Inevitably, everyone that has any interest in Data Science faces a wall, and that wall’s name is Mathematics. As much as we would love to avoid the complexities of the math in data science, when a stakeholder asks, “Can you explain the answer to me?” the answer is probably a process that comes from a whole pipeline of ideas that took data and performed mathematical calculations to arrive at the answer. As important as it is to know the correct algorithm to do the work, it is equally important to conceptually understand the math behind the algorithm. We’ll look at some of the key mathematical concepts so that we can slowly recreate the engine and learn the mechanics of data science.
In recent years, Python has exploded to become one of the fastest-growing languages. Traditional object-oriented programming languages have many rigid rules, and Python often breaks the convention of these languages, offering simplicity to counter this rigidity. For novice programmers, it may be more beneficial to learn code through the lens of simplicity and elegance versus robustness and completeness. Both have their tradeoffs, and here are a few key examples of how Python separates itself from the pack.