zetom.info Tutorials Interactive Data Visualization Pdf


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trademarks of O'Reilly Media, Inc. Interactive Data Visualization for the Web, the cover . Bitmaps. PDF. Table of Contents | vii. HTML. (Hypertext Markup Language). • Used to mark up the content of a web page by adding a structure to the elements in the web page. • Elements. That man is Scott Murray, and his book is Interactive Data Visualization for the Web. —Wendy . Bitmaps PDF SVG viii | Table of Contents

Interactive Data Visualization Pdf

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Create and publish your own interactive data visualization projects on the web— even if you have little or no experience with data visualization or web. Interactive Data Visualization, Big Data, JavaScript, Open Evolution of Web -based Interactive Data Visualization. 6 2 million PDF-files. Interactive Data Visualization for the Web, 2nd Ed. The second edition of my wildly successful book on D3, data visualization, download from site · download from Powell's · download from an independent bookstore · download from zetom.info (EPUB, PDF).

What You Will Learn Produce designer output that will astound your bosses and peers.

Drive business intelligence from Excel using BI in the Cloud. Gather source data from corporate and public sources.

Integrate charts, maps, and tables to deliver visually stunning information. Discover new insights as you chop and tweak your data as never before. Adapt delivery to mobile devices.

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Outshine competing products and enhance existing skills. They have a nice collection of business dashboards and live demos for inspiration. Their charts and maps work across all devices and platforms, are highly customizable and have beautiful interactions.

But you can always get started with their unrestricted free trial and then download if you like it.

If the application is big and complex, then libraries like Google Charts and FusionCharts makes sense, otherwise for small hobby projects Chart. It uses HTML5 canvas element for rendering charts. All the charts are responsive and use flat design.

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It is one of the most popular open-source charting librar ies to emerge recently. Check out the documentation for live examples of all six chart types.

It offers a decent number of charts which covers the most commonly used chart types like bar, area, pie and gauges. It is flexible and user friendly because Google! You can view this gallery to get an idea of various charts and the type of interactions to expect. Highcharts Highcharts is another big player in the charting space.

Like FusionCharts, it also offers a diverse range of charts and maps right out of the box.

Other than normal charts, it also offers a different package for stock charts called Highstock which is also feature rich. You can view the various chart types it offers in the demo section. Highcharts is free for non-commercial and personal use, but you will have to download a licence for deploying it in commercial applications.

Leaflet Leaflet is an open-source library developed by Vladimir Agafonkin for mobile-friendly interactive maps. It is extremely light at just 33kb and has lots of features for making any kind of maps. In the words of author: Leaflet is designed with simplicity, performance and usability in mind.

A summary of all filtering steps, advantages and example results are displayed in Fig. The tooltip and label activities Fig.

Description clockwise starting from top right.

The color button allows not only to change the color of an existing graph but also to encode categorical or numerical values by color. For charts such as scatter, principal component driver or line plot the direct chart elements are color encoded. For hierarchical clustering an additional color column is added.

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Similar to the color button, size can be used as another level of information content in a chart. Scatter points can be annotated by selecting a column for the label button. The tooltip button can be used to hover over chart items to quickly screen through the data bottom-right.

The selection tool allows the user to select data in a scatter plot for sub setting and further analysis bottom-left. The filter button can be used to quickly apply categorical and numerical filtering top-left. Full size image Instant Clue comprises a Statistical Toolbox for multivariate data analysis Instant Clue promotes the visual analysis of data, but also offers several statistical tests that are applied in an interactive way. Several tests are automatically performed and do not require further action by the user.

By clicking on the desired groups that should be compared, the test is automatically calculated, and the p-value is indicated in the chart above lines between tested groups Fig. In addition, performed tests are stored and can be exported at any time. Noteworthy, if an activity each test is an activity cannot handle missing values, the data are automatically filtered before submission to the specified activity, without changing the source data.

The toolbox covers numerous techniques, including supervised learning, clustering, dimensional reduction, time series as well as curve fitting. In the following we describe and present results of supervised learning, time series and curve fitting to illustrate the functionality and ease of the presented software.

Figure 3 Statistical Toolbox. Description clockwise starting from top-right.

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The statistical toolbox offers functionality to compare two-groups using non-parametric and parametric tests e. To integrate multiple omics data, users can use method such as sparse generalized canonical correlation analysis SGCCA 35 to select features such as genes, proteins, miRNAs that strongly contribute to the multi-omics signature right.

A comprehensive toolbox for supervised learning has been implemented using the scikit-learn library allowing the user to set up pipelines with data-preprocessing, feature selection and prediction bottom.

Time series data can also be explored and techniques such as base line correction and measuring the area under curve AUC are implemented in an interactive way. In general, these methods are utilized to predict class memberships based on a learning process. The training dataset consist of n samples and m features as well as the class labels.

These data can be used to train an estimator which in turn is able to predict a health condition based on the used features for uncharacterized subjects. Such classification tasks were successfully used to predict new kinase-substrate relationships 3 and many other applications in biological and medical science 27 , Instant Clue offers several functions to establish an estimator for prediction, based on the scikit-learn library S4 shows the dialog window to interactively construct a prediction pipeline.

To increase the generalization ability, accuracy and prediction speed of an estimator it is often useful to select the most important features or to apply a dimensional reduction technique feature selection before training an estimator.

Established pipelines can be saved and subsequently used to predict class memberships of unseen data. Thus, Instant Clue provides a convenient way to accomplish classification tasks. Moreover, the software offers several functions to analyze data in an unsupervised fashion such as Principal Component Analysis PCA , k-means or Density-based spatial clustering of applications with noise DBSCAN clustering allowing users to identify underlying patterns.

S5 illustrates the raw output of a PCA and k-means clustering analysis. Clustering algorithms can also be utilized to predict cluster membership of unseen data. Time series analysis Instant Clue offers the possibility to explore time series data. The activities to analyze time series data aim to smooth data such as an intensity along a time axis.It is time to put that investment into something people will notice.

Factors believe that collaborative work is beneficial because working Comput. So your data is safe. ChartBlocks ChartBlocks is another online chart builder that is well designed and allows you to build basic charts very quickly.

Your annual report as an interactive data visualization could be an ongoing report that updates in real-time as your data is updated which results in a more accurate story of the report. It is timely, relevant and accurate, ensuring a manager can review the report to understand assorted customer behaviors and trends. All code examples updated for the latest version of D3 4.