NameArtem

NameArtem's Stars

• streamlit/streamlit

  Streamlit — A faster way to build and share data apps.

  Language:Python38.5k3255k3.4k

• BVLC/caffe

  Caffe: a fast open framework for deep learning.

  Language:C++34.3k2.1k4.8k18.6k

• iterative/dvc

  🦉 Data Versioning and ML Experiments

  Language:Python14.3k1334.8k1.2k

• yzhao062/pyod

  A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

  Language:Python9k1473461.4k

• open-metadata/OpenMetadata

  OpenMetadata is a unified metadata platform for data discovery, data observability, and data governance powered by a central metadata repository, in-depth column level lineage, and seamless team collaboration.

  Language:TypeScript6.4k477.6k1.2k

• awslabs/deequ

  Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.

  Language:Scala3.4k77339552

• Data-Learn/data-engineering

  Getting Started with Data Enngineering

  Language:Python1.2k15410574

• catboost/tutorials

  CatBoost tutorials repository

  Language:Jupyter Notebook1.1k360411

• maks-sh/scikit-uplift

  :exclamation: uplift modeling in scikit-learn style in python :snake:

  Language:Python7601974100

• mrpowers-io/quinn

  pyspark methods to enhance developer productivity 📣 👯 🎉

  Language:Python6672011599

• matyushkin/ds

  👨‍🔬 In Russian: Обновляемая структурированная подборка бесплатных ресурсов по тематикам Data Science: курсы, книги, открытые данные, блоги и готовые решения.

  Language:Jupyter Notebook20213020

• dvgodoy/handyspark

  HandySpark - bringing pandas-like capabilities to Spark dataframes

  Language:Jupyter Notebook193142026

• sharmaroshan/Statistics-for-Data-Science

  Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

  Language:Jupyter Notebook1473062

• NameArtem/deployml_course

  Репозиторий для открытого курса «Промышленная эксплуатация моделей машинного обучения»

  Language:Jupyter Notebook9617836

• maxcom/scala-course-2020

  Language:HTML2410010

• ianramzy/ticker-iq

  📈 Stock screener and portfolio analyzer, providing key insights on financial reports, news articles and more!

  Language:Java13205

• ankitvgupta/pycodestyle-action

  A Github Action that runs pycodestyle and leaves a comment on your pull request with style errors.

  Language:Shell8314

• dimaakapout/hse_CLTV

  ВШЭ Выпускной проект Современный анализ данных

  Language:Jupyter Notebook1300

• KoshkinSvk/DA-projects

  Language:Jupyter Notebook110

• maryblack/Tonal_Analysis

  Language:Python1100

• NameArtem/dash-recipes

  A collection of scripts and examples created while answering questions from the greater Dash community

  Language:Python1100

• olivaPodliva/stock_prediction

  Language:Jupyter Notebook1

• yyulka/LTV-HSE

  Language:Jupyter Notebook1