Interviewing as an art – Finding the smart, talented people with the right vibe for your company is always hard. Being on the other side is also hard. Not only in the sense of being good enough but also “interviewing” the work place – is the job interesting \ challenging? are the colleagues nice, talented, will you like to work with them? etc. There are many posts and theories about what you should ask in order to find the right candidate and less posts and discussions about how to approach the candidate, make him or her comparable and being able to get the maximum out of it during a stressing interview. I find this post specially sensitive and inviting.
http://www.zdfs.com/code/2015/on-interviewing-software-engineers
Airbnb airflow – a framework by AirBnb which help create, schedule and monitor data pipelines.
https://github.com/airbnb/airflow
Seaborn cheat-sheet – the statistical data visualization package completing \ improving the PyData eco-system. A simple tutorial that can also be used as a cheat-sheet.
https://beta.oreilly.com/learning/data-visualization-with-seaborn
Data Science Ipython notebooks – a collection of links of Ipython notebooks which relates to data science. Include notebooks from Kaggle, AWS, PyCon, etc. Can also add a the link to some seaborn notebook..
https://github.com/donnemartin/data-science-ipython-notebooks
Pitfalls of A\B testing – tl;dr – statistics is tricky and you should remember the underlining assumptions. Also a good chance to remind A/A testing as a sanity check.
http://blog.dato.com/how-to-evaluate-machine-learning-models-the-pitfalls-of-ab-testing
