data-assessment
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This is Udacity's Data Analyst Nanodegree's 5th project; which is Wrangling and Analyzing WeRateDogs Twitter account.
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💹📈Investigating the oils market prices in addition to the stock market prices between the start of 2001 to the end of 2023. 💰📉
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DAIS‑10‑Continuum — Repository Description DAIS‑10‑Continuum is the official home of the Data Attribute Importance Standard (DAIS‑10) and its underlying semantic engine, the Data Importance Fading System (DIFS‑10).
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Generates a match score of two person names from 0-100, where 100 is the highest, on how closely two individual full names match. The scoring is based on a series of tests, algorithms, AI, and an ever-growing body of Machine Learning-based generated knowledge
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Experimental Python tool for assessing raw data readiness for process mining and event-log generation
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This repository has the Udacity data wrangling exercise, which helps us practice the wrangling process.
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Wrangle and Analyze Data of twitter WeRateDogs account
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Data gathering, Assessing and cleaning on audible dataset
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DECI-Udacity Lvl3 Wrangling and Analyze Data Project
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A virtual Internship introduced by KPMG AU on Forage.
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Step by step Guide to data assessment, cleaning and analysis using a hypothetical phase II clinical trial dataset in Python
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This repository provides R scripts for reproducing virtual species generating, modeling species distribution and final figures related with published manuscript.
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This repo contains files that show the different steps in data wrangling - gathering, assessment and cleaning. The use case is to wrangle data for WeRateDogs twitter account.
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A hands-on learning repository focused on data assessing and cleaning using Python. Covers inspecting data quality, handling missing values, correcting inconsistencies, detecting outliers, and preparing clean, reliable datasets for analysis, visualization, and machine learning workflows.
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This report details the steps employed for wrangling the data used in the "WeRateDogs" project. The three steps in the wrangling phase of data analysis – gathering, assessing, and cleaning – were strictly followed.
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It was the project of the udacity data analysis nano degree, my job was to gather data from Twitter API programmatically with python then assess it and discover its quality and tidiness issues then clean these issues with pandas and finally, I did some analysis and visualization to this data to make insights of it
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