The constant evolution of technology has meant data and information is being generated at a rate unlike ever before, and it's only on the rise. Furthermore, the demand for people skilled in analyzing, interpreting and using this data is already high and is set to grow exponentially over the coming years. These new roles cover all aspect from strategy, operations to governance. Hence, the current and future demand will require more data scientists, data engineers, data strategists, and Chief Data Officers. In this blog, we will be looking at different set of interview questions that can certainly help if you are planning to give a shift to your career towards data science.

A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this! Calculus is not even required.

The foundation of machine learning and deep learning systems wholly base upon mathematics principles and concepts. It is imperative to understand the fundamental foundations of mathematical principles. During the baseline and building of the model, many mathematical concepts like the curse of dimensionality, regularization, binary, multi-class, ordinal regression, and others must be artistic in mind. The basic unit of deep learning, commonly called a neuron, is wholly based on its mathematical concept, and such involves the sum of the multiplied values involving input and weight. Its activation functions like Sigmoid, ReLU, and others, have been built using mathematical theorems. Linear algebra plays a requisite role in machine learning due to vectors' availability and several rules to handle vectors.