I have built the computer vision models in 3 different ways addressing different personas, because not all companies will have a resolute data science team. quality-control manufacturing big-data-analytics jupyter-notebook cognitive services industry solutions

Data science encompasses a wide range of areas, topics, and sub-domains such as Big Data, Machine & Deep learning (ETL, TensorFlow, Keras), Data Mining/Visualization (EDA), BI, Predictive Analytics, Statistical Analytics, etc.

A Personal Work Notebook on Gitbook. 1）编程知识点总结；2）大数据场景下的用户数据解决方案实例

Homework Notebooks of Art of Analyzing Big Data - The Data Scientist’s Toolbox.

Performed Big Data Analysis on Bundesliga Football League Dataset using tools PySpark, spark-SQL, and numpy and done in Jupyter Notebook.

In this project, I analyzed weather data from the NCEI Global Surface Summary of Day dataset using PySpark in Jupyter Notebook. Tasks included data cleaning, statistical analysis, and forecasting for temperature, wind speed, precipitation, and extreme weather events. The project also predicts future weather patterns for Cincinnati and Florida.

In this jupyter notebook file, fictional data of football players was used to perform big data analytics in python. It involves using librarires such as pandas and matplotlib.

This repository is a comprehensive collection of notebooks that covers various data science projects in detail. Each project is designed to provide a clear understanding of the data science pipeline, from data acquisition to model deployment.

This is my final project for PSTAT 135, Big Data Analytics, using PySpark to conduct county-wide voter turnout regression analysis by demographic. This project was done in collaboration with Tyler Kim and Erasmo Rivas. The GCP storage bucket linked below contains the full project, while the Jupyter notebook and exported PDF are included here.