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30-days-of-python

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

8-week-sql-challenge

Case study solutions for #8WeekSQLChallenge at https://8weeksqlchallenge.com

airflow

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

alembic

A database migrations tool for SQLAlchemy.

articles

References to the Medium articles

artifactory-docker-examples

Examples for using Artifactory Docker distribution in various environments

awesome-ai-ml-dl

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

coin_flip_game

Used to illustrate why bagging is effective

colab_offical_doc

Chemical Process Control

comet_examples

complex-sql-exercise

SQL queries of all kind being put together as a single repository

consumptionsaving

Library for solving consumption-saving models

cookbook-2nd-code

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

coursera-machine-learning-engineering-for-prod-mlops-specialization

Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

crash-course-on-python-by-google

Quiz solution for this course

credit-card-application-fraud-detection-using-supervised-machine-learning-models

The provided dataset contained application (identity) fraud cases. It was a supervised problem as the data included a column showing the application’s fraud label (whether an application was fraudulent or not). It also contained several identifying data fields about the applicant such as SSN, address, phone number, etc. The dataset had 1,000,000 re

credit-card-transaction-fraud-detection-using-supervised-machine-learning-with-an-imbalanced-dataset

Credit card fraud is a burden for organizations across the globe. Specifically, $24.26 billion were lost due to credit card fraud worldwide in 2018, according to shiftprocessing.com. In this project, our goal was to build an effective and efficient model to predict fraud. We analyzed a real-world dataset that contained a list of government related

currencylayer-api

Reliable, accurate, real-time exchange rates for 168 world currencies, delivered in portable JSON format, compatible with any application. Full interactive documentation, code, integration guides and more available at https://currencylayer.com - Sign up for the Free Plan now!

data-modeling-using-postgres

Creating a Postgres Star schema and ETL that loads raw data from json files

data-science-expert

This repository includes my project, done during a Data Science Internship

data-warehousing_labs

Star Schema, Aggregate Models

dataiku-api-client-python

Python client for the DSS public API

dataiku-hive-udf

A collection of Hive UDFs

dataquest

This is a repository for storing and sharing data resulting from working on projects and materials in Dataquest

dataquest-guided-projects-solutions

My dataquest project solutions

dbt-codegen

Macros that generate dbt code

dbt_master

dealing_with_lists_in_pandas

This is how you perform data analysis on list values in pandas dataframes.

dis_poc

dmn-js

View and edit DMN diagrams in the browser.