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How Learning French Refreshed My Analytical Strategy

Parlez Vous Francais?

A few months after graduating with an advanced engineering degree, I find myself back in the classroom, this time for my first class of beginner French. All about me I hear snippets of broken French from my Canadian classmates: phrases, simple sentences and questions. I know three words, which I can pronounce in a distinctly American way: bonjour, merci, and croissant. The “beginner” level of French language for Canadians, it turns out, is a little different from the “beginner” level for an American. I reassure myself that I’m a fast learner and struggle through the first class.

After years of focus in one area of work, it’s natural to grow confident in your carefully crafted method of learning and doing. Once varied problems start to take on familiar forms, and it becomes easier to prescribe a certain solution. Stepping into French, I realized my tried and true approaches to learning were not going to prove effective. Several months later, here are some lessons I learned.

Failing: Fast and often. I find the most difficult part of language acquisition is not grammar or syntax, but the inevitability of mistakes. Regarding mistakes as taboo creates a major roadblock to personal improvement. The same holds true with solving a difficult analytics problem. Instead, sharing in-progress or flawed work with colleagues helps to break through the small failures and clear a path to a robust solution.

Out with the old and in with the new – Suppressing instinct and embracing a new technique. As with many language learners, my first instinct when I don’t know a word is to simply throw in the word from another language. Similarly, we tend to retain old sentence structures, until the structures of the new language become natural. R users can understand how this relates to learning the dplyr workflow or transitioning to functional programming. While these changes feel like a major paradigm shift at first, the impact on future work can prove invaluable.

Analytics MacGyver. Asking someone about their aunt’s profession can sound something more like “What does your mother’s sister do in life?” coming from a novice speaker. This roundabout method may sound silly, but is arguably better for the learning process than simply inserting words in English. Analytics professionals must also be bricoleurs, utilizing many resources, tools, and experts to make complex and unfamiliar problems tractable.

Diving in and staying in. Immersion and persistence are key to language acquisition. In analytics, methods are rapidly changing and improving. Attempting to become proficient in every new technology can be tempting, but dedicating time to one technology allows for quicker mastery.

Abstraction and derivation of meaning. In the early stages of learning, every interaction with a new language can feel like a game of abstraction, as we try to translate back to our mother tongue. As sentences become phrases, then complex sentence structures, the problem becomes a greater puzzle. Here is where I’d argue that many analytics professionals would find joy in the challenge of language acquisition - the feeling of successfully working through a verbal puzzle and constructing a response, hopefully more expressive than oui or non.

Lucia is an Operations Research Analyst at CANA Advisors. To find more content on learning and leveraging analytics, continue to visit our CANA Connection.

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