Metis Week 1: The Whirlwind

Metis is an immersive data science bootcamp, and is what’s currently keeping me busy and out of mischief. The first week has flown by and has given us a whirlwind tour of visualisations (using Matplotlib and Seaborn) and data analysis (using Pandas) as well as pair programming and our first project.

One of my favourite parts of the week has been starting each morning off with pair programming.  Even more so as we were introduced to the method through this brilliant video, making it analogous to spooning. I’ve learnt SO much from my fellow classmates (I’m hoping I’ll be able to repay the favour at some point!) Emy got me thinking about the complexity of my function and how I could reduce it.  We were dealing with a pretty simple case but he counselled wisely to design the function to be able to deal well with more complex cases. Taking into account complexity has been probably one of the biggest shifts in my thinking as it’s pushed me to think through different ways of doing things rather than just what works.  Michael recommended that we test edge cases to see whether there were any limitations to the function we’d written.  We consequently discovered that it didn’t work for the numbers at the start and end of the range, and that got us rethinking (when we otherwise would have assumed that we’d got the solution and sat back on our laurels). Davis was teaching me all the shortcuts. It’s a brilliant feeling that I have the opportunity to learn from talented peers 🙂

We also completed our first project, which was a pretty steep learning curve in figuring out how to split up tasks, and organise the workflow of the team.  Because of  the tight deadline, we were working in parallel to bring in necessary additional data, clean the data, analyse it and visualise it. So those doing the latter stages worked with dummy data to begin with.  This works to a certain extent but reduces the ability to be responsive to what you find in the analysis to figure out what’s interesting to focus on and explore.  There were also massive overheads in working together without Git as we spent most of Sunday afternoon aligning our code and debugging compatibility issues.  (The team generally wasn’t comfortable with it and we were using Jupyter Notebooks which are saved as HTML files and so version control for the Python code ended up being a nightmare. In hindsight, we should have used Notebooks as our playground, and then version controlled them as plain Python files.)

A quick write-up of our first project (about the ubiquitous challenge of finding housing in the crazy city of New York!) can be found here.

 

 

 

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