R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)
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Updated
Jun 2, 2024 - R
R package that makes basic data exploration radically simple (interactive data exploration, reproducible data science)
Unlock insights into the U.S. healthcare landscape from 2019 to 2020. Our PowerBI-driven analysis delves into hospital performance, patient outcomes, and payer-provider dynamics. Dive into detailed reports and visualizations for informed decision-making, empowering healthcare stakeholders, and shaping the industry's future.
Next generation of automated data exploratory analysis and visualization platform.
AI studio for you and your business. Create assistants, connect databases, APIs (like Stripe) or CSV/Excel files. Use AI to create insights, workflows, action items.
PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
This report explores potential insights that can be derived from the Employees Data Set.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Discover a curated collection of dynamic Power BI dashboards covering financial analytics, HR metrics, streaming service trends, real estate dynamics, and more. Meticulously designed for comprehensive data exploration, this repository continues to expand with new and impactful visualizations.
IU Projects
First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.
Pheno-Ranker is a tool designed for performing semantic similarity analysis on phenotypic data structured in JSON format, such as Beacon v2 Models or Phenopackets v2.
Build a data catalog by running a single line of code
Biologically Plausible Programming
An attempt to figure out the order of the movies ranked 1001-2000 on TSPDT based on available partial rankings.
🚚 Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Study and notes of Data Science lifecycle.
Customer Segmentation using R
This project focuses on analyzing fitness data collected from various tracking devices to gain insights into users' activity levels, sleep patterns, calorie expenditure, and heart rate. The dataset used in this project consists of multiple CSV files, each containing different aspects of fitness-related data.
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