This second part of the ‘Python for Data Scientists’ post talks about the specifics of Python for data scientists. Part 1 of Python for Data Scientists talks about Python generally and can be found here.

An analytics roadmap for growth

How do I make and use an ARIMA model?¶

The college football season once again came to an end back in January, which now leaves us college football fans with nothing to do but post football memes online and feign interest in other sports until August rolls around again. This year, I’ve decided to jump the gun and start planning ahead for the 2018 college football season. With the 2018 college football season schedule already announced, we can start planning our vacations to see our favorite teams, rivalry games, and stadiums.

This blog post is the first in a series discussing different theoretical and practical aspects of the Frank-Wolfe algorithm.

Most data scientists have to write code to analyze data or build products. While coding, data scientists act as software engineers. Adopting best practices from software engineering is key to ensuring the correctness, reproducibility, and maintainability of data science projects. This post describes some of our efforts in the area.

In principle A/B testing is really simple. To do it you need to define two separate user experiences, and then randomly allocate users between them:

Data science needs fast computation and transformation of data. Native NumPy objects in Python provides that advantage over regular programming objects. It works for as simple a task as reading numeric data set from a file on the disk. We demonstrate it in few easy lines of code.

A short tutorial that shows how you can train deep learning models in a distributed way with Tensorflow, MXNet, and Pytorch in Polyaxon.