. The designing of a visualization depends on the aesthetics of how it is presented in what context and who will be the end consumers of the visualization (Lavrič, Bohak, & Marolt, 2017), the data visualization designers know how to keep in mind about the point of data visualization being presented such as size of the screen, quality of the screen and time of that report being generated. As the end-users are on boarding with the enhancing of digitalization both of the professional front and a personal friend the number of screens that each person goes through has been increasing. In the given link (https://www.visualisingdata.com/training/) “Shows the color combinations that one can use within a visualization to enhance the web based or applications on the screen. The American Society of disabilities uses the color Oracle to to understand how people with compromised color visualization/compromised color vision be able to view a specific section of the website or visualization. The American Association of people with disabilities (Honghui, 2020)lays down the rules and most of the popular web services and applications now I work hard to accommodate the needs of people with disabilities (Lavrič, Bohak, & Marolt, 2017). The given application can be used in the outlook web based app which needs the detection of any changes in the color matrix to make sure that it is compatible with the people with disabilities, also this can be extended into the data visualizations produce within the data repository such as SharePoint or Google Drive which host data (Lavrič, Bohak, & Marolt, 2017).

Bibliography

Honghui, M. H. (2020, June 09). DataV: Data Visualization on large high-resolution displays. Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/…

Lavrič, P., Bohak, C., & Marolt, M. (2017, August 11). Collaborative view-aligned annotations in web-based 3D medical data visualization. Retrieved from IEEE: https://ieeexplore.ieee.org/abstract/document/7973…

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Answer 2

HTML Color Pickers

HTML color pickers are devices for capturing the color of a given pixel in a screen area and illustrating its code in various forms, for example, HTML, HSL, RGX, and Delphi. The primary color of the pixel that our mouse cursor is recently pointing at is the selection of color and the part that is mainly current at the top right side of the app windows alongside the code of that specific color. Also, it organizes the pixel along with the vertical and hormonal range between the two latest selected pixels shown (Kirk, 2019).To choose the color and its code selection of the suitable arrangement in the code color plan list, then by pointing the mouse cursor for capturing the color that is Alt+X, it can also be altered in the Hotkeys menu options.

The primary role of HTML color pickers is processing many and millions of colors and harmonies of color. Color arrangement is specifically with pleasing combinations of more than two colors chords obtained from their relations on a color wheel(Kirk, 2021). Understanding the color chords and arrangement is considered more effective when discovering a likely color palette. It can be utilized as the standalone color scheme, the performance of the color scheme that majorly creates and makes the proposals of the fewer colors that can be a perfect combination with the final selected color.

Using these features, we can obtain the perfect color combination. For instance the Anny studio web, we have to select the primary color for the required design. The color picked will always provide a less color musically corresponding with it (Kirk, 2019). The colors are commonly an automatic proposal that can be selected and edited in any of the chosen of them for more progress. All the colors were picked because of the programs that are majorly considered from the color list on the right side of the windows, and when closing all the app, it is automatically saved in a given file. Color picker features majorly range from the code formats like HTML, HSL, HEX feature of sampling that is screen freeze evaluation of the pixels arrange can be measured between the points

References

Kirk, A. (2019). Data Visualization: a successful design process. Packt publishing LTD.

Kirk, A. (2021). Visualization Insights from Tom Worville’s “Lionel Messi’s Ten Stages of Greatness.” SAGE Publications, Ltd.

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San Jose State University Dat

Read the chapter 2, and 5 to answers these questions.

Chapter 2 Problems: 2, 5, 6, 10

2) Describe the difference in roles assumed by the validation partition and the test partition.

5) Using the concept of overfitting, explain why when a model is fit to training data, zero error with those data is not necessarily good.

6) In fitting a model to classify prospects as purchasers or nonpurchasers, a certain company drew the training data from internal data that include demographic and purchase information. Future data to be classified will be lists purchased from other sources, with demographic (but not purchase) data included. It was found that “refund issued” was a useful predictor in the training data. Why is this not an appropriate variable to include in the model?

10) Two models are applied to a dataset that has been partitioned. Model A is considerably more accurate than model B on the training data, but slightly less accurate than model B on the validation data. Which model are you more likely to consider for final deployment?

Chapter 5 Problems: 1, 2, 5, 7

1) A data mining routine has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) and 952 as non-fraudulent (920 correctly so). Construct the confusion matrix and calculate the overall error rate.

2) Suppose that this routine has an adjustable cutoff (threshold) mechanism by which you can alter the proportion of records classified as fraudulent. Describe how moving the cutoff up or down would affect
a. the classification error rate for records that are truly fraudulent
b. the classification error rate for records that are truly nonfraudulent

5) A large number of insurance records are to be examined to develop a model for predicting fraudulent claims. Of the claims in the historical database, 1% were judged 148 EVALUATING PREDICTIVE PERFORMANCE to be fraudulent. A sample is taken to develop a model, and oversampling is used to provide a balanced sample in light of the very low response rate. When applied to this sample (n = 800), the model ends up correctly classifying 310 frauds, and 270 nonfrauds. It missed 90 frauds, and classified 130 records incorrectly as frauds when they were not.
a. Produce the confusion matrix for the sample as it stands.
b. Find the adjusted misclassification rate (adjusting for the oversampling).
c. What percentage of new records would you expect to be classified as fraudulent?

7) Table 5.7 shows a small set of predictive model validation results for a classification model, with both actual values and propensities.
a. Calculate error rates, sensitivity, and specificity using cutoffs of 0.25, 0.5, and 0.75.
b. Create a decile-wise lift chart in R.

