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acamica

Apuntes del curso Data Science

acamica-data-science

EN CURSO: Archivos de la carrera de data science presencial de Acámica, entre septiembre de 2019 y febrero de 2020.

acamica_datascience_201904_202006

acamica_ds_bog-3

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

amazon-sagemaker-workshop

Amazon SageMaker workshop lab guides and materials

an-introduction-to-interactive-programming-in-python----coursera

Study Material for the course

anaconda-issues

Anaconda issue tracking

analisis-espacial-con-r

analisis_de_datos_con_python

minicurso de análisis de datos utilizando Python, numpy, Pandas y bibliotecas relacionadas.

aplicaciones-python

awesome

:sunglasses: Curated list of awesome lists

awesome-pycharm

A curated list of resources for learning and using PyCharm, a Python IDE from JetBrains

aws-cloudformation-coverage-roadmap

The AWS CloudFormation Public Coverage Roadmap

aws-data-wrangler

Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

aws-serverless-data-lake-framework

Enterprise-grade, production-hardened, serverless data lake on AWS

aws-toolkit-jetbrains

AWS Toolkit for JetBrains - a plugin for interacting with AWS from JetBrains IDEs

az-103-microsoftazureadministrator

AZ-103: Microsoft Azure Administrator

cavariants

:exclamation: This is a read-only mirror of the CRAN R package repository. CAvariants — Correspondence Analysis Variants. Homepage: https://www.R-project.org

charlas_aeropython

Notebooks y otro material utilizado en las charlas de AeroPython ETSIAE (UPM)

cheatsheets

RStudio Cheat Sheets

churn-prediction-model-telecommunication

Python - Model Comparison | Logistic Regression, Decision Tree, Random Forest, KNN

churn_analysis

customer retention analysis - python - data exploration, random forest & logistic regression

churn_analysis_python_machine-learning

The goal of the project is to determine the important reasons of employees churn in a company. The dataset has been taken from Kaggle.com. This dataset has 34 features and a target label 'Attrition' with 1470 instances. This project is using various machine learning models- Naive Bayes, Decision Tree, Random Forest, Knn, SVC, LogisticRegression to predict whether the employee will leave the present company or not in future.

citadel-datathon-2021

Citadel Datathon

cloudword

clublectura_r4ds

codigos-ana-sofia

conda

OS-agnostic, system-level binary package manager and ecosystem