This repository includes the data wrangling about the Global Inequality dataviz project using vanilla js and the library arquero.js
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Updated
May 23, 2024 - JavaScript
This repository includes the data wrangling about the Global Inequality dataviz project using vanilla js and the library arquero.js
CSVs sliced, diced & analyzed.
OpenRefine is a free, open source power tool for working with messy data and improving it
Data Science Foundations II | Data Wrangling, Cleaning, and Tidying | How to Clean Data with Python
Data Science Foundations II | Portfolio Project. Data Visualization
Package that builds a JSON inventory/manifest from public primary or derived datasets
This repository contains a collection of data science projects which I did during the IBM Data Science Professional certification programme. Each project demonstrates different aspects of data science, data analysis, data visualization and machine learning.
Learning the data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. Working with data in Python using libraries like NumPy and pandas.
R for Social Scientists
Minimalist Data Wrangling with Python (Open-Access Textbook)
Data Science Foundations I | Exploratory Data Analysis in Python | Inspect, Clean, and Validate a Dataset | EDA: Inspect, Clean, and Validate a Dataset
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.
In this advanced course, Learning the three phases of data wrangling: gathering, assessing, and cleaning data.
Notebooks with some EDA and Machine Learning Models
Library to make MongoDB aggregation framework and pipelines easy to use in python.
Prepping tables for machine learning
Zui is a powerful desktop application for exploring and working with data. The official front-end to the Zed lake.
Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
Personal notes during reading Statistics with R by Jenine K. Harris, 1st ed. (2019)
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