Exploratory Data Analysis Eda 1 Pdf Data Analysis Information Pydata london meetup #58tuesday, september 3, 2019sponsored & hosted by man group**** pydata.orgpydata is an educational program of numfocus, a 501(c)3 no. 2016 11 msc in data science dissertation project: automatic hierarchical data extraction from relational databases. 2016 11 20 podcast, invited guest to datalatam, episode “mesa redonda sobre la industria de datos” (in spanish).
Automated Exploratory Data Analysis On Databases Diego Arenas Given two connections, a source and target database, it will collect metadata for a exploration such as: number of rows and columns. number of distinct values and nulls per column. distribution of the categorical variables. statistics of the numerical variables. trends from time series data. We designed and implemented a python custom package to facilitate the ex ploration of databases for data science and data engineering projects. we designed and implemented a microservices architecture to support data streaming analytics. Diego will present some of the challenges of exploratory data analysis in data science and data engineering projects and how to approach them. he will show how to use the aeda python library (github ) developed by himself that helps with the exploration of new data sets from databases. His research is focused on the cutting edge of advanced analytics, in the fields of business intelligence, data warehousing, and machine learning. currently, diego is researching a solution for automated, exploratory data analysis predicting maintenance requirements of power generators.
Exploratory Data Analysis Diego will present some of the challenges of exploratory data analysis in data science and data engineering projects and how to approach them. he will show how to use the aeda python library (github ) developed by himself that helps with the exploration of new data sets from databases. His research is focused on the cutting edge of advanced analytics, in the fields of business intelligence, data warehousing, and machine learning. currently, diego is researching a solution for automated, exploratory data analysis predicting maintenance requirements of power generators. We present an automated solution for relational databases that traverses the data and extract features with minimum human intervention. this tool analyses the database, with a set of traditional measures taken from classical statistics and information theory. Automated exploratory data analysis. simplifying data exploration. automated exploration of files in a folder structure to extract metadata and potential usage of information. data science for good links. The problem of extracting hierarchical data from a curated relational database is addressed in this project. it is assumed low or non existent prior knowl edge about relationships in the database. This tool is designed to streamline the process of performing exploratory data analysis on various data sources, including csv, excel, and sql databases. it assists in loading, exploring, preprocessing, and visualizing your data with minimal manual intervention.
Comments are closed.