Data Science Immersive [Week 2 Review]

From Wikipedia, on the Shinkansen: “The maximum operating speed is 320 km/h (200 mph). Test runs have reached 443 km/h (275 mph) for conventional rail…”

I think back about the classes I took in university and took into consideration its pace versus General Assembly’s course. To put it simply, lessons in university ran at the MRT pace (at ~80km/h), where everyone has time to get onto the train and ride along at a leisurely pace, relatively speaking. GA’s course is nothing like that and I think of it to be like the Shinkansen – you had better step onto the train fast to ride along. Once on it, be prepared for one of the most exciting rides in your life!

The week’s lessons were focused mainly on learning about the Pandas dataframe, SQL and EDA. We also used the Seaborn package to help in data visualisation. Boom! Three key tools that data scientists use, all covered in the span of 5 days. With tons to read up on and many Jupyter notebook examples to go through, I have had little time to revisit the material covered last week.

As I stumble around my way in the world of data science, I am painfully aware that my coding still sucks. Going up a steep learning curve means having to trial and error many lines of code to see what works and what doesn’t. But I think the more important learning point is to figure out why writing code in a certain way works, and why it breaks in another. Python has lots of datatypes and methods, not all of them talk to one another so I need to keep checking that my datasets are of the right datatype before manipulating them.

The other thing I am painfully aware of – the fact that I will be having a non-existent social life outside of GA. Most of my waking hours have been spent on poring through lines of code and hitting shift+enter on my Jupyter notebooks, and I expect this trend to continue through the next 10 weeks left of the immersive course. DSI is tough for me, but I imagine it to be even harder for my course mates who are married and with families of their own. Secretly I admire their courage for having taken the leap of faith in coming for the course while losing family time, money and sleep. My prayer is that it will all pay off for us when we graduate towards the end of the year as GA produces Singapore’s first batch of data scientist to-bes!

Screen Shot 2017-09-10 at 6.08.38 AM
Hopefully, we all sail off happily into the sunset with jobs in hand after graduation!

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