The summer project at the IAA is a “toss you into the deep end of the pool” experience to teach you how to swim. Swim in this case as a data scientist or at least the in training version of one.
The project structure gets many things right and the first is
Shock you out of your comfort zone
Unlike most other projects in an academic setting which come at the end of a semester worth of lecturing on a single subject area the summer project is a learn by doing affair. It shakes up the paradigm and forces the participant to learn while attacking the seemingly daunting task and many of us need that initial shaking to let us know this isn’t your typical masters. But, how you might ask, can you successfully complete a project with little background experience, crucial subject knowledge, or full ability?
Team work from the word go
Luckily you aren’t alone. You have four other people with different backgrounds, experience, and talents to drawn from and rely on. Again unlike most projects for academia that I have been involved in you work as a team rather than on your own. The volume, pace and scope of the project aren’t achievable by a single individual, but the teams handled the load and made it to presentation time in good order.
High level of initial motivation, talent and work ethic
I may have been lucky in my group but from talking with other students it doesn’t seem so. These people are good. They have talent and drive as well as a wealth of damn useful experiences they bring to the table. Our team, and other teams I spoke to, learned a great deal from each other. Whether it was using R in unexpected ways, coding novel SAS macros, slide design/visualization or organizational skills the whole team experience is more than the sum of its parts.
Diversity of experience
Every group had the same data, but looking at and sitting in on presentations during the final phase, the learnings were as diverse as the cohort. We saw demonstrated analysis with perspectives on aspects of the data that our group never saw. Employee workload across regions or policy initiation versus claims were two that stood out to me as data crops we could have harvested but just didn’t see.
Learn as you work
The institute doesn’t leave us to our own devices for a month and expect a complete project. There is a learn by doing and just in time teaching methodology at play. Formal lectures and training on all aspects of data analysis happens concurrent with the project. This hands on experience and lecture material is enough to push the student to learn more and investigate with the overarching goal of the completion of the summer project. A lecture on data cleaning in the morning can be directly applied to the project set in the evening while a visualization tenant can be applied to slide design the same day. Having a reason to use and learn the material other than its presence on a midterm in the nebulous future is big reason the summer project works for teaching the cohort the career of data analytics. I learned more about SAS in 3 weeks than I did in 10 years on the job because I needed to use it to get the task done.
In no way a magic bullet, but a big take home for future analysis on my part, is the idea that looking at a set of provided data, of for example income, one can achieve powerful results with the addition of time and/or location to this basic data. Moving forward, transforming or enhancing the data with new variables of this type will definitely become part of my analytics tool box. The summer project experience also showcased the talent and diversity in the cohort as a whole and increased my energy and enthusiasm for the program and team based methodology that is central to the IAA.
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