Privacy and Security focused Segment-alternative, in Golang and React

JavaScript SDK for RudderStack - the Customer Data Platform for Developers.

rtdl makes it easy to build and maintain a real-time data lake

Data analytics, exploration, sentiment analysis and topic analysis (LDA) on Amazon customer reviews. And cool interactive plots.

Containerized PostgreSQL client for executing SQL commands in containerized environments

📋 Need more information about your merchants or customers on your marketplace site? Add new data fields to their profile pages for them to fill-up.

Rust SDK for RudderStack - Open-source, warehouse-first Customer Data Pipeline and Segment-alternative. It collects and routes clickstream data and builds your customer data lake on your data warehouse.

Create an abstract component with a customer data to not duplicate the code to each component and send many requests to BE

A comprehensive e-commerce database solution using MySQL with complete table schemas for customers, products, orders, and transactions. Includes sample SQL queries, installation guide, and Node.js application setup for managing an online store.

Postman collection with example API calls for rtdl services

Repositories of analytics workshop

This is a simple website instrumented with analytics-next-cc and made to run in an httpd container

Attention mechanism based multiclass text classification on a dataset of customer complaints about consumer financial products.

Fork of Analytics Next (aka Analytics.js 2.0) that enables carbon copying (cc-ing) an additional endpoint

Example how to combine Google Analytics and hull.js to track website visitors

Project: Classifying consumers and minimising canvassing costs

Churn Modelling - unusual rate at which customers leaving the company, we need to figure out why? we need to understand the problem? We actually need to create a demographic segmentation model to tell the bank/company which customers are at high risk of leaving.

We maintain all customers in one database. There are heaps of customers who have user cards. So, I decided to split up the customers based on the country and load them into corresponding country tables.

Uppgift från IHM.

Customer analytics using SQL inside a Jupyter notebook.