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Data analysis
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Data storytelling
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Data collaboration
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Here’s what else to consider
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Data visualization is a key skill for any data scientist, as it helps you explore, analyze, and communicate your data insights effectively. However, creating static charts and graphs can limit your ability to interact with your data and discover new patterns and relationships. That's why interactive data visualization tools can be a great asset for your data science skills, as they allow you to manipulate, filter, and drill down into your data dynamically. In this article, you'll learn how interactive data visualization tools can improve your data science skills in four ways: by enhancing your data exploration, analysis, storytelling, and collaboration.
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- Akshay Toshniwal Senior Specialist at LTIMindtree | Thought Leader at Global AI Hub 3
- Andreas Aristidou, PhD Senior Data Scientist | PhD Economist with MS in Computer Science driving scalable research @ Netflix. 2
1 Data exploration
Interactive data visualization tools can help you explore your data more efficiently and intuitively, as you can adjust your views and parameters on the fly. For example, you can use sliders, buttons, or dropdown menus to change the time range, the aggregation level, or the variables of your charts. This way, you can quickly test different hypotheses, identify outliers, and find hidden trends in your data. You can also use tools like Plotly or Bokeh to create interactive maps, heatmaps, or scatter plots that let you zoom in and out, hover over points, or click on regions to get more information.
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- Akshay Toshniwal Senior Specialist at LTIMindtree | Thought Leader at Global AI Hub
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Raw data exploration also called Exploratory Data Analysis (EDA) is crucial to understand hidden data insights, data distribution, associated data patterns, and other aspects about the data.Python libraries like Plotly, Matplotlib, Seaborn, Bokeh, and others can be used for building interactive charts.Tools like core Tableau, Power BI, Google Data Studio, and other viz. applications can also be beneficial in analyzing the raw data.Interactive visualization for data exploration will help in understanding the data and generate some key insights.
UnhelpfulLike Like Celebrate Support Love Insightful Funny 3 - Andreas Aristidou, PhD Senior Data Scientist | PhD Economist with MS in Computer Science driving scalable research @ Netflix.
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Tableau is a great tool that is easy to use and provides a lot customization as well as built-in tools for easy data manipulation and visualisation utilizing mainly drag and drop functionality.
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2 Data analysis
Interactive data visualization tools can also help you analyze your data more deeply and accurately, as you can apply filters, calculations, or transformations to your data interactively. For example, you can use tools like Tableau or Power BI to create dashboards that let you slice and dice your data by different dimensions, such as location, category, or customer segment. You can also use tools like D3.js or Shiny to create custom visualizations that let you perform complex operations on your data, such as clustering, regression, or sentiment analysis.
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3 Data storytelling
Interactive data visualization tools can also help you tell better stories with your data, as you can engage and persuade your audience more effectively. For example, you can use tools like Flourish or Datawrapper to create interactive charts and graphs that let your audience explore your data at their own pace and level of detail. You can also use tools like R Markdown or Jupyter Notebook to create interactive reports or presentations that combine your data visualizations with code, text, and multimedia elements.
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- Andreas Aristidou, PhD Senior Data Scientist | PhD Economist with MS in Computer Science driving scalable research @ Netflix.
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Storytelling with data is a crucial skill of a successful data scientist. Crafting a good story requires not only the skills to analyze and visualise the data but crucially to understand your target audience. For example, how will they consume your material (in a live presentation or asynchronously via a dadhboard? what is theit technical background (e.g. will the addition of confidence intervals help or confuse them)?
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4 Data collaboration
Interactive data visualization tools can also help you collaborate better with your data, as you can share and communicate your data insights more easily and securely. For example, you can use tools like Google Data Studio or Qlik Sense to create and publish interactive data visualizations online that anyone can access and interact with. You can also use tools like Streamlit or Dash to create and deploy interactive web apps that let you showcase your data analysis and models. You can also use tools like GitHub or Colab to share and edit your code and data with other data scientists.
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5 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
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