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Similarly, you can reference tables generated from knitr::kable(), e.g., see Table 0.1.

TABLEAU READER 8.2 DOWNLOAD CODE

Reference a figure by its code chunk label with the fig: prefix, e.g., see Figure 0.1. 7.3.4 Useful Links on Data Visualization Resources, Trends, and Tutorials.7.3 Additional Resources for Aspiring Data Visualizers.7.2.1 Laying the groundwork for data visualization.6.9.3 Success factors for Marketing Analytics.

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6.9.2 Risks and Pitfalls: Identify reason for failure and how to avoid them.

6.9.1 Important Prerequisites for Marketing Analytics.

6.9 Case Study on data viz project for Marketing Analytics.

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6.8.5 Tips to upgrade data visualization design.6.8.4 Possible Scenarios which may create a dilemma for the user.6.8.3 Limitations of Data Visualization Tools.6.8.2 Pitfalls in the different stages of visualizations.6.8.1 Critical points of failure in data visualization projects.6.8 Risks and pitfalls in a Data Visualization Projects.6.7.11 Tips for Data Visualization Projects.

6.7.6 Useful platforms and tips for data visualization project.

6.7 How to decide what type of visualization to use:.

6.6.1 Hook The Audience on Your Key Message.

6.6 How to Structure a Data Viz Project.

6.5 Finding Insights from Data Visualization.

6.4 Planning a Data Visualization Project.

6.3 Overview of process for a data visualization project.

6.2 Important Prerequisites of data visualization project.

6.1.2 Step 2: Understanding Your Data Set.

6.1.1 Step 1: Understanding the Business Issues.

6 How to run a data visualization project.

5.4 The Data Visualization Hippocratic Oath.

5.3 General Guidelines for Ethical Visuals.

5.2 Ethical dimensions of Visualization.

5.1 Importance of Ethics in Visualization.

4.8.1 How to Customize a Legend in Python with Matplotlib.

4.8 Using Visualization Softwares and Libraries.

4.7.3 Takeaway 3: Every Tool Forces You Down a Path.

4.7.2 Takeaway 2: We Still Live in an ‘Apps Are for the Easy Stuff, Code Is for the Good Stuff in the World’.

4.7.1 Takeaway 1: There is No Perfect Tool, Just Good Tools for People with Certain Goals and Mindsets.

4.7 Takeaways From Recreating One Chart Using 24 Tools.

4.6 Using Design Patterns to Find Greater Meaning in Your Data.

4.5 Choosing the Right Baseline in Data Visualization.

4.2.2 How to decide which chart type to use?.

4.2 Data Explanation (Like, Storytelling).

4.1 Data Exploration (Like, Outlier Detection).

3.9.12 The Atlas of Sustainable Development Goals 2018 - Data Visualization of World Development.

3.9.11 Visualization of big data security: a case study on the KDD99 cup data set.

3.9.10 Washington Wizards’ Shooting Stars.3.9.7 Kissmetrics blog: visualization of metrics.3.9.6 Adding up the White Oscars Winners.3.9.5 An Interactive Visualization of NYC Street Trees.3.8.2 A Guide to Who is Fighting Whom in Syria.3.8.1 Connecting the Dots Behind the Election.3.7.3 State of the Union 2014 Minute by Minute on Twitter.3.6.3 Understanding Plastic pollution using visualization.3.6 Dust in the Wind: Visualization and Environmental Problems.3.5.2 Hans Rosling’s 200 Countries, 200 Years, 4 Minutes.3.4 Visualizing Urban Data for Social Change.3.3.4 How People Like You Spend Their Time.3.3.1 Young Voters, Class and Turnout: How Britain Voted in 2017.2.8 Contemporary Research Results & What’s Next.2.7.4 Handbooks to improve your visualization design.2.7.3 Developing a Data Visualization Style.2.7.2 Infographics vs. Data Visualizations.2.7.1 Typography and Data Visualization.2.7 Implications of Good Data Visualization.2.6.1 Data Mining and Data Visualization.2.5.3 Corporate Scorecards and Data Visualization.2.5.2 How visualization impacts Industry/business.2.4.4 R for Data Visualization: Grammar of Graphics.2.4.3 Python for Data Visualization：10 Useful Python Data Visualization Libraries.2.4.1 Brief Description of popular tools.2.2.6 Adapt your story to a different set of audiences.2.2.2 How to choose the best form of Visualization.2.1.1 Melissa Anderson’s Principles of Design.1.6.4 Useful Links on Data Visualization Resources, Trends, and Tutorials.1.6 Additional Resources for Aspiring Data Visualists.1.5.5 Data visualization can’t explain data, leaving room for interpretation.1.5.3 Automatic visualization vs. context-specific visualization.

1.5.2 Standing out categorization of information on the website.

1.5 How to tell stories using data visualization’ and ‘exploratory data visualization’ theory example.

1.3.1 Key Figures in the History of Data Visualization.

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