Tags: data visualization*

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  1. ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.

    Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.

    - Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
    - Histograms, boxplots, pairplots, correlation matrices.
    - t-tests, ANOVA, chi-square test.
    - Linear, logistic, and multivariate regression.
    - Time series analysis.
    - k-means, hierarchical clustering, DBSCAN.

    Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
  2. This article demonstrates how to use Pandas plotting capabilities for common data visualization tasks, suggesting that Pandas can be sufficient for routine EDA without relying on libraries like Matplotlib.
  3. GovGlance is a data visualization platform that provides insights into government transparency, accountability, and civic tech in the world.
  4. This article features a curated list of the top data science articles published in July, covering topics such as LLM apps, chatGPT, data visualization, multi-agent AI systems, and essential data science skills for 2024.
  5. Quadratic is a modern spreadsheet that combines the familiarity of a spreadsheet with the power of code, allowing you to work with data and code collaboratively in real-time. It supports popular programming languages like Python, SQL, and JavaScript, and offers features such as dynamic charts, APIs, multi-line formulas, and AI integration.

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