Tools and Techniques
If you're still using Python 2, here's a well-organized collection of Python 3 features to inspire you and help ease the transition.
Data science and data engineering are closely related fields and it's useful to develop skills in both. In this series of posts, Robert Chang from Airbnb considers data engineering from the perspective of a data scientist. This first part offers a useful introduction to data engineering and the next two parts will cover Airflow, frameworks, abstractions and patterns. This promises to be a fantastic series.
Ensembles have rapidly become one of the hottest and most popular methods in applied machine learning. Virtually every winning Kaggle solution features them, and many data science pipelines have ensembles in them too. This is a great tutorial that explores ensembles and ways to implement them.
In this tutorial, you'll create a neural network that converts an image of a design mockup into the code for a simple website. This is a great tutorial and there's a Github repo of code to go along with it.
This graduate level series from the University of Notre Dame provides an overview of Bayesian computational statistics methods. The series is well organized and includes videos, notes, and homework.
With thousands of classes on data science, machine learning, R, Python, and more, Skillshare gives you the skills you need for your best year yet. And for January only, Data Elixir readers can get their first 3 months for just 99 cents (a $45 value)! Click here to start today.