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What are the essential data visualization tools for a machine learning professional in FinTech?

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Data visualization is a crucial skill for any machine learning professional, especially in FinTech, where complex data needs to be communicated clearly and effectively to various stakeholders. Data visualization tools help you explore, analyze, and present your data in engaging and interactive ways. In this article, we will cover some of the essential data visualization tools for a machine learning professional in FinTech, and how they can help you achieve your goals.

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Hassnain C.

Dynamic Finance Professional | Finance Leader Pivoting to Commercial Business Finance | CFA Level 2 Candidate | ACCA…
Sathanandh C

Advanced Quant Finance | Data Science | Summer Intern - Deem Finance | IMTG'25 | CEG'18

1 Python libraries

Python is one of the most popular programming languages for machine learning, and it has a rich ecosystem of libraries for data visualization. Matplotlib is a low-level library that provides basic plotting functions and customization options, and it is the foundation of many other libraries. Seaborn builds on Matplotlib and offers more advanced and aesthetic plots, including heatmaps, boxplots, and violin plots. It also supports statistical analysis and data exploration. Plotly creates interactive and web-based plots such as scatter plots, line charts, and pie charts. Furthermore, it supports dashboards, animations, and 3D graphics, and can be used with other frameworks like Dash and Streamlit.

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Hassnain C.

Dynamic Finance Professional | Finance Leader Pivoting to Commercial Business Finance | CFA Level 2 Candidate | ACCA Member
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Essential data visualization tools for machine learning professionals in FinTech include Matplotlib, Seaborn, Plotly, and Tableau

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Abhinil Kumar

Building SuperAGI ?? | Kniru | StaTwig | JM Financial Ltd. | Nogozo | IIT Kharagpur
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Some essential data visualization tools for a machine learning professional in FinTech include: Matplotlib: Widely used for creating static, animated, and interactive visualizations in Python. Seaborn: Built on top of Matplotlib, it provides a high-level interface for drawing attractive statistical graphics. Plotly: Offers interactive plots and dashboards, suitable for presenting financial data and machine learning results. Tableau: Powerful for creating dynamic dashboards and exploring large datasets with its drag-and-drop interface. Power BI: Microsoft's tool for creating interactive reports and dashboards, integrating well with other Microsoft products like Excel and Azure.

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2 R packages

R is a popular programming language for machine learning, and it has a wide selection of packages for data visualization. One of the most used is ggplot2, which implements the grammar of graphics. This system creates plots based on layers and aesthetics, and it can generate histograms, bar charts, and scatter plots. Shiny is another package that lets you create interactive web applications with R code. You can use it to develop dashboards, widgets, and charts that react to user inputs and events. Leaflet is a package that enables you to create interactive maps with R code. It allows you to display spatial data, add markers, layers, popups, and legends.

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Sathanandh C

Advanced Quant Finance | Data Science | Summer Intern - Deem Finance | IMTG'25 | CEG'18
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In addition to ggplot2, Shiny, and Leaflet, other essential R packages for data visualization in machine learning include plotly, dygraphs, heatmaply, and gganimate. Plotly facilitates interactive and dynamic plots for exploring complex datasets, while dygraphs specializes in interactive time series visualization. Heatmaply is useful for creating interactive heatmaps to visualize hierarchical clustering and correlation matrices. Lastly, gganimate enables the creation of animated plots to illustrate changes or trends over time, particularly valuable for analyzing market dynamics in finance.

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3 BI tools

BI tools are software applications that aid in analyzing and visualizing data for business intelligence purposes. They usually have a graphical user interface that allows you to drag and drop data sources, filters, and charts. Tableau, Power BI, and Qlik are some of the most widely used tools - Tableau for creating interactive and visually appealing dashboards and reports, Power BI for connecting, transforming, and visualizing data from various sources, and Qlik for exploring and analyzing data using an associative engine. However, there are many more to choose from. The best tool for you depends on your data, your objectives, and your preferences. It is essential to use the tool that communicates your data effectively and efficiently.

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4 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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What are the essential data visualization tools for a machine learning professional in FinTech?

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1 Python libraries

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4 Here’s what else to consider

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What are the essential data visualization tools for a machine learning professional in FinTech?

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