It’s been a while since my last post on here, but I can assure you, I have been up to great things. Within the past eight months since this year began I have:
- Resigned from my position at Urbanation Inc;
- Travelled to and fallen in love with Portland, OR;
- Started and finished my fast-track course in Data Analytics, Big Data, and Predictive Analytics;
- Gotten involved with #SitTO. (Media links here, here, and here);
- Come out to my family and greater group of friends.
Needless to say, the last eight months have pushed me to grow in every sense of the word: technically, with Big Data and programming skills; professionally, from a clearer career path and newfound craving for learning more of the aforementioned technical skills; socially as I’ve learned through my summer program and #SitTO that although I am most comfortable and productive being alone, it takes a team to actualize and accomplish bigger goals; and personally as my coming out process has paralleled a good, hard, critical, no-bullshit look at my self.
I finished (and nailed) my last exam last Thursday and I’ve been taking it easy with my freedom. I’m in no rush to go job-hunting as I’d like to recollect, review, and hone everything that I’ve learned in the past three months.
We arrive at the crux and purpose of this post – to assign myself to goals. My over-arching career goal is to harness the power of big data and contribute to building a better, more livable and equitable city. In the meantime, my short term goals are to:
- Hone my skills in SQL, R, and HiveQL;
- Familiarize myself with CLI commands;
- Learn Python;
- Find a space/firm where I can combine my love for big data and urbanism.
To achieve this, my objectives are to:
a. Practice loading and analyzing comprehensive datasets from Kaggle and other sources. I’ll use Hive and Hadoop to manage and/or parse larger datasets (like the City of Toronto’s Parking Tickets), and R for in-depth analyses and visualizations;
b. Practice using PuTTy CLI commands while loading datasets into Hive and the HDFS;
c. Take a recommended Udacity course on Computer Science (specializing in Python);
d. Speak with those in the big data and/or urbanism field and learn from their industry insights;
e. Attend a CivicTechTO meetup.
I’m very excited for the next phase in my life and career. I think there’s huge untapped potential for big data in city-building, particularly in Canada. The field of urban planning is known to follow an archaic schema and is lethargic to move from legacy documents like the zoning by-law (est. 1986).
Big data can be quickly harnessed to identify key patterns in city-building that will provide guidance for decision-makers and as former New York City Mayor Bloomberg so affectionately puts it, “In God, I trust. Everyone else, bring data”. A challenge here would be finding actual big data for Toronto (as opposed to simply ‘open data’, like I have ranted here and here) but I strongly believe the prevalence of big data sets becoming available in Canada is inevitable. In the meantime, I’ll be preparing for the wave to hit.