Introduction
The Daily Fantasy Sports (DFS) industry has exploded in popularity in recent years, largely due to the exponential growth of users playing on industry titans such as Fanduel and DraftKings. These platforms allow users to gamble real money by selecting a set of players known as a roster from a sport, with rules constraining the total salary and specific player position types required for a selected roster.Each player in the roster can accumulate points based on their performance in the sport in the upcoming game that day.The user then enters this roster into contests against other users who have also entered their rosters, with the aim of selecting the roster that accumulates the most points based on the competitions set rule for point accumulation. The user or subset of users with the top accumulated points at the end of the competition win the pot of money entered...
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Data Munging and Analyzing Barcelona OSM Data
The XML data for the city boundary of Barcelona was downloaded from OSM to clean and transform into a json encodable structure to allow loading into MongoDB, providing storage and artbitrary querying to enable further data analysis of the Barcelona OSM dataset. I chose the Barcelona dataset because I just recently spent a few weeks there and noticed some pretty cool urban planning features of the city (ie. Avinguda Diagonal) that could be cool to explore further.
Inspired by the "Diagonality" of Barcelona streets and to give the scope of the project a little more focus, I will be attempting to analyze the Barcelona OSM data and see if I can generate a measure for the "degree of diagionality" for Barcelona and possibly compare this measure with other cities. Not knowing ahead of time the difficulty of this, I may eventually have to...
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Update: I have since been accepted to the Master's in Artificial Intelligence program at UPC in Barcelona, and am currently completing the degree, which has changed my plans from that described below. I've kept the original post for my own historical continuity and for others thinking about taking the self taught route into AI. I've received a few inquiries why I ultimately chose to do a Master's degree, and my response is in the comments of this HackNews thread discussing this exact issue.
Artificial Intelligence seems like a drastically difficult discipline to master, but is a conventional degree a prerequisite for being an AI practitioner? This blog documents my attempt at a self-curated, self-taught and self-evaluated education in Artificial Intelligence.
About Me
I'm Cole MacLean, a classically trained Chemical Engineer from Calgary, Alberta, Canada.
I've been developing my Computer Science skills since 2009, where I made my...
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