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10-steps-to-become-a-data-scientist

📢 Ready to learn! you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.

311-services-nycdata-eda

Analysis of 311 services data for a freelancing project

airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

amexpert-2019

Analytics Vidhya AmExpert ML Competition

amexpert-2020

Dimension

amundsen

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

av

Contains solutions to AV competitions

av-wns-analytics-wizard-2019

WNS Analytics Wizard 2019 - War of Wizards organised by Analytics Vidya

av_clubmahindra

Club Mahindra DataOlympics 03-05-2019 - 05-05-2019

av_jabathon

Analytics Vidhya Hackathon for Data Engineers

av_nlp_competition

av_wns

Repo for 5th place solution for WNS Hackathon on AnalyticsVidhya

bitgrit_softbank_competition-

SoftBank Forex Algorithm Challenge

bitgrit_softbank_ml_competition

SoftBank Forex Algorithm Challenge: Develop foreign exchange risk management and trading algorithms

calories-alert-kafka

Simple alert system implemented in Kafka and Python

check_ms_win_tasks

:four_leaf_clover: Monitoring Windows Scheduled Tasks :four_leaf_clover:

codeprinter

Print out code easily

competitions

Notebooks of different competitions

cookbook

The Data Engineering Cookbook

data-science--cheat-sheet

Cheat Sheets

data-scientist-roadmap

Toturial coming with "data science roadmap" graphe.

electiondata

This location contains election data that I used in doing some datawrangling

example-airflow-dags

Example DAGs using hooks and operators from Airflow Plugins

fbcrawl

A Facebook crawler

feature_engineering_session_dhs18

Contains code and presentation for my interactive hack session, 'Effective Feature Engineering: A Structured Approach to Building Better Machine Learning Models' where we look at two interesting case studies on how to effectively leverage feature engineering and use a structured approach to build good machine learning models. This is created for the purpose of being presented in Analytics Vidhya's DataHack Summit 2018

flask-app

Flask Web app to integrate Service now and Nagios. It will display the matching hosts& incidents of the searched string

hdinsight-spark-scala-kafka

A basic example of how to read and write streaming data using Apache Spark and Kafka on HDInsight

house_price_predictions-eda

Kaggle House Price Predictions Dataset EDA

iit-bhu-codefest2018

Code Fest Organised by IIT-BHU via Analytics Vidya

iris-dataset-eda-and-algorithms

Applying EDA on Iris Dataset and Implementing Machine learning Algorithms