Published
Edited
Jul 15, 2022
1 star
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data_original_withjd = FileAttachment("data_original_withJD.csv").csv({typed: true})
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Inputs.table(data_original_withjd)
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FileAttachment("data_cleaned.csv").csv()
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Inputs.table(dataset_prep)
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viewof summary_data = SummaryTable(dataset_prep, {label: "Data Scientist's Salary – Overview"})
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typestructureAttributtyp = FileAttachment("typestructure - attributtyp.csv").csv()
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Inputs.table(typestructureAttributtyp)
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wordcloud1 = FileAttachment("wordcloud (1).svg").image()
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variousexploration = FileAttachment("variousExploration.png").image()
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boxplot2 = FileAttachment("boxplot2.png").image()
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boxplot1 = FileAttachment("boxplot1.png").image()
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scatterchart = FileAttachment("ScatterChart.jpg").image()
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choroplethmap = FileAttachment("ChoroplethMap.jpg").image()
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nestedcircles2 = FileAttachment("NestedCircles2.jpg").image()
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columnboxplotchart = FileAttachment("ColumnBoxplotChart.jpg").image()
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nodelinkdiagramradarchartsunburstchart = FileAttachment("NodeLinkDiagramRadarChartSunBurstChart.jpg").image()
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ca = FileAttachment("CA-2.png").image()
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il = FileAttachment("IL@1.png").image()
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dataset_prep = FileAttachment("data_prep.csv").csv()
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import { toc } from "@nebrius/indented-toc" // This component generates the Table of Contents
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import {SummaryTable} from "@observablehq/summary-table"
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One platform to build and deploy the best data apps

Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
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