Tom's starred repositories
the-incredible-pytorch
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
awesome-apache-airflow
Curated list of resources about Apache Airflow
machine-learning-book
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Spark-The-Definitive-Guide
Spark: The Definitive Guide's Code Repository
practical-statistics-for-data-scientists
Code repository for O'Reilly book
LearningSparkV2
This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]
pyspark-examples
Pyspark RDD, DataFrame and Dataset Examples in Python language
airflow-tutorial
Apache Airflow tutorial
Python-for-Probability-Statistics-and-Machine-Learning
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
data-pipelines-with-apache-airflow
Code for Data Pipelines with Apache Airflow
algorithmic_trading_book
2 books and related source codes for algorithmic trading.
Python-for-Finance-Second-Edition
Python for Finance – Second Edition, published by Packt
Python-for-Finance-Cookbook-2E
The repository of "Python for Finance Cookbook" 2nd edition
Algorithmic-Trading
Algorithmic trading using machine learning.
Practical-Data-Science-with-Python
Practical Data Science with Python, published by Packt
Python-for-Finance-Cookbook-2E
The repository of "Python for Finance Cookbook" 2nd edition
hypothesis-testing-with-python
True difference or noise? 📊
Data-Pipelines-with-Airflow
This project helps me to understand the core concepts of Apache Airflow. I have created custom operators to perform tasks such as staging the data, filling the data warehouse, and running checks on the data quality as the final step. Automate the ETL pipeline and creation of data warehouse using Apache Airflow. Skills include: Using Airflow to automate ETL pipelines using Airflow, Python, Amazon Redshift. Writing custom operators to perform tasks such as staging data, filling the data warehouse, and validation through data quality checks. Transforming data from various sources into a star schema optimized for the analytics team’s use cases. Technologies used: Apache Airflow, S3, Amazon Redshift, Python.
Advanced-Algorithmic-Trading
The book <Advanced Algorithmic Trading> and its source code
WQU-data-science-challenges
I successfully completed a 2-unit, 16-week and 6 mini-projects of the Data Science module at WorldQuant University. The mini-projects included scientific computing, data wrangling, machine learning and natural language processing with Python.
Learning-Apache-Spark-2
Code repository for Learning Apache Spark 2, published by Packt
financial-engineering-wqu
Repository for MScFE, WQU.
A-B-testing---experimentation-project
#A/B testing: A step-by-step guide in Python This is a walkthrough of how to design and analyse an A/B test using Python.
sample-parquet-files
A repo hosting sample parquet files.
WQU-Applied-data-science-lab
The WQU Applied Data Science Lab is a sequence of eight projects, where you solve real-world problems using data science tools. Each project consists of four lessons and one assignment.