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Answer 1

Data visualization is a technique in where huge amounts of data is collected and with some tools based on analytics get a different kind of graphical type of representation. In this way the business can be viewed better in this kind of way. The business with huge amounts of data don’t know what to do with that (Sahay, 2017). There are different types of tools in data visualization. Some examples are Tableau, Microsoft Power BI, Excel, and Google charts. The different forms of graphical representations are charts, Pi charts, histograms, maps etc.

The key component of data visualization is determining what the company really wants and determining if there is any need of doing this technique (Sahay, 2017). The second component is determining where the data is taken and how it will be used to know how the business doing. This component gives what this will do to the business. The last component is understanding where to get data from and which format the data is in i.e., structured, or unstructured.

This course can give different approaches on how data visualization works. In this course can learn about how datasets are used in bringing the best in the visualization. This course will give different types of tools learning and understanding which can be used in the development of the student future in data visualization. This can be used in making business better.

References:

Sahay, A. (2017). Data visualization. Business Expert Press.

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Answer 2

Definition of data visualization

Data visualization is the process of representing and presenting data to leverage understanding. The process involves using the graphical representation of data to make understanding seamless (Kirk, 2019). Data visualization adopts visual tools such as graphs, charts, maps, etc., to make complex data seamless to understand and derive meaning. In the wake of big data, data visualization is a vital tool that enables decision-makers to comprehend and analyze complex data for effective decision-making. The major advantage of big data is that it makes understanding complex data seamless, leading to speedy decision-making.

Component of data visualization

There are three major components of data visualization, which include perceiving, interpreting, and comprehending data. At the perception stage, the audience tries to understand what they see (Kirk, 2019). It involves making simple cognitive functions and asking a simple question about what they can see. At the interpreting stage, the audience tries to get the meaning of what they see. The last component is comprehending, which involves getting the real meaning from what they have interpreted. It is important to note that different audiences may comprehend the visualization differently based on their understandings.

Techniques I want to learn from the course

Data visualization concepts are vital in the current big data era. Organizations are generating massive amounts of data from which they struggle to get meaningful revenue (Kirk, 2019). Thus, I aim to acquire competent data visualization skills to facilitate the seamless analysis and interpretation of big data. I want to learn color skills, graphs, charts, and other critical data visualization tools, ideal for my future career journey.

References

Kirk, A. (2019). Data visualization a handbook for data-driven design. Los Angeles, CA Sage. ISBN 978-1473912144

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Answer 1

Annotations to Enhance Graphics

Data visualizations convert complex data into readable and interactive data by providing the features that make the data easy to understand and effective when read. Annotations are among the considerations that make complex data easier to read by providing the users with the necessary support to understand the data in the visualizations. Annotations entail determining the amount of help that the consumers of a visualization need to understand data visualizations (Kirk, 2019). When creating visualizations, one must understand the users to determine if their capabilities support their understanding of the data visualizations. If the users could use some help, the developers determine the right help to enable the readers to understand the visualizations. The screenshot below is from Energy Upgrade California’s website (California Public Utilities Commission and California Energy Commission, 2020). It contains information about the rising temperatures using the historical average to show the current and changing heat waves. While the graphic is clear and has no distractions, annotations would help the readers interact more with the graphic and make it more readable.

The readings along the Y-axis will entail many estimations since the users have different accuracy levels when reading the provided scale. The graphic could use an annotated caption at the edge or along the shaded area showing different temperatures to help readers understand the represented Y-axis’s values and the data. Captions will provide localized values of the temperature value represented at the point the user hovers their mouse. The essence of using annotated captions is to help the readers easily read the temperatures without struggling to align the temperature level with the matching value on the Y-axis. Besides, the captions should have information concerning the years on the X-axis and the city. As a result, the users will find the visualization interactive and friendly, leading to the consumption of its intended information.

References

California Public Utilities Commission and California Energy Commission. (2020). Climate change in California: Facts, effects and solutions. Energy Upgrade California. https://www.energyupgradeca.org/climate-change/.

Kirk, A. (2019). Data visualisation: a handbook for data driven design (2nd ed.). Sage.

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Answer 2

Annotation

Annotation is the crucial components denoted as a drawing, charts, or graphs that provide any extra information or ideas highlighted in the interest of using the disambiguation purposes. A presentation should establish two features of the presented data: Show links within the information that are too complicated to express with words. Make it easy for the audience to immediately absorb the information offered and analyze the consequences from that data. Annotation creates a clear component that turns the boring graphical representation into a meaningful or interesting way of conveying the information (Kirk, 2015). However, adding the accuracy of the data into the charts or graphical representation enables the audience to understand the presentation.

Using this website as a data annotation platform https://www.data-to-viz.com/caveat/annotation.html. It contains the dataset of figures of spaghetti charts which elaborate on the groups of the outcomes that have confusing figures that are hard to understand. Hence, every pattern is hard to be elaborated (Sinar, 2015).

Computers can analyze the world via a visual lens or a fresh, enlightened viewpoint in the digital era. Image annotation is one of the most important jobs a computer has. Computer vision, robotic vision, face recognition, and machine learning systems to analyze pictures depend on image annotation (Kirk, 2015). In enhancing the graphical representation above to have a clear understanding, I will highlight the main point such that when I want to elaborate on the characteristics of Amanda, I will increase the intensity of the line of Amanda shown below:

References

Kirk, A. (2015). Data Visualization: a successful design process. Packt publishing LTD. https://www.data-to-viz.com/caveat/annotation.html

Sinar, E. F. (2015). Data visualization. In Big Data at Work (pp. 129-171). Routledge.

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