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Alert: Sadly, I don't have time to maintain this but we have now a new edition 2020 available at https://www.amazon.com/dp/B086ML4PGW/ref=cm_sw_r_sms_awdb_t1_Z6oHEbGMPYFXF. The "Clear and Sane" framework for rapid data modeling, from the book "Clear and Sane engineering" (by Cheikhna Diouf) , a lightweight alternative to UML-like diagrams for en…
This project includes data modeling, engineering and analysis of employee data. Demonstrates use of SQL, creating schemas, queries, joins, aggregate functions, primary keys, and foreign keys.
Investigated Lyft riders’ data set, by performing data wrangling, conducting exploratory data analysis, and building statistical machine-learned model, using python packages, to determine KPIs, that guide riders’ cancellation decision
This project involved Data Engineering and Data Analysis where I designed the tables to hold data from 6 CSV files, imported the CSVs into a SQL database using PostgreSQL and wrote SQL queries to answer the given questions. A bonus analysis included creating some charts to analyze employee salary data.
Modeling the data with Postgres and building an ETL pipeline using Python. I will define fact and dimension tables for a star schema for a particular analytic focus, and write an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.
ETL (Extract, Transform & Load) Pipline to extract user activity and song data from json files and ingestion into a Postgres Database. This Project is part of the [Udacity Data Engineering nanodegree](https://www.udacity.com/course/data-engineer-nanodegree--nd027).
Exploring, cleaning and wrangling SFM Technologies' water consumption dataset using python and pandas library, in order to obtain clean data. This is data is modeled and visualized in a Power BI dashboard.