Washburns

Andrew has experience with data warehouse modeling, front-end development, and engineering. He has a BS in Mechanical Engineering and a MS in Computer Science. He lives in Reno, Nevada with his wife and champion Pembroke Welsh Corgi.

Open source books and websites which enabled my data career.

Software

Data science workflow

A good data scientist is proficient at the entire transform, visualise, model workflow. R 4 Data Science teaches practical skills in R which can be rapidly implemented on a project.

The Python Data Science Handbook is similar resource for Python using Jupyter notebooks. Pretty much every data job requires extensive experience with Python, so this is worth practicing.

Real businesses store data in SQL warehouses. The extract in ETL is typically a SQL dialect. Use cheat sheets, or W3 School for help translating.

Math

VMLS

Vector, Matrices, and Least Squares introduces multidemensional data in the best kind of way. Highly recommended for those interested in modeling.

Kalman and Bayesian filters in Python is another open source book built in Jupyter. While mainly applicable for robotics and control, the way the material is presented is intuitive and rigorous.

The Elements of Statistical Learning is a graduate level textbook on model regression and classification. Currently working through this.