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.

First open-source data discovery and observability platform. We make a life for data practitioners easy so you can focus on your business.

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.

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.

Build a data catalog by running a single line of code

Biologically Plausible Programming

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.

PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis

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.

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.

The data extraction and processing involved thorough exploration, preprocessing, and visualization of the "Video Game Sales with Ratings" dataset.

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

Documentation: Methodology and Exploratory Data Analysis

Portfolio of my Business Intelligence and Finance Analysis Projects.

Repository that contains material for training sessions on creating dashboards using Python.