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Forget Streamlit: Create an Interactive Data Science Dashboard in Excel in Minutes

KDnuggets

By Shamima Sultana on June 19, 2025 in Data Science Image by Editor | Midjourney While Python-based tools like Streamlit are popular for creating data dashboards, Excel remains one of the most accessible and powerful platforms for building interactive data visualizations. We will demonstrate using a simple e-commerce sales dataset.

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20 Examples of Interactive Power BI Dashboards

Analytics Vidhya

This article explores 20 diverse Power BI dashboard examples, showcasing how data can be transformed into actionable insights. From sales and marketing to HR and social media, these dashboards offer inspiration for your data visualization projects.

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8 Ways to Scale your Data Science Workloads

KDnuggets

Every data scientist has been there: downsampling a dataset because it won’t fit into memory or hacking together a way to let a business user interact with a machine learning model. You can develop interactively from a notebook directly within BigQuery, letting you focus on model development, while BigQuery handles the infrastructure.

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How Data Analytics Improves Lead Management and Sales Results

Smart Data Collective

Reading: How Data Analytics Improves Lead Management and Sales Results Share Notification Font Resizer Aa Font Resizer Aa Search About Help Privacy Follow US © 2008-23 SmartData Collective. You can now track every lead interaction, score potential buyers, and forecast conversions with a high level of precision. All Rights Reserved.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

This article reflects some of what Ive learned. They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. Tableau, Qlik and Power BI can handle interactive dashboards and visualizations. And guess what?

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AI Agents in Analytics Workflows: Too Early or Already Behind?

KDnuggets

In this article, we tried to find out the answer to this question and analyze the timeline to see whether it is too early to do this or too late. You can see the article titles too often, “ will LLMs replace data analysts? ”. Data Analytics Agents The agents went one step further than traditional LLM interaction. value_counts().head(15)

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A Complete Guide to Matplotlib: From Basics to Advanced Plots

KDnuggets

It doesnt take much to do so: ax.plot(years, sales, color=#2ecc71, linewidth=2.5) Annotate when appropriate: ax.annotate(Record Sales in 2020!, plot(years, sales, color=blue) axes[1].scatter(years, scatter(years, sales, color=red) plt.tight_layout() The last line prevents overlapping.