Week 1

Day 1 started at a sprint.  I was expecting to spend some time getting our environments up to speed, so I had not set up my MacBook yet.  That was a mistake.  

We were programming right away with an assessment.  I did not have a solid dev environment and wasted time installing modules, looking up syntax, etc.  Come to class ready to rock.

The rest of the week went a lot smoother.  OO and SQL were good reviews.  Lectures although fast-pace have been well organized.  The daily exercises were challenging even for someone coming from a software background, so I am particularly impressed by the students have come in with no programming experience.

The rest of the week was all about building out the data science toolkit: iPython notebook, pandas, EDA, Unix, graphing.

The EDA exercise was fun.  The lectured covered a process for EDA which really helped to squeeze as much value out of a data set as possible.  My partner and I worked on a 311 calls data set which was almost entirely categorical data.  It was a good exercise figuring out how to extract numerical data and thinking of good business questions to answer.

An intense, but satisfying first week.


A few tips... Don't register at the last minute like me.  Make sure to get pre-course materials.  These are worth working through before starting the bootcamp.

Mixer

Tonight, I dropped in at a mixer for the Galvanize data science bootcamp where members of the 1st and 2nd cohort of the Seattle bootcamp had an opportunity to meet one another.  

Only a month ago, I had attended an info session.  On Monday, I will join the 2nd cohort for day 1 of a journey to become a data scientist.  

Talking with the other data science fellows tonight has reassured me that this career pivot is the right choice.  The 1st cohort just finished a week ago, so it's a little too early to know how the bootcamp translates into jobs.  

But they are confident and have no regrets.