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?
Education has always been a hot topic among intellectuals and reformers. It has seen quite a change in the last decade or so, but not significant enough to get noticed. The new era of learning is still focused on keeping students in the classroom in the hopes that they will bring a better future to themselves and to society as a whole. The current education system has always been focused on a batch study where individual growth is never focused on. With the expansion of the internet, things have changed drastically, as now, anyone can do self-study using YouTube, Udacity, or TED.
That's because, to paraphrase Amazon's Jeff Bezos, artificial intelligence (AI) is "not just in the first inning of a long baseball game, but at the stage where the very first batter comes up." Look around, and you will find AI everywhere--in self driving cars, Siri on your phone, online customer support, movie recommendations on Netflix, fraud detection for your credit cards, etc. To be sure, there's more to come. Featuring 30 lectures, MIT's course "introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence." It includes interactive demonstrations designed to "help students gain intuition about how artificial intelligence methods work under a variety of circumstances."
In this video from Intel AI DevCon 2018, Andrew Ng from Deeplearning.ai and Landing.ai When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."
How can scientists deal with the huge volume of new research publish on a daily basis? How can computers go further than merely parsing scientific papers, and actually suggest hypotheses themselves? When will we see a computer as another member of the lab team, serving hundreds of scientists simultaneously from its huge data set of extant research? This is the work of John Bachman, a systems biology PhD from Harvard Medical School, and Ben Giori, a postdoctoral fellow at Harvard Medical School's systems pharmacology lab. They're part of Darpa's Big Mechanism project, which is developing technology to read research abstracts and papers to extract pieces of causal mechanisms, then to assemble these pieces into more complete causal models, and to produce explanations.