Let's start off with a slightly curious question – have you considered building machine learning models using just pen and paper? It's actually a phenomenal method of learning how an algorithm works underneath. But here's the catch – we are living in the midst of a data boom. This means we can't calculate or design anything manually. The data we work with in machine learning projects is huge (and I mean HUGE).

"Efficiency is doing things right. Effectiveness is doing the right thing." As data scientists, we are often taught to be effective and do whatever it takes to get the job done. But ask yourself this – are we efficient at what we do on a day-to-day basis in a data science project? Is there any way to quicken the code we run or the monotonous processes we go through each day?

"Efficiency is doing things right. Effectiveness is doing the right thing." As data scientists, we are often taught to be effective and do whatever it takes to get the job done. But ask yourself this – are we efficient at what we do on a day-to-day basis in a data science project? Is there any way to quicken the code we run or the monotonous processes we go through each day?