Last week, due to awesomeness of the internet, I learned that PyCon 2015 conference is happening in April in Montreal. This got me super excited, even though I don’t quite get to use Python as much as I’d love to. The conference seems to be organized so well, in the beautiful city of Montreal, with amazing workshop options, hotel share options, AND on-site childcare!

So there I am, excited and trying to plan how I can swing it, looking up flights (bonus post for you on saving over 50% on flights) and emailing this girl about sharing a hotel room… Then bummer! Not only the conference was sold out, but also most of the workshops! (I was only hoping to attend tutorial days) But to re-phrase that old saying: if you can’t go to a conference, let a conference come to you!

I made a list of the workshops I would take if I could go, and started looking online for authors and their past presentations. Luckily, all of them had prep materials and some even had videos!

Here is the list focused on machine learning and data analysis for all of you, fellow curious Python lovers. Thank you so much to speakers for sharing these amazing study materials.

1. Machine learning with Python, basics

   Hands-on data analysis with Python by Sarah Guido
   Description
   Python is quickly becoming the go-to language for data analysis. However, it can be difficult to figure out which tools are good to use. In this workshop, we’ll work through in-depth examples of tools for data wrangling, machine learning, and data visualization. I’ll show you how to work through a data analysis workflow, and how to deal with different kinds of data.

2. Hadoop with Python (video) by Donald Miner
   Description
   In this tutorial, students will learn how to use Python with Apache Hadoop to store, process, and analyze incredibly large data sets. Hadoop has become the standard in distributed data processing, but has mostly required Java in the past. Today, there are a numerous open source projects that support Hadoop in Python and this tutorial will show students how to use them.

3. Learning Pandas by Brandon Rhodes
   Description
   The typical Pandas user learns one dataframe method at a time, slowly scraping features together through trial and error until they can solve the task in front of them. In this tutorial you will re-learn how to think about dataframes from the ground up, and discover how to select intelligently from their abilities to solve your data processing problems through direct and deliberately-chosen steps.

4. Bayesian statistics made simple (video) by Allen Downey
   Description
   An introduction to Bayesian statistics using Python. Bayesian statistics are usually presented mathematically, but many of the ideas are easier to understand computationally. People who know Python can get started quickly and use Bayesian analysis to solve real problems. This tutorial is based on material and case studies from Think Bayes (O’Reilly Media).

5. Building a recommendation engine with Python (video) by Diego Maniloff, Christian Fricke, Zach Howard
   Description
   In this tutorial we’ll set ourselves the goal of building a minimal recommendation engine, and in the process learn about Python’s excellent Pydata and related projects and tools: NumPy, pandas, and the IPython Notebook.

This post begs a follow-up on takeaways from each class. To be continued…