My periodic accountability report for a self-study approach to learning data science. December 17, 2017

I've written about assessment recently, but other activities are helping me in my goal to become a data scientist besides studying and project work. I talk about some of those things here. Since my last check-in two weeks ago, I've done the following:

Attended two meetups. For R Ladies, with lightning talks, and deeplearning.ai's "Pie and AI" on how to break into Data Science/Machine Learning Engineering. Evening events aren't my favorite, but I attended these two nights in a row as I start networking in earnest. "Pie and AI" was particularly interesting because it was targeting people very much like me. I've taken deeplearning.ai's Deep Learning specialization and blogged about how much I appreciate workera's offerings for data/ai folks to test their skills. But they are also attempting to offer some sort of four-month, part-time, mentorship for machine learning engineers in the new year. They were short on details, and it sounds like it's still in the formative stage. I did put my name on the list for more information and am curious to see what they offer, but also I'm not holding my breath due to my goal of looking for full-time work starting in the new year. I will say that I'm very grateful for all the opportunities out there and do feel especially fortunate to live in the Bay Area (I mean except for the outrageous cost of living - so perhaps its a wash luck-wise).

Two webinars. ODSC's Evolutionary AI is the New Deep Learning by Babak Hodjat. This topic is one I've found interesting since grad school (genetic algorithms, anyone?), so I wanted to learn a little more about it. I also attended a webinar offered by Path Forward, which is a non-profit that offers returnships with participating companies. Returnships are a bit like mentorships or internships for people returning to the workforce after an extended break due to caregiving. It's mostly moms returning to work, but their interpretation of who can participate and what caregiving is is broad. The webinar covered general advice for restarting your career and included a panel discussion with two women who had benefited from the returnship.

Studying. I've reviewed some machine learning techniques like logistic regression, k-nearest neighbors, support vector machines, and the kernel trick. I've also done some more algorithmic coding (recursion) and watched some videos on techniques for technical interviews. I own Cracking the Coding Interview by Gayle Laakmann McDowell, and the code samples are all in Java, which I can muddle through but haven't touched in years. I prefer skipping around the book to read the strategic parts while using Hackerrank or LeetCode in python is good enough for me right now.

Project Work. I took a break from studying to do some personal project work. I'm not ready to blog about it here since I want to keep this journal entry short, and if I start talking about it, I won't stop. I will say I'm proud of my skills in scraping data, and happy that I've come far enough to know how to architect a recommendation engine from data gathering to deployment (at least at the high-level; fingers crossed on the execution). This is certainly not something I could conceive of even six months ago, let alone a year ago. I've been reading and digesting and asking questions for a while, trying to catch up with the state of tech, but I also credit the Full Stack Deep Learning boot camp with filling in the missing spots in my knowledge.

Between the interview prep and the Path Forward seminar, I have some more thoughts on what to prepare for when interviewing. I'll be spending some time thinking about what these points should be.

• I should be prepared to do a quick walkthrough of my resume and have a coherent and compelling path despite my lapse of time. I've already been tweaking my linked in profile to play up that trajectory.
• I should be doing mock interviews and have stories around two or three projects that make the case as a compelling hire.
• I should be prepared for a phone screen to make sure I 'm excited about a company as well as understand what the process is.
• Finally, I should remember to talk openly about failure. Part of me is struggling with fears of not belonging, so this one requires a certain amount of courage and equanimity. Thinking of interviewing as a reiterative process is helpful, and I'm trusting that repeated exposure to it will be a good thing.