google-analytics-etl

ETL process to pull online ads performance results from Google Analytics and stream the data into database

googleads-python-lib

The Python client library for Google's Ads APIs

googleanalytics

Приложение для работы с Google Analytics. Написал его себе в помощь: выгружать данные, чтобы потом строить на них BI-системы

joinmarket-clientserver

CoinJoin implementation with incentive structure to convince people to take part

js--base-course

Базовый курс JS

m

Collection of cutom M functions for Power Query and Power BI

martinheinz

My GitHub Profile README. Don't just fork, star it, so others can find it too! 👀

math4business

Math4Business with Ledgys (about blockchains/tracability)

mortalkombatjs

osint-tools

:eyes: Some of my favorite OSINT tools.

osint_public

p1aton

p1aton.github.io

pkgenerator_checker

Generate Bitcoin Private Keys and check them against blockexplorer.com

powerbi

Power BI files and scripts

prisma-nextjs

project-guidelines

A set of best practices for JavaScript projects

pure-react

exercises and examples based on the book Pure React by Dave Ceddia

python-yandex-logs-api

Скачивание отчета из Яндекс Метрики Logs Api через Python

python3-in-one-pic

Learn python3 in one picture.

r-tutorials

Приложения к книге "Статистический анализ и визуализация данных с помощью R"

react-marathon

reactpro

rmytarget

Пакет для загрузки данных из API MyTarget в R.

ryandexwebmasterv3

Connector for Yandex Webmaster API V3

sherlock

🔎 Hunt down social media accounts by username across social networks

stackbit-theme-fjord

Fjord is a minimal blogging theme for Stackbit

super_json_normalize

A generic, unlimited level json normalizer which handles arrays at any location. Outputs a normalised set of data with foreign keys which can be imported into a relational table

thesis-bitcoin-clustering

The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.

top-bitcoin-addresses-py

Collects a list of the 50 highest valued Bitcoin addresses