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PyCon Montreal 2015 tutorials – Hands-on way to learn Data Science in Python

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PyCon(s) carry a benevolent motive of helping the Python community worldwide by providing extensive knowledge resources. I started following PyCon conferences from 2013. My first learning experience from PyCon tutorials & workshops inspired me to follow it back in the year 2014 and this craze continued in 2015 as well.

You can check out the training recommendation for tutorials of Pycon 2014 here.

PyCon Montreal 2015


Watching these PyCon videos has been an immense learning for me and I am glad that I could spare time to go through them. The event is divided in 2 parts: Tutorials (Workshops) or talks. Workshops aim to provide 3 hour hands on sessions where the instructor also acts as a facilitator. In this article, I have compiled a list of workshop videos you should watch from PyCon 2015.

To remove the confusion (because there are quite a few talks on data science here), I have made a recommended roadmap. This roadmap is a sequence of tutorials & workshop held at PyCon 2015 watched in a structure.

Let’s get started!

List of Workshops at PyCon 2015

Below is the roadmap of the workshops held at PyCon 2015. We recommend beginners to watch these videos in the listed sequence to help understand these concepts better, intermediates & experts can watch these videos as per their ease.

1. Sarah Guido – Hands on Data Analysis with Python

data analysis, Python

Good place to start learning data science in Python! Watch Sara explain these concepts in very simple manner. This video will be helpful for anyone wanting to perform data analysis in Python.


2. IPython & Jupyter in Depth: High Productivity Interactive and Parallel Python

Ipython, Jupyter, Python

I personally use IPython notebooks for the interactive data exploration and recommend it to every data science professional. In case you are wondering what are Python notebooks? this video is just the right place to start. It provides a super basic introduction of notebooks in Python, which then moves on to the explanation of super developed notebooks for Python such as iPython and Jupyter.


3.  Pandas From The Ground Up

Use of pandas in Python

Data Science in Python couldn’t have been effective without Pandas. This video will help you to understand Pandas by performing various exercises as practiced by the instructor. He emphasises more on learning while solving exercises. Apart from that, you will get to know about various functions in panda that you might have missed out till now.


4. Statistical inference with Computational Methods

Statistical inference in Python

This videos demonstrates the methods to make statistical inferences in Python by evaluating the sample, quantifying precision, hypothesis testing and performing similar steps. The instructors also reflects upon the massively scalable potential of python in statistical analysis.


5. Making Beautiful Graphs in Python and Sharing Them

Python, Visualization

This workshop beautifully explains the concept of data visualization supported by various other features (matplotlib) of python which are used to make your visualizations more apt and appealing. It also feature sets of challenges which will definitely excite your grey cells.


6. Bayesian statistics made simple

Bayesian statistics in Python

Remember the Monty Python’s Deal of No Deal? This video is must watch for anyone who is wanting to learn Bayesian statistics from scratch. This workshop begins with deriving Bayes theorem, then proceeds to the Bayesian statistics followed by solving some real world cases. It gives an awesome in-depth overview of Bayesian statistics.


7. Machine Learning with Scikit-Learn (I)

Python, Machine learning

This video explains the in depth concepts of Machine Learning. It begins with explaining machine learning with scikit-learn, then explains the concepts of supervised and unsupervised learning and eventually, concludes with model validation. This is useful hub of knowledge for machine learning enthusiasts.


8. Machine Learning with Scikit-Learn (II)

SciKit, Python, Machine Learning

This video starts from where the previous one ends. It further deep dives into the concepts of machine learning, thereby, explaining the concepts such as heterogeneous data modelling, text feature extraction, clustering, large scale text classification for sentimental analysis etc. followed by some practice exercises, which will definitely compel you to think hard.


9. Winning Machine Learning Competitions With Scikit-Learn

Kaggle competitions, machine learning

This video reveals how does winning data scientists think while solving Kaggle competitions? Here, the instructors has intended to create a contest similar to Kaggle competition among the attendees by forming their groups and helping them with the required tips, tricks, hacks used to solve such questions.


10. Hands-on with Pydata: How to build a minimal recommendation engine?

minimal recommendation engine

By watching this video, you will get acquainted with the concept of recommendation problem supported by its challenges and solutions. It also covers various practice problems on pandas, DataFrames, setup evaluation functions, test dummy solutions etc.


11. Practical Graph/Network Analysis Made Simple

Practical graph network analysis

Networks Redefined! This is one of the best video I have come across pertaining to Network Analysis. The instructor tries to explain the these concepts in the most simpler manner. This video illustrates every possible example which can help you to understand this concept without any difficulty.


12. Twitter Network Analysis with NetworkX 

twitter analysis with network X, python

Lately, I met a lot people who are wanting to learn more about Twitter Analysis. This is a must watch for the ardent lovers of social media analysts. In this video, twitter network analysis has been explained using the concepts of network theory/networkX and twitter APIs.


13. Learn Hadoop with Python

hadoop with python

This is a good resource available for learning Hadoop with Python. The instructor flawlessly explains the concepts such as MapReduce, Pig, Snakebite for HDFS, HBase, Spark & PySpark in a simple manner. This is a must watch if you are inclined towards big data.


14. Introduction to Spark with Python

introduction to spark

This video explains the distributed data processing framework – Spark. Spark is based on resilient distributed datasets. It is generally used for big data processing. The trainer has taken the coding, altogether to a different level. This video is helpful for group of enthusiasts who make use of python for handling big data.


End Notes

In this article, we covered the list of data science related workshops held at PyCon Montreal 2015. We also defined a roadmap to help beginners learn python one step at a time. I believe these are fantastic resources for hands on learning and developing data science skills.

If you face any difficulties while learning Python, feel free to ask us here. In the next article, we’ll bring up the list of most useful talks related to Data Science in PyCon 2015.

Did you find the article useful? Do let us know your thoughts about this article in the box below.

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  • Jeff Weakley says:

    Fantastic compilation. I might have run across these at some point as I am a fan of this conference but I genuinely appreciate you compiling this into a watchable list. Big Thanks! Jeff

  • Anon says:

    Thanks for filtering out the data-science-specific videos, Kunal. The YouTube channel is a trove of Python videos, and I’d been meaning to do it myself.

  • ol dusty says:

    Thank you for this nicely curated list. There are so many PyCon videos, every time I go to youtube to watch one, I always find something so interesting… that I still haven’t even made it all the way to the end the list!

  • Anurag Reddy G V says:

    Awesome post…I was looking for something like this. Thanks a Ton!

  • tin ho says:

    Very informative. Thanks.

  • Anurag Reddy G V says:

    The github links mentioned are not working though.

  • raj kumar says:

    Dear kunal
    I am in teaching professon and doing phd as external candidate.

    My research area is machine learning
    I teach the subjects data mining,data bases,pattern recognition
    I know python basics
    Naturally i got interest on data analysis

    What are the oppurtunities for a person like me?

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