I am going to update this as I progress through the fellowship starting at week 0. The fellowship officially begins on September 14th, 2020. I am in the Data Science program cohort 2020C.
Week 0: The week before the program we needed to ensure that our development space was ready for action on day 1. I had Anaconda on my home machine, but it needed to be updated. I created a virtual environment especially for Insight :). Insight fellows have the opportunity to sign up for AWS resources. I know that I will need this (ran into this issue during my dissertation when I lacked computational resources for parts of the project), so I signed up for credits fast. If you ever have an opportunity to use AWS do it! I have met some of the other fellows and I see that many of us have pets and like board games! We communicate frequently on a Slack like app which is incredibly convenient.
Week 1: We began the week with introductions, a coaching and development workshop and giving serious thought to what project we will move forward with. The expectation is that we will have decided on a scalable data science project by the end of the week. Each day promptly begins at 10am CT (for me is 9am MT), and ends roughly at 6pm. What I am enjoying about this fellowship is there is a coaching and development component in order to prepare us for the job market. We learned that a resume is a marketing tool designed to highlight relevant transferable skills. When I began my career, I obviously did not know this and had some pretty embarrassing resumes. We spoke about giving and receiving feedback in a professional environment. My question that I posed to the coaching and development team: what about receiving objectively poor feedback? They shared that we can be selective in what feedback we choose to accept. We do not need to act on every piece of feedback. So, if and when I receive poor feedback again: feedback based on something I cannot change such as my appearance, my voice, my hair rather than something professional-I will 100% reject it. When I have received terrible feedback in the past that I did not ask for , I was deeply hurt. One piece of advice that was posed to all fellows is that we should be specific when asking for feedback as a means to direct the feedback to more or less what you are looking for. For instance, if you want feedback specific to your presentation skills, you can say, “I would like feedback related to my presentation abilities in areas of x and y.” If you leave it open-ended, you may receive feedback related to things that are not important to you.
The first day of the program was exhausting. I was not expecting the entire day to be accounted for. Let me repeat: the entire day. The day is filled with a combination of presentations and project deliverables. This is an intense program, but the quality is quite impressive. I will add this caveat: not all of the sessions are winners, in my opinion. But as with life, you can’t win em’ all. On our first day we met the founder of Insight, Jake Klamka. This was particularly impressive to me because I have rarely met the founder of any company before. Jake shared his background and why he founded Insight. He commented on my Linkedin post when I was accepted as a fellow, and gave a hearty congratulations! Jake immediately struck me as passionate, caring and thoughtful. It is no surprise that every staff member I have interacted with reflects those same values.
The program is focused on doing as a way of learning. We will move and build at an industry pace. There were no classes, but we had several breakout activities/activities where we needed to quickly think on our toes.
My group scraped data from Google trends, and we had some issue with the setup, lol. Toward the end of our 30 minute allotted time, I presented and sort of winged it, but I recall of needing to do that in industry as well.
We learned about what constitutes a data product(the thing we are building in this fellowship). A data product’s primary objective is use data to facilitate an end goal. This article does a wonderful job of explaining the concept of a data product in detail. Examples of data products: Zillow Zestimate, Fitbit, coupons recommended based upon purchase history, and Instagram suggestions.
After the first two days of the program, our focus has relied mainly in project ideation. The project ideas that I thought were excellent are actually not as feasible as I hoped. I have the habit of first determining if the data set exists, then trying to identify a problem and subsequent solution. I have been advised to think in this order:
- User (who will be the user of this product?).
- What problem will this product solve (the solution needs to be actionable).
- There needs to be data available for this product!
So, I can think of an amazing data product only to discover I cannot locate a datatset. Or I can locate a datatset, but not be able to think of a actionable solution. I am hoping that I can remedy this issue soon. Luckily, I am pitching some of my project ideas to other fellows and they are incredibly helpful in offering feedback. Looking forward to week 2 of the program.