The technology market share is just increasing like an oil spill in the ocean and becoming more and more complex and overwhelming to cope with. To help you learn and understand the ever-changing landscape of technology, we are extending a list of 30 Top LinkedIn Voice in Technology. Allie is the Global Head of Machine Learning Business Development, Startups, and Venture Capital at Amazon Web Service (AWS). Her area of expertise includes AI, Machine Learning, Crypto, Web3 & NFTs. Emmanuel is an undergraduate student of Chemical Engineering, named as the Young Influencer of the year by TIBA.

There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?

If 2017 left you breathless, exhausted by unexpected headlines, then brace yourself. The coming year may bring even more turbulent change, according to the CEOs, academics, economists and other bold thinkers we consulted for our annual peek at the year ahead